Contents

List of Tables and Figures……………………………………………………………….2

Abstract……………………………………………………………………………………..4

Introduction………………………………………………………………………………...6

Review of Literature…………………………………………………………………….10

Methodology………………………………………………………………………………24

Results……………………………………………………………………………………..33

Discussion…………………………………………………………………………………45

Conclusion……………………………………………………………………………..56

References………………………………………………………………………………57

Appendices…………………………………………………………………………….77

1

List of Tables and Figures.

Chapter 2 Review of Literature

Figure2.1. An example of an elite college player’s training plan with games played midweek. p.14

Chapter 3 Methodology

Figure 3.1. Optimising depth jump height. p. 25.

Fig. 3.2. Diagram of experiment design (adapted from Yetter and Moir, 2008 and Bomfim Lima et. al, 2011). p. 26.

Figure 3.3. Standardised warm up procedure conducted before each session. p.27.

Figure 3.4. Experimental countermovement jump protocol.p.28.

Figure 3.5. Diagram of the MyJump app interface during a CMJ test session. Highlighted are some of the tools necessary to calibrate performance and capture data. p. 29.

Figure 3.6.Experimental Depth Jump protocol. p.29.

Chapter 4 Results

Fig 4.7. Comparing 1RM of study to: college squad and WRU national average. p. 33.

Table 4.8. Significant CMJ results relating to depth jumps versus control. p. 33.

Figure 4.9. Countermovement jump performance (control) compared against own college and national average. p.34.

Figure 4.10. Highlighting mean countermovement jump performance @4 minutes post VCA. p.34.

Table 4.11.Significant CMJ results relating to HFS versus control. p. 35.

Figure 4.12. Highlighting mean countermovement jump performance @7 minutes post VCA. p. 35.

Table 4.13. Significant CMJ results relating to HBS versus control. p. 36.

Figure 4.14. Highlighting mean countermovement jump performance @10 minutes post VCA. p. 36.

Table 4.15. Significant CMJ results relating to HBS versus DJ. p. 37.

2

Fig 4.16. Worst, best and mean performance @seven minutes post HBS VCA. p. 37.

Fig 4.17. Worst, best and mean performance @seven minutes post HFS VCA. p.38.

Table 4.18. Significant CMJ results relating to HFS versus DJ. p.37.

Table 4.19. Significant 10m sprint results relating to control versus DJ. p.38.

Table 4.20. Significant 10m sprint results relating to control versus HFS. p.38.

Figure 4.21. Highlighting mean 10m sprint performance @four mins post VCA. p.39.

Figure 4.22. Highlighting mean 10m sprint performance @seven mins post VCA. p.39.

Figure 4.23. Highlighting mean 10m sprint performance @ten mins post VCA. p.40.

Table 4.24. Significant results relating to 10m sprint performance between control and HBS. p. 40.

Table 4.25. Significant results relating to 10m sprint performance between DJ and HBS. p.40.

Figure 4.26. Comparison of study control 10m sprint time versus college squad overall and national average. p.41.

Table 4.27. Significant results relating to 10m sprint performance between DJ and HFS. p.41.

Table 4.28. Significant results relating to 10m sprint performance between HBS and

HFS. p. 41.

Fig 4.29. Worst, best and mean sprint times @four minutes post HBS VCA. p.42.

Fig 4.30. Worst, best and mean sprint times @four minutes post DJ VCA. p. 42.

Fig 4.31. Worst, best and mean sprint times @ten minutes post DJ VCA. p. 43.

Fig 4.32. Worst, best and mean sprint times @ten minutes post HBS VCA. p. 43.

Figure 4.33. Comparing relative strength of study to: college squad and WRU national average. p. 44.

3

Abstract

The purpose of the present study was to investigate the potentiating effects of heavy

back squats (HBS), heavy front squats (HFS) and depth jumps (DJ) on

countermovement jump (CMJ) performance and subsequent 10m sprint performance

in elite youth rugby union players. The HBS and HFS voluntary conditioning activities

(VCAs) consisted of parallel back or front squats with 30%, 50% and 70% of the

participant’s 1RM. The DJ VCA consisted of two sets of five DJs. All VCAs were

completed following a standardised warm up procedure. Nine participants randomly

performed HBS, HFS, DJ VCAs and measured CMJ performance and 10m sprint

times at four, seven and ten minutes post VCA. Significant levels of potentiation

(PAP) were recorded for all VCAs compared to control in CMJ performance. The DJ

VCA proved most significant in comparison with other VCAs for CMJ performance

(DJ vs HBS p=0.024144 at four mins and p=0.045515 at ten mins post VCA; DJ vs

HFS p=0.026769 at four mins and p=0.0422 at seven minutes post VCA). Significant

potentiating results were elicited in 10m sprint performance following HBS, HFS and

DJ VCA versus control VCA, with HBS producing significant results in the nine

paired t-tests versus control (at four, seven and ten minutes post VCA for overall,

forwards and backs) and elicited the most PAP versus other VCAs. Interestingly

though, HBS and HFS mean sprint speed decreased at ten mins post VCA and DJ

VCA had the fastest mean result (DJ versus HFS p=0.049727, and DJ mean 10m

sprint time 1.76888 vs HBS mean 10m sprint time 1.79222 at ten minutes post VCA).

The most responsive potentiating effects for both CMJ performance and 10m sprint

speed were recorded from the three most relatively strong participants of the study.

The significant findings in both CMJ performance and 10m sprint speed prove that

DJs or other modified plyometric VCAs can elicit higher responses in some

4

individuals for both CMJ and 10m sprint and further that at ten minutes post VCA

and beyond they may be far more suitable in sustaining a potentiating response.

5

Introduction

Post-activation potentiation (PAP) is a phenomenon which involves enhancement of

muscle force and power (Seitz et. al, 2014). This usually involves the use of a

voluntary conditioning activity (VCA) prior to a performance, or as part of a warm up

(Ebben et. al, 2000; Hodgson et. al, 2008; Jensen and Ebben, 2003). Acute

improvements in human performance following PAP protocols have been reported in

activities such as sprinting and jumping (Crewther et. al, 2011; Hodgson et. al, 2005;

Tillin and Bishop, 2009; Kilduff et. al, 2008).

Potentiation can be attributed to light chain phosphorylation and increased

recruitment of motor units (Evetovich et. al, 2014), enabling the actin-myosin

interaction to become more sensitive to calcium ions (Ca2+). This leads to increased

levels of myosin cross bridge activity and increases in performance (Sale, 2004)

through structural change to the myosin head and higher force production of the

cross bridges (Gullich and Schmidtbleicher, 1996). Subjects with higher percentages

of type II muscle fibres may see more acute enhancement of their performance

(Terzis et. al, 2009). Another mechanism proposed suggests neural factors, such as

excitability of motor neurons being responsible for increased contractile performance

after previous muscular activity (Moir et. al, 2011; Gullich and Schmidtbleicher, 1996;

Seitz et. al, 2014).

There are many studies across a wide range of sports in the literature that

investigate PAP: basketball (Talpey et. al, 2014), Gaelic football (Byrne et. al, 2014),

football (Keiner et. al, 2013), weightlifting (Fukutani et. al, 2014), rugby (Bevan et. al,

2010; Comyns et. al, 2010; Esformes and Bampouras, 2013; Lombard et. al,

accessed ahead of publication), bodybuilding (Lowery et. al, 2012), track athletics

6

(Bomfim Lima, et. al, 2011), field (throwing event) athletics (Bellar et. al, 2012) and

volleyball (de Villereal et. al, 2007) to name a few. There are conflicting results in

many studies on PAP partially due to the numerous methodological differences in

approach to the phenomenon (Bevan et. al, 2010; Hodgson et. al, 2005).

There are many studies in the literature that involve the physical development of, or

the physiological responses to stimuli of elite youth rugby players (Bevan et. al,

2010; Comyns et. al, 2010; Esformes and Bampouras, 2013; Lombard et. al,

accessed ahead of publication). The participants of this study were already

accustomed to physical fitness and strength testing several times annually (WRU,

unpublished data). The experimental procedures followed combine two previously

published test frameworks creating an experimental protocols that will provide

indicative data (Yetter and Moir, 2008; Bomfim Lima et. al, 2011). This study

examines different responses to voluntary conditioning activities (VCAs) on a group

of elite youth rugby players: the heavy back squat (HBS), the heavy front squat

(HFS) (Yetter and Moir, 2008), and depth jumps (DJ) (Byrne et. al, 2014).

Subsequent performances in countermovement jump height (CMJ) will be measured

at different time points following the VCA using a specialist iPhone app called

MyJump (Balsalobre-Fernandez et al., 2014). The researcher aims to demonstrate

the suitability of such an inexpensive, user-friendly piece of equipment (Murray and

Olcese, 2011). After each CMJ, the participants complete a timed 10m sprint. The

researcher hypothesises that levels of post activation potentiation (PAP) elicited from

DJ VCA would match levels of PAP elicited following HBS and HFS VCAs mirroring

results of Tobin and Delahunt, 2014 whose plyometric stimulus elicited PAP

responses comparable to using heavy preloading stimuli (Crewther et. al, 2011; de

Villareal et. al, 2007; Kilduff et. al, 2008; Weber et. al, 2008; Young et. al, 1998).

7

Depth jumps (DJ) have been shown to elicit PAP previously and can be viewed as a

more appropriate biomechanical fit for improvement in sprint performance than

squats (Bomfim Lima et. al, 2011; Byrne et. al, 2014; Chen et. al, 2013; Crewther et.

al, 2011). If the levels of potentiation found in DJ significantly match or outperform

heavy preloading stimuli then there are huge, untapped practical applications

transferable to the preparation of rugby players in future prior to performance (Bevan

et. al, 2010). Strength and conditioning professionals could potentially utilise

individual PAP responses to stimuli in prescription of specific warm up activities

using little or no equipment that induces lasting performance-enhancing PAP

responses (Seitz et al., 2014; Turner et al., 2014; Wilson et al., 2013; Zemkova et al.,

2014).

The researcher will also compare relative strength to conclusions drawn from other

studies that ‘stronger’ subjects exhibited greater PAP responses when compared

with ‘weaker’ subjects (Seitz et. al, 2014) although the responses are reported as

being highly individual (Turner et. al, accessed ahead of publication). Some

conflicting results have been realised in PAP studies due to muscle fatigue from

preload stimuli that masks potentiating effects, although it is clear that potentiation

and fatigue can coexist (Batista et. al, 2007; Docherty et. al, 2004; Gossen and Sale,

2000; MacIntosh and Rassier, 2002; Rassier and MacIntosh, 2000). The volume,

type and intensity of the VCA are believed to have a significant impact on the

relationship between potentiation and fatigue (Tillin and Bishop, 2009; Tobin and

Delahunt, 2014). The time elapsed between VCA and action are also highly

individual and in some cases in studies on rugby players it has taken rest intervals of

eight minutes before significant improvements have been recorded (Bevan et. al,

2010; Kilduff et. al, 2008). This study measures potentiation at several time intervals

8

and aims to determine optimal rest intervals for all participants (four, seven and ten

minutes) (Seitz et. al, 2014).

HBS and HFS are widely used key compound exercises in the development of elite

youth rugby union players and are shown to improve functional performance (Mina

et. al, 2014; Crewther et. al, 2011). They are easy to implement and potentiation of

performance has been well documented in rugby players for sprint speed (Bevan et.

al, 2010; Kilduff et. al, 2007; Kilduff et. al, 2008) and jump performance (Esformes

and Bampouras, 2013). The technical difference in performing two different squat

techniques makes comparisons interesting, as some propose similar levels of

activation in front squats versus back but with lighter loads (Clark et. al, 2012; Gullett

et. al, 2009).

To date very few studies have examined the effect of a plyometric VCA on

potentiation of sprint performance. A plyometric VCA requires complex use of

movement skills which must be developed in an appropriate timeframe so athletes

display mastery before being allowed to progress (Turner and Jeffreys, 2010).

Methods of inducing PAP which require less equipment (just bodyweight) may be

better tolerated by players and coaches, are attractive alternatives especially when

functional performance is improved and might prove the determining factor in an

athlete’s performance (Hodgson et. al, 2005; Mina et. al, 2014; Rassier and

MacIntosh, 2000; Tillin and Bishop, 2009; Turner et. al, accessed ahead of

publication).

9

Review of Literature

Post-activation potentiation (PAP) elevates motor performance to a higher level by

preconditioning the muscle and responding to voluntary conditioning activities (VCA)

thus improving subsequent performance (Crewther et al., 2011; Tobin and

DeLahunt, 2014). Although the exact mechanisms behind this phenomenon are still

the subject of much debate, it is thought be due to increased phosphorylation of

myosin regulatory chains (Fukutani et al., 2014). This renders the myofilaments more

sensitive to myoplasmic calcium ions, increased alpha motor neuron excitability and

reduced synaptic inhibition. These mechanisms result in greater cross-bridge

attachments within the muscles which allowing subsequent augmentation of power in

the form of PAP (Carter and Greenwood, 2014; Mola et al., 2014). Individuals with

greater maximal strength display more elevated levels of myosin light chain

phosphorylation and possess larger and stronger type II muscle fibres, meaning elite

athletes that possess higher type two fast-twitch muscle fibres have increased

subsequent performance (Bullock and Comfort, 2011; Chiu et al., 2003; Hodgson et

al., 2008; Parry et al., 2008; Smith and Fry, 2007; Seitz et al., 2014; Tillin and

Bishop, 2009). In response to high-intensity exercise seen in VCAs, type II muscle

fibres exhibit greater neural excitation (Seitz et al., 2014). Exercises designed to

elicit PAP during training or before competition have been shown to influence

neuromuscular characteristics, including peak force or strength, joint range of

movement (ROM), velocity and muscle activity during the exercise (Mina et al.,

2014).

There is much conjecture in the literature as to appropriate VCAs, periods of rest,

volume load and levels of potentiation reported. This study draws comparison with

other PAP studies that incorporate use of HBS, HFS, CMJ, 10m (and other distance)

10

sprints and depth jumps and specifically involves semi-professional, professional and

elite youth rugby players (Bevan et. al, 2010; Comyns et. al, 2010; Esformes and

Bampouras, 2013; Lombard et. al, accessed ahead of publication). Much of the

previous research into PAP has involved dynamic exercise of the lower body (HBS)

(Till and Cooke, 2009) with relatively few studies relating to plyometric VCAs and the

majority of these studies attempting to induce PAP using CMJ (Hilfiker et al., 2007;

Masamoto et al., 2003; Till and Cooke, 2009). Although there are a great number of

studies that support and report potentiation (Chatzopolous et al., 2007; Gourgoulis et

al., 2003; Kilduff et al., 2008; McBride et al., 2005; Rixon et al., 2007; Yetter and

Moir, 2008; Young et al., 1998), there are large numbers of studies that also report

no potentiation following VCAs (Jensen and Ebben, 2003; Jones and Lees, 2003;

Mangus et al., 2006). The apparent lack of potentiation is often attributed to

insufficient muscle activation (Batista et al., 2011).

Previous research has also established an intricate interplay between potentiation of

muscles and fatigue responses that are extremely individual in nature (Talpey et al.,

2014; Tillin and Bishop, 2009). Several factors affect this interplay which include but

are not limited to intensity of strength, age, gender, genetics, activity, rest period,

and training experience (Kilduff et al., 2007; Kilduff et al., 2008., Sale, 2002; Till and

Cooke, 2009). Performance of an activity following VCA will increase if potentiation

offsets fatigue (Rassier and Macintosh, 2000). Moderate rest periods seem to be

optimal (7-10 minutes) in terms of power augmentation after a completing a VCA.

Some studies report PAP in vertical jump 18.5 minutes after VCA following near

maximal back squats; PAP may not fully dissipate until thirty minutes after VCA is

performed (Rixon et al., 2007; Seitz et al., 2014; Wilson et al., 2013).

11

The perception is that stronger individuals are more likely to elicit higher levels of

PAP following VCAs as evidenced in the literature where stronger, more experienced

athletes’ improvements in CMJ were considerably higher than weaker athletes (Chiu

et al., 2003; Crewther et al., 2011; Duthie et al., 2002; Gourgoulis et al., 2003; Keiner

et al., 2013; Rixon et al., 2007). Empirical literature also reports average-to-strong

correlation between strength parameters and sprint performance with both

acceleration (strength r=0.67-0.49; power r=0.73-0.81) and velocity (strength r=0.68-

0.53; power r=0.74-0.82) at 9.1m (Brechue et al., 2010).

When examining the research on post-activation potentiation (PAP) it is important to

note numerous methodological differences in the literature that relate to studies

conducted on rugby players (Bevan et al., 2010). Several potentiation studies have

been conducted on rugby players to improve sprint speed (Bevan et. al, 2010;

Comyns et. al, 2010) and jump performance (Esformes and Bampouras, 2013; Tobin

and Delahunt, 2014). Due to intersubjective variability (percentage of fast-twitch

muscle fibres, relative strength, recovery time), it is highly unlikely that any one PAP

protocol will prove effective for every player tested (Weber et al., 2008). Rationale

dictates that even though not all players will respond and elicit PAP, some will as a

direct result of a well-planned pre-game PAP protocol perform at a higher level than

previously due to enhanced potentiation (Batista et al., 2011).

The modern game of rugby union at the elite level is contested with great ferocity

and games are won or lost dependent on extremely small margins. Modern day

rugby players are bigger, stronger and faster (especially at elite level) compared to

twenty years ago which means they are better equipped to deal with the rigours of

the modern game (Sedeaud et. al, 2014). As a result, players are better at resisting

fatigue that is produced from short duration, high intensity activity (Lombard et. al,

12

2014). The optimal physiological characteristics required demand that rugby specific

periodised conditioning programmes are prescribed and that these will become key

factors in players’ success. This premise demands that the training programme

followed by the subjects of this study replicates the high-velocity high-intensity

efforts, interspersed with low-velocity high intensity efforts (scrums, rucks and

mauls), long recovery periods and numerous stoppages in play they will encounter

throughout the season (Baker and Nance, 1999; Bangsbo et. al, 2006; Hartwig et al.,

2011). The non-traditional concurrent periodised programme prescribed for the elite

youth athletes in this study therefore includes critical elements shown to improve the

physical development of rugby players: work capacity, improving strength, power,

speed, and optimising body size and muscularity for specific positions (Argus et al.,

2012; Duthie, 2006). Their individual conditioning programmes focus not only on

performance but also on minimising injury risk (Oliver et al., 2011). As proportionate

percentages of the different energy systems are called into use during a game it is

logical that all three energy systems are trained. This can prove a challenge as

different modes of training produce different physiological changes that rationalise

the concurrent methods being used to maximise overall fitness development (Rhea

et. al, 2008).

The players reach their peak during the preparatory phase and then have to try and

maintain levels of strength during this ‘in-season’ of up to 35 weeks. This involves

high volumes of team training (non-resistance), game-based fatigue-inducing aerobic

and anaerobic drills, which can prove

13

Figure21. An example of an elite college player’s training plan with games played midweek.

problematic due to the demands of other modes of training (Argus et al., 2012;

Turner, 2011). Intensity of effort is essential to maintain training-induced adaptations

during the competitive phase. Resistance training is limited to two sessions per week

which allows recovery between sessions (Adams et al., 1992; Gamble, 2008; Meir,

2012). Many of the training methods involved use exercises that enhance power as

the players’ have developed an adequate ‘base’ of strength (Baker, 2001).

This period in the players’ burgeoning rugby careers is the ideal time to develop the

coordination and skill techniques needed to acquire high maximum strength values.

Bompa (2000) stated young people cannot merely be considered ‘mini adults.’

Specificity of training becomes more relevant due to them having less experience in

training in resistance training programmes given that they are still developing

(Gamble, 2008). Beginners achieve good transfer from general training but don’t

attain the specific adaptations to complex concurrent or conjugative periodised

programmes that elite athletes may realise (Young, 2006). Applying focus to

14

movement patterns and contraction velocity enhances intermuscular coordination

and helps the athlete ‘tune-in’ to newly acquired force-generating capability (Young,

2006). The players in this study began training using a periodised strength training

programme at the age of sixteen which has allowed them to reach high strength

values quickly (Keiner et al., 2013). Failure to maximise development during this

window of opportunity would limit adult potential and is particularly crucial in

developing elite youth rugby players (Keiner et al., 2013; Oliver et al., 2011).

Research suggests that the greatest improvements in strength and power are

realised within the first one to two years of commencing structured training (e.g.

within an academy environment). In an environment such as this it is important to

emphasise this physical development to realise maximal gains (Argus et al., 2012).

The researcher expects to see more elevated potentiating responses following VCAs

from those within the group with more resistance training experience in comparison

to those in the first year of resistance training (Chiu et al., 2003; Wilson et al., 2013).

Players regularly undergo testing which provides valuable individual objective

feedback of the players’ physical abilities, capabilities, health, strengths and

weaknesses. It also allows evaluation of the training intervention and informs

prescription of individualised training programmes that aim to improve game

performance (Duthie, 2006; Hoffman et al., 2009).

In logistical terms alone traditional methods such as HBS or HFS associated with

eliciting PAP responses (heavy near-maximal isotonic exercise and maximal

isometric contractions) are highly impractical and not feasible prior to a game (Turner

et al., 2014). This study and others contends that PAP can be elicited following depth

jumps or other modified submaximal plyometric exercises on a par with levels

achieved using traditional methods like parallel squats (HBS) which potentially

15

eliminates entirely the need for fatiguing near-maximal contractions where injury is

risked in-season (Batista et al., 2011; Crewther et. al, 2011; de Villareal et. al, 2007;

Kilduff et. al, 2008; Weber et. al, 2008; Young et. al, 1998). If the contention that

elicited PAP levels dissipate up to thirty minutes after VCA is performed is

corroborated then this timeframe offers an incredible opportunity for conditioning

professionals to work potentiating VCAs into warm ups prior to games (Rixon et al.,

2007; Seitz et al., 2014; Wilson et al., 2013). Due to the individual nature of response

to PAP stimuli, the strength and conditioning professional must investigate how each

of their players respond to certain stimuli and be able to prescribe specific optimal

loads (if any) so maximal potentiation can occur prior to a game (Talpey et al., 2014;

Tillin and Bishop, 2009; Turner, accessed ahead of publication; Zemkova et al.,

2014) Researchers should determine whether or not their players are responders or

non-responders within the training environment before they can recommend or reject

a PAP protocol (Till and Cooke, 2009). This information could specifically inform a

warm up design to precondition the neuromuscular system, reducing the risk of injury

and enhancing performance on the field of play (Mina et al., 2014).

The work of Esformes and Bampouras (2013) examined PAP responses to parallel

squats (HBS) and quarter squats (QS) in semi-professional rugby players using

CMJ. They reported PAP elicited following both VCAs but HBS elicited a more

potentiating response at 5 minutes post-VCA. Greater depth of HBS when compared

to QS thereby increased activation of gluteus maximus and work produced was

responsible for increased CMJ height (Esformes and Bampouras, 2013). Structural,

core exercises like HBS and HFS directly relate to rugby, utilising multiple joints,

various loads (eccentric and concentric) and velocities (Baechle and Earle, 2008;

Crewther et al., 2011). HBS have a long history in fitness training, exercise for

16

rehabilitation and strength, and conditioning for performance in sport (Clark et al.,

2012; Mina et al., 2014). The movement emphasises loading of the spine,

stabilisation of the muscular posture, and on different levels of activation of the

primary muscles throughout the movement (Clark et al., 2012). Potentiation of

performance is evident in both CMJ and sprints following HBS in the literature

(Bevan et al., 2010; Esformes and Bampouras, 2013; Kilduff et al., 2007; Kilduff et

al., 2008), but they are more likely to potentiate a CMJ which has a similar

biomechanical pattern than a sprint (Crewther et al., 2011; Yetter and Moir, 2009).

To date, higher volume loads appear to report higher levels of PAP induction when

compared to lighter volume loads. These techniques take advantage of PAP and

increase the rate of force development (RFD) which leads to increases in

acceleration and velocity (Fukutani et al., 2014; Lim and Kong, 2013). Introduction

of the HFS as a VCA in this study is to explore another potentiating stimulus that

individual players in the academy group might respond more favourably to. HFS

employ lighter volume loads which could considerably reduce the stresses and

forces placed on the body in-season and similar levels of activation may potentially

be achieved (Gullett et. al, 2009). Both types of exercise are used in the players’

programmes week on week and produce different challenges and training effects

(Clark et al., 2012).

Interestingly, the vast majority of studies that use CMJ as a VCA utilise a force

platform as the gold standard for measuring height (Glatthorn et al., 2011; Requena

et al., 2012; Sayers et al., 1999). Force platforms measure vertical jump height using

both time in the air and take-off velocity methods (Balsalobre-Fernandez et al.,

2014). Whilst not widely seen as the most accurate method, flight time has been

proved to be highly valid and reliable, most instruments calculate jump height using

17

this method (Glatthorn et al., 2011; Requena et al., 2012). Aside from force platforms

for measurement of CMJ height, other methods include accelerometers, infrared

platforms, high-speed cameras and contact platforms have been used but all have

potential drawbacks. Most are extremely expensive and their use seems to be

confined to elite sports teams or academic laboratories. Further, they are bulky and

need special computer software to analyse the data (Balsalobre-Fernandez et al.,

2014). Current research opportunities demand less bulky, more user friendly

equipment that deliver comparably valid and reliable data. This study used the

MyJump app developed for use with Apple products. This uses low cost, high-speed

camera footage and calculates vertical jump height from the flight time. Using an

inexpensive, user-friendly interface on an iPad allows almost immediate evaluation

of performance and makes objective analysis more readily available to all (Murray

and Olcese, 2011). The use of more modern analytical tools like iPads using apps to

review video footage and performance indicators is becoming the norm especially at

elite level (Evans, 1998a; Evans 1998b). This particular application was tested for

validity against a force platform and delivered near perfect agreement (intraclass

correlation coefficient 0.997) for reliability of reporting CMJ height and for validity

(r=0.995) (Balsalobre-Fernandez et al., 2014).

The ability to out-accelerate an opponent over the first few metres is crucial in rugby

and is ‘arguably the most universally required attribute for success in team sports’

(Comfort et. al, 2012 p. 1). Intermittent high-intensity sprints lasting between 10-22m

often determine the most critical elements in a game (Comfort et al., 2012). Previous

research into PAP and its direct transfer to sprinting in professional rugby players

found that when individual responses were taken into consideration, significant

improvements in 5m and 10m sprint times were realised following preload stimulus

18

(91% 1RM back squat) (Bevan et al., 2010). One study on rugby players’ sprint

performance following HBS (3RM) found no significant potentiating effects four

minutes after VCA. This was partially attributed to a lack of proficiency in technical

application of sprinting technique (Comyns et al., 2010). Sprinting is a complex

activity influenced by neuromuscular function and involves coordination of many

different muscles. The muscles then require activation at the appropriate time and

intensity to maximise performance. This potentially masked the ‘true potentiating

effects’ of the HBS stimulus but more likely was due to the inappropriate rest interval

and single set procedures prescribed following the VCA (Comyns et al., 2010;

Wilson et al., 2013).

A recent meta-analysis of studies relating to PAP suggests: moderate intensity (60-

85% 1RM), multiple sets and rest periods of between 7-10 minutes in length are

ideal in optimising PAP-inducing effects (Lim and Kong, 2013; Wilson et al, 2013).

There are also strong cases for lengthening rest intervals still further when taking

into account reported PAP in studies at 18 minutes and premise that it may not

dissipate until thirty minutes after VCA (Rixon et al., 2007; Seitz et al., 2014; Wilson

et al., 2013). One of the focuses of this study is potential PAP responses elicited

during the initial starting phase and acceleration phase of a sprint measured over

10m. Out-acceleration of an opponent over the first few metres is arguably more

important in achieving success than peak running velocity (Comfort et al., 2012). It is

important to generate appropriate power when completing a linear sprint which is

initiated through explosive concentric force production of the hip and knee extensor

muscles Therefore it is essential to include representative training methods that

generate sufficiently greater force and can be prescribed as part of any specific

warm up (Byrne et al., 2014).

19

Plyometric exercises utilise the mechanism of the stretch-shortening cycle (SSC)

that describe an eccentric or lengthening phase or stretch followed by amortisation

(isometric transitional phase), leading into an explosive concentric contraction. This

phenomenon is sometimes referred to as the reversible action of muscles (Turner

and Jeffreys, 2010). When usage of SSC is applied efficiently, mechanical energy

recovery of up to 60% has been reported when applied to economical sprint

technique and contributes to a greater level of non-metabolic energy sources in

running velocity (Markovic et al., 2007; Turner and Jeffreys, 2010; Verkoshansky,

1996; Voigt et al., 1995).

Most human motion and many sporting movements are influenced by eccentric-

concentric coupling or SSC and performance depends on its efficient use (Cowell et

al., 2012). Essentially, plyometric exercises involve jumping and landing. Failing to

develop these basic skills adequately limits athletic potential and exposes players to

greater risk of injury (Turner and Jeffreys, 2010). Jeffreys (2007) advocates use of a

method of introducing plyometric exercises (pyramid), which involves three

categories of exercise whose intensity can be manipulated within each stage. This

focuses on technical development and ensures that on completion of the process,

players are technically proficient in all plyometric exercises (Turner and Jeffreys,

2010). Strength and conditioning coaches integrate the use of plyometric exercises

into resistance programmes either as stand-alone or as part of complex training

protocols; plyometric training has been accepted as a standard training method for

improving leg muscle power and athletic performance (Markovic et al., 2007; Turner

and Jeffreys, 2010). Ideally to ensure maximal performance, plyometric exercise

should follow strength training to reduce the risk of injury to the muscle tendon

complex. This increases quality and quantity of type II muscle fibres and peak power

20

output (Turner and Jeffreys, 2010). Loads, strain and velocity can be modified during

the eccentric phase during exercises that impose various mechanical stimuli and

manifest different adaptational and functional effects (Cowell et al., 2012). The aim is

for the most forceful concentric contraction possible that is achieved through: pre-

activation of the musculature; short amortisation phase; short duration contractions;

high speed eccentric muscle action velocities and small amplitude movements

(Cowell et al., 2012). Thus, the potentiating effects of the SSC increase with the

speed of the eccentric contraction, and decreases the longer the amortisation phase

lasts (Flanagan and Comyns, 2008). Fundamentally, the rate of force development

(RFD) is trainable and elite youth rugby players can reach peak values quickly and

improvements in CMJ heights may be realised (Turner and Jeffreys, 2010).

Complex training (which is based on the phenomenon of post-activation potentiation)

can be defined as a set-for-set combination of a heavy resistance exercise (preload)

followed by a biomechanically similar plyometric exercise (Bogdanis et al., 2014;

Weber et al., 2008). Repeated over time superior acute and chronic gains occur in

muscular strength and power when compared with other training programmes.

Performance of explosive power exercises are also enhanced while muscles are in

this potentiated state (Docherty et. al, 2004; Weber et al., 2008).

This study aims to demonstrate the potential in eliciting potentiation from plyometric

stimulus (depth jumps) when compared to the heavy resistance exercises HFS and

HBS (Bullock and Comfort, 2011; De Villareal et al., 2007). If supported this would

have major implications to improvements in performance on the field and could be

achieved with less acute fatigue, allowing shorter and more efficient periods of intra-

set rest (Tobin and Delahunt, 2014). DJ should only be conducted by those athletes

engaged in plyometric training. Less demanding drills should be mastered prior to

21

more complex drills like DJ that develop explosiveness and the ability to utilise

strength as quickly and forcefully as possible (Adams et al., 1992; NSCA Position

Statement, 1993; Turner and Jeffreys, 2010).

There are a small number of studies in the literature that utilise depth jumps to elicit

PAP responses. One study by Till and Cooke (2009) found that modified tuck jumps

were ineffective in eliciting a PAP response and failed to improve 10m and 20m

sprints (Byrne et al., 2014). A recent example by Turner (accessed ahead of

publication) used body mass and body mass +10% to elicit PAP in plyometric warm-

up protocols prior to sprint performance and potentiated subsequent sprint

performance. Other research employing modified DJs demonstrated significant

potentiation responses in subsequent CMJ power output but results differed largely

due to different box heights in all these studies (33 cm vs. 43.2 cm vs. 60 cm box)

and different variations of rest intervals prior to subsequent activies (Bullock and

Comfort, 2011; Hilfiker et al., 2007; Massamoto et al., 2003). Chen et al. (2013)

found increases in CMJ following both single and double set drop jumps at intervals

of two minutes, six minutes and twelve minutes with more significant increases after

two sets were completed. These findings were dissimilar to those of de Villareal et al.

(2007) whose use of a three-set DJ experiment design may have induced fatigue,

masked potentiating effects and attributed to no significant improvements being

reported in CMJ. Another factor that may affect performance of CMJ following DJ is

knee joint, ankle joint or leg stiffness (Turner and Jeffreys, 2010). Komi (2003)

suggests that high levels of stiffness in lower limb muscles during SSC exercises

increase the amount of stored and reused elastic energy.

There is only one study in the literature that measures PAP response to

experimental protocols using depth jumps in subsequent sprint performance (20m).

22

The study found that DJ elicited PAP using a protocol including three DJ and

measured responses one minute after completion of the VCA (Byrne et al., 2014).

Tobin and Delahunt (2014) measured CMJ following a plyometric intervention at

intervals of between one and five minutes. They found the magnitude of PAP elicited

was comparable to that reported elsewhere in the literature (Crewther et al., 2011;

de Viilareal et al., 2007; Kilduff et al., 2008; McCann and Flanagan, 2010; Weber et

al., 2008; Young et al., 1998). Importantly though their study did not replicate recent

research using rugby players that suggests rest intervals of around eight minutes

may be required before significant performance improvements are realised (Bevan et

al., 2010; Kilduff et al., 2008). Bomfim Lima et al (2011) employed a DJ protocol to

measure PAP responses in CMJ and 50m sprints. This study lends part of its

experimental framework from that research. They found that the DJ potentiation

protocol was effective in inducing PAP and improving performance in both CMJ

height only at a time interval of 15 minutes in the experimental condition

(measurements taken at five, ten, fifteen minutes) and sprint speed was improved at

the 10 and 15 minute intervals only (Bomfim Lima et al., 2011). Higher box heights

employed in some of the named studies may have affected muscle activation levels

and forces accepted throughout the eccentric phase and higher maximal forces on

landing. This implies that jumping high whilst maintaining brief ground contact times

is more fatiguing using higher boxes compared to lower boxes (Bullock and Comfort,

2011). Dynamic contractions also result in earlier onset of peripheral fatigue than

isometric contractions due to lactate accumulation (Lim and Kong, 2013).

23

Dissertation Methodology

A total of n=9 healthy male rugby union players from Coleg Sir Gar Rugby Academy

volunteered to participate in this study. They were made up of four forwards and five

backs. Each participant provided written informed consent forms signed by

parent/guardian(s). Examples of these can be found in appendix ii. The participants

(age 17 ± 1 year, mass 86.48 kg ± 12.40 kg) were either Regional under 18’s squad

members or WRU-tracked players. Participants weightlifting and resistance training

experience varied from between 12 months to 30 months, all were competent in all

necessary techniques and were aware of potential risks involved having completed

inductions at the start of the year. All participants reserved the right to withdraw from

the process at any time for any reason and all aspects of the study were conducted

under the strictest of confidence. All tests were completed in an organised manner

with health and safety of participants in mind by dedicated, trained support staff and

the researcher (Harman, 2008). The participants completed a total of five sessions: a

session where subjects familiarised themselves with CMJ and 10m sprint

procedures, and calculated optimal DJ height (Byrne et al., 2014), and four PAP

testing sessions (HBS, HFS, DJ, Control). Each individual took part in optimal DJ

height test to incrementally test their ability to complete the task effectively and

safely (Byrne et. al, 2014). Athletes over 100kg should not be prescribed DJ of over

0.51m in height (NSCA Position Statement, 1993). The procedures employed are

outlined in fig 3.1.

24

Maximum Jump Height DeterminationThree practice depth jumps from each of the four drop heights (0.20,

0.30, 0.40 and 0.50m) with fifteen seconds rest in between sets (Byrne et al., 2010).

After this height is raised 0.1m until the optimal height is found (Bomfim Lima et., al 2011).

Figure 3.1. Optimising depth jump height. (Adapted from Byrne et al., 2014)

All participants in the study completed every session, testing each VCA and control

through the process. All participants were fit and healthy and were medically cleared

to take part by the team’s support staff. The order of the sessions was randomised

across the participant population (Yetter and Moir, 2008). The researcher and their

trained assistants were present for each session (Rimmer and Sleivert, 2000). The

testing was carried out in as near as identical conditions for each test at the same

time of day (8 a.m.) and a minimum of 48 hours after the previous session to

minimise fatigue and to ensure maximum reliability (Harman, 2008).

This study used a randomised, crossover design to investigate the potentiating

effects of four VCA treatments (HBS, HFS, DJ and control) on CMJ and 10m sprint

speed (Yetter and Moir, 2008). The subjects performed three CMJs and three

subsequent 10m sprints with 3 minutes rest between each at four, seven, and ten

minutes post-VCA. CMJ height was measured at each interval as was 10m sprint

speed. Regional and WRU tracked age grade players undergo physical and strength

testing three times during the year (WRU, unpublished data), so 1RM’s were

available from which to calculate appropriate load for heavy front squats and heavy

back squats. Standard 20kg Olympic barbells and Eleiko Olympic weights disks were

used in this study. It was determined that as the subjects were in-season maximal

testing was inappropriate, therefore 1RM HFS was obtained from WRU data tested

25

for in April during a rest period of three weeks from usual game activity (WRU,

unpublished data). Following this HBS 1RM was determined by calculating a load

equivalent to 125% of 1RM HFS (recommended by Ajan and Baroga, 1988).

Fig. 3.2 Diagram of experiment design (adapted from Yetter and Moir, 2008 and Bomfim Lima et. al, 2011).

Fig 3.2 demonstrates the structure of the experiment design. The process took no

more than twenty-five minutes for a participant to complete a VCA test session and

the timings were crucial to ensure no participants were kept waiting. Due to the

limitation of space in the gymnasium and equipment, testing was conducted on a

staggered basis. This meant in any session no more than the nine participants took

26

Standardized 5 minute warm-up using general and specific body

movement patterns

Heavy Back Squat

5 x 30% 1RM

4 x 50% 1RM

3 x 70% 1RM

Heavy Front Squat

5 x 30% 1RM

4 x 50% 1RM

3 x 70% 1RM

Depth Jump

2 sets of 5 jumps

15 secs between each jump

3 minutes rest between sets

CONTROL

Walk for three minutes

At 4 minutes CMJ then sprint

At 7 minutes CMJ then sprint

At 10 minutes CMJ then sprint

4 minutes rest

6

minutes

exercise

4 minutes rest

part and they were staggered at five minutes for HBS and HFS sessions, six minutes

on control sessions and seven minutes for DJ sessions.

The warm up employed various full body dynamic movements designed to: elevate

core body temperature, enhance motor unit excitability, neuromuscular activity,

improve kinaesthetic awareness, utilising specific biomechanical movements,

maximising the ranges of motion used in a game and reducing the risk of injury

(Jeffreys, 2007; Mina et al., 2014). Research suggests these factors are associated

with post-activation potentiation which would contribute to performance

enhancement (Costa et al., 2011). The subjects performed ten exercises

consecutively for thirty seconds each (300 seconds or five minutes total). The

exercises employed are illustrated in fig 3.3.

Figure 3.3. Standardised warm up procedure conducted before each session.

27

Following the warm up the participants rested for four minutes before walking for

three minutes. The participants then rested for a further four minutes before

completing a CMJ for height followed immediately by a 10m sprint. After three

minutes had elapsed, the subject completed another CMJ for height followed

immediately by a 10m sprint. After three minutes had elapsed, the subject completed

a final CMJ for height followed immediately by a final 10m sprint. The protocol for the

CMJ is in fig 3.4. Measurements of CMJ for height were calculated based on flight

times recorded using the MyJump app. The CMJ station was directly beside the 10m

sprint station. The layout of the gymnasium allowed one researcher to record CMJ

height using the MyJump app (which is available on the Appstore) before the

participant moved sideways from the CMJ station to complete the sprint please see

layout in appendix lxi. To capture the video footage required, the researcher lay flat

on the floor with the iPad facing the participant (in the frontal plane), 1.5 m from the

participant. Participants were instructed to perform the CMJ using their arms if they

so wished. They started from a static standing position and with their legs straight

during the flight phase of the jump (Haekkinen and Komi, 1985). The landing was

performed simultaneously with either feet keeping ankle dorsiflexion. Participants

were instructed to jump as high as possible (Balsolobre et al., 2014). On recording

the footage, the researcher selected the take-off position frame using the play and

scroll video button highlighted in fig 3.5. The researcher then scrolled on to locate

the landing frame. Once this had been selected the app calculated the flight time (in

milliseconds). This process took approximately 20 seconds per attempt.

28

Figure 3.4. Experimental countermovement jump protocol.

Figure 3.5. Diagram of the MyJump app interface during a CMJ test session. Highlighted are some of the tools necessary to calibrate performance and capture data.

(Brower) Digital timing gates were employed to measure sprint times as they deliver

much higher degrees of reliability (within 0.02 seconds or 0.1%) in comparison to

stopwatches where human error has been shown to reduce reliability and validity of

29

data, leading to times of up to 0.24 second faster than actual (Walker and Turner,

2009). A separate researcher was responsible for monitoring the station and

recording times. A 10m course was measured in the gymnasium which left an

appropriate distance of 15 metres for deceleration (Young et al., 2008). Brower Test

Centre timing gates were placed at the start line and the 10m line. All subjects

started from a consistent semi-crouched standing position 30 centimetres behind the

line for each sprint. This start position is much more suitable for rugby union players

with large stature or mass than a three point stance which would involve a great deal

of familiarisation (Duthie et al., 2006). As soon as they broke the plane of the timing

gate the timer started and once they broke the plane at the 10m gate the time was

recorded. All the subjects were familiar with these procedures having regularly tested

using this procedure during WRU testing. Following the first round of CMJ then sprint

at four minutes post-VCA each participant rested for three minutes then completed

round two of CMJ then sprint at seven minutes post-VCA, followed by a further three

minutes rest and a final CMJ and sprint at ten minutes post-VCA.

The calculated 1RMs of HBS and recorded 1RM of HFS were used to calculate

loads for set 1 (5 x 30%), set 2 (4 x 50%) and set 3 (3x 70%) of 1RM for each

participant as the loads required for both HBS and HFS prior to the experiment. To

ensure high levels of safety spotters were present to monitor lifters during sets of

HBS and HFS. Their role was also to ensure the top of the thighs were parallel to the

ground during the lowest point of the descent and that there was no assistance

during the ascent even though moderate loads were used (Adams et al., 1992;

Fukutani et al., 2014; Yetter and Moir, 2008). Two minutes rest were provided

between each of the three sets in each VCA (Yetter and Moir, 2008). Following the

VCA (both HBS and HFS followed exactly the same procedures), the participants

30

rested for four minutes before completing round one of CMJ and 10m sprint at four

minutes post-VCA. Following a further three minutes rest, round two of CMJ and

10m sprint was completed at seven minutes post-VCA, and round three of CMJ and

10m sprint was completed following a final three minutes rest at ten minutes post-

VCA.

Following the warm up the participants rested for four minutes before completing one

set of five DJs with 15 seconds rest in between each one (in line with protocol

discussed in fig 3.6). Three minutes rest followed set one, set two followed the same

procedures as the first. The platform employed was 0.50m in height. Following

completion of set two, the participants rested for four minutes before completing

round one of CMJ and 10m sprint at four minutes post-VCA. Following a further

three minutes rest, round two of CMJ and 10m sprint was completed at seven

minutes post-VCA, and round three of CMJ and 10m sprint was completed following

a final three minutes rest at ten minutes post-VCA.

2. Once the subject contacts the ground after the step off, it is paramount to immediately rebound which redrects stored potential energy in the form of kinetic energy vertically

3. Minimising the amortization phase time is critical to harness the most stored energy and therefore generate the largest SSC force.

Depth Jump Protocol

1. A depth jump is performed by the subject stepping off an elevated platform (at their OJH), landing, then reversing the eccentric action into a concentric vertical upward action.

Figure 3.6..Experimental Depth Jump protocol.

31

Analysis of variance was conducted on all variables to include mean, median, mode

and standard deviation. Testing for statistical significance took place using paired t-

tests for both 10m sprint speed and CMJ performance which were conducted

between: each VCA versus control; between HBS and DJ; between HBS and HFS;

and between DJ and HFS (all of these were calculated at four, seven and ten

minutes post-VCA). Further variables of statistical significance were examined using

single factor anovas using results from all VCAs plus control for both 10 sprint speed

and CMJ performance.

Following these tests more paired t-tests were conducted separately on both

forwards and backs for both 10m sprint speed and CMJ performance: each VCA

versus control; between HBS and DJ; between HBS and HFS; and between DJ and

HFS (all of these were calculated at four, seven and ten minutes post-VCA). Further

variables of statistical significance were examined for forwards and backs using

single factor anovas using results from all VCAs plus control for both 10 sprint speed

and CMJ performance.In addition to the stated comparisons, some comment is

made on individual responses/non-responses to stimuli.

32

Results

Figure 4.7. Comparing 1RM of study to: college squad and WRU national average.

Table 4.8. Significant results relating to depth jumps versus control.

33

Figure 4.9. Countermovement jump performance (control) compared against own college and national average.

Figure 4.10. Highlighting mean countermovement jump performance @4 minutes post VCA.

34

Table 4.11.Significant results relating to HFS versus control.

Figure 4.12. Highlighting mean countermovement jump performance @7 minutes post VCA.

35

Table 4.13. Significant results relating to HBS versus control.

Figure 4.14. Highlighting mean countermovement jump performance @10 minutes post VCA.

36

DJ HBS p-valueCMJ DJ vs. HBS @ 4 mins post VCA 42.67522 40.32165 0.024144

CMJ DJ vs. HBS @ 10 mins post VCA 43.93162 39.44472 0.045515

Statistically significant results from t-testsfor CMJ performance between DJ and HBS

Table 4.15. Significant results relating to HBS versus DJ.

Figure 4.16 Worst, best and mean CMJ performance @seven minutes post HBS VCA.

Figure 4.17 Worst, best and mean CMJ performance @seven minutes post HFS VCA.

37

Table 4.18. Significant results relating to HFS versus DJ.

Control DJ p value1.833333 1.791111 0.0280431.837778 1.768889 0.00032

1.9075 1.825 0.0090361.794 1.736 0.002786

Statistically significant results from t-tests for 1om sprint speed between control and DJ

10m sprint DJ vs. control @ 7 min post VCA10m sprint DJ vs. control @ 10 min post VCA

10m sprint Forwards DJ vs. control @ 7 min post VCA10m sprint Backs DJ vs. control @ 10 min post VCA

Table 4.19. Significant results relating to 10m sprint performance between control and DJ.

Control HFS p value1.841111 1.801111 0.049351.837778 1.80444 0.014124

1.8925 1.8475 0.0464771.81 1.744 0.019484

Statistically significant results from t-tests for 1om sprint speed between control and HFS

10m sprint HFS vs. control @ 4 min post VCA10m sprint HFS vs. control @ 10 min post VCA

10m sprint Forwards HFS vs. control @ 10 min post VCA10m sprint Backs HFS vs. control @ 4 min post VCA

Table 4.20. Significant results relating to 10m sprint performance between control and HFS.

38

Figure 4.21. Highlighting mean 10m sprint performance @four mins post VCA.

Figure 4.22. Highlighting mean 10m sprint performance @seven mins post VCA.

39

Figure 4.23. Highlighting mean 10m sprint performance @ten mins post VCA

Control HBS p value1.841111 1.76 0.0002891.833333 1.761111 0.0004571.837778 1.792222 0.003101

1.88 1.805 0.0352541.9075 1.81 0.0044441.8925 1.8325 0.004892

1.81 1.724 0.004421.774 1.722 0.0233081.754 1.76 0.028823

10m sprint Forwards HBS vs. control @ 7 min post VCA10m sprint HBS Forwards vs. control @ 10 min post VCA

10m sprint HBS Backs vs. control @ 4 min post VCA10m sprint HBS Backs vs. control @ 7 min post VCA

10m sprint HBS Backs vs. control @ 10 min post VCA

Statistically significant results from t-tests for 1om sprint speed between control and HBS

10m sprint HBS vs. control @ 4 min post VCA10m sprint HBS vs. control @ 7 min post VCA10m sprint HBS vs. control @ 10min post VCA

10m sprint Forwards HBS vs. control @ 4 min post VCA

Table 4.24. Significant results relating to 10m sprint performance between control and HBS.

HBS DJ p value10 m Sprint HBS vs DJ @4 min post VCA 1.76 1.817778 0.00400510 m Sprint HBS vs DJ @7 min post VCA 1.761111 1.791111 0.044106

10 m Sprint Forwards HBS vs DJ @10min post VCA 1.724 1.79 0.02568710 m Sprint Backs HBS vs DJ @4 min post VCA 1.8325 1.8925 0.03884

Statistically significant results from t-testsfor 10m sprint speed between DJ andHBS

Table 4.25. Significant results relating to 10m sprint performance between DJ and HBS.

40

Figure 4.26. Comparison of study control 10m sprint time versus college squad overall and national average.

HFS DJ p value1.804444 1.76889 0.049727

Statistically significant results from t-tests for 1om sprint speed between HFS and DJ

10m sprint HFS vs. DJ @ 10 min post VCA

Table 4.27. Significant results relating to 10m sprint performance between DJ and HFS.

HFS HBS p value1.801111 1.76 0.0145591.794444 1.761111 0.041505

1.8725 1.805 0.03675210m sprint HFS vs. HBS @ 7 min post VCA

10m sprint Forwards HFS vs. HBS @ 4 min post VCA

Statistically significant results from t-tests for 1om sprint speed between HFS and HBS

10m sprint HFS vs. HBS @ 4 min post VCA

Table 4.28. Significant results relating to 10m sprint performance between HBS and HFS.

41

Figure 4.29. Worst, best and mean sprint times @four minutes post HBS VCA.

Figure 4.30. Worst., best and mean sprint times @four minutes post DJ VCA.

42

Figure 4.31. Worst., best and mean sprint times @ten minutes post DJ VCA.

Fig 4.32 Worst., best and mean sprint times @ten minutes post DJ VCA.

43

Figure 4.33. Comparing relative strength of study to: college squad and WRU national average.

44

Discussion

The potentiated state of muscle can produce acute effects on performance

capabilities, and repeated over time can induce superior chronic physiological

adaptations in comparison with other training approaches (Gullich and

Schmidtbleicher (1996). The initial aims of this study were to compare several VCAs

(control, HBS, HFS and DJ) and hypothesised that potentiating effects are

comparable from plyometric VCAs (DJ) to more traditional methods such as HBS

and HFS. This would lead to induction of significantly reduced levels of fatigue and

allow shorter, more time efficient rest intervals between sets (Tobin and DeLahunt,

2014). This study prescribed use of more moderate loads for both HFS and HBS as

proportions of 1RM which were identified as being ideal for eliciting PAP (Lim and

Kong, 2013; Wilson et al, 2013). 1RM HFS were determined at the WRU National

Centre of Excellence in the Vale of Glamorgan (see appendix v). Data retrieved

produced mean loads for HFS of 105.83 kg ± 12.25kg, which helped determine

mean loads for 30%, 50% and 70% 1RM values of 31.75kg ± 3.67kg, 52.92kg ±

6.12kg and 74.08kg ± 8.57kg. Accordingly estimated loads for 1RM of HBS were

128.82kg ± 12.82kg, providing mean 30%, 50% and 70% 1RM load values of

39.69kg ± 4.59kg, 66.15kg ± 7.65kg and 92.60kg ± 10.72kg (Ajan and Baroga,

1988). Fig. 4.7 highlights a comparison of 1RM front squat from study participants

versus their rugby academy counterparts and versus WRU tracked players. All

participants of the study were above the national average see appendix v (WRU,

2015). Part of the rationale of employing loads directly calculated from 1RM HFS to

measure sprint performance was as a direct result of recommended future research

directions outlined by Yetter and Moir (2008).

45

As reported previously, studies on potentiation often find that VCAs produce a

potentiating effect (Bevan et al., 2010; Esformes and Bampouras, 2013; Kilduff et al.,

2007; Kilduff et al., 2008) or participants are unresponsive to stimuli (Byrne et al.,

2014; de Villareal et al. 2007; Till and Cooke, 2009). It is extremely important to note

that comparisons in the literature regarding PAP can be conflicting and tentative,

largely due to the numerous methodological approaches employed in various studies

(Hodgson et al., 2005). This research drew on established procedures from previous

research, attempting to draw statistically significant data and comparisons that might

have direct and current impact on strength and conditioning PAP practice and inform

further research (Bomfim Lima et al., 2011; Yetter and Moir, 2008).

In this study, all VCAs produced potentiating effects versus the control for both CMJ

performance and 10m sprint speed. Statistical analyses were carried out using

paired t-tests and single factor anovas (see appendices xv-lvi). CMJ performance

following DJ VCA was significantly higher than control VCA at four, seven and ten

minutes post VCA (see table 4.8). It is also worth noting that the control mean CMJ

performance of the participants of this study performed better in this setting

compared to the average performance for the entire Coleg sir Gar rugby academy in

April WRU testing and versus WRU nationally tracked players (see fig. 4.9. and

appendix vii). The highest mean CMJ performance during this study was following

the DJ VCA at four, seven and ten minutes post VCA (see figs. 4.10, 4.13 and 4.14.).

These elicitations were not realised at five or ten minutes post DJ VCA using similar

DJ VCA procedures by Bomfim Lima et al. (2011), but mean CMJ height increased

at fifteen minutes post DJ VCA.

Specific verbal instruction was also employed by the researcher during this study

when CMJ performances were measured. Participants were instructed to ‘jump high

46

and a little faster than your last jump’. This encouraged participants to perform DJ

with significantly shorter ground contact times similar to previous research

(Arampatzis et al., 2001). Significant potentiation of performance in CMJ height

following HFS VCA was also evident at four, seven and ten minutes post VCA when

compared to control VCA. The most significant improvement occurring within the

backs at seven minutes post VCA (see table 4.11). Interestingly, significant

potentiation was observed in CMJ performance following HBS VCA at four and

seven minutes post VCA in comparison to control VCA, but not at ten minutes post

VCA when the lowest mean HBS scores were recorded for both forwards and backs

(see table 4.13 and fig 4.14). Following this when comparing DJ VCA to HBS VCA,

the researcher found that the DJ VCA had a more significant potentiating effect for

CMJ performance at both four and ten minutes post VCA than the HBS VCA

(p=0.024144 at 4 min and p=0.045515 at 10 min see figs. 4.10, 4.14 and table 4.15).

Similarly, in comparison to HFS VCA the DJ VCA produced a more significant

potentiating effect at four and ten minutes post VCA (p=0.026769 at 4 min; p=0.0422

at 10 min see table 4.18).

These findings confirm that plyometric VCAs requiring little or no equipment can

induce PAP of similar or greater levels than traditional methods. Notable individual

CMJ performances following both HBS and HFS VCAs at seven minutes are

highlighted in figs. 4.16 and 4.17. Also when comparing HFS and HBS VCAs for

CMJ performance at four, seven and ten minutes post VCA the values are extremely

closely correlated (Pearson correlations at four mins post VCA r=97.9%, at seven

minutes, r=96.06% and at ten minutes, r=97.02. See appendix xix). Similar levels of

PAP are elicited in these instances using much lighter loads which reduces

47

considerably the forces and stress placed on the body of the subjects’ in-season

(Gullett et. al, 2009).

The HBS VCA produced a clean sweep of nine significant paired t-tests that

demonstrated the most potent potentiating effects versus the control VCA for 10m

sprint times overall at four, seven and ten minutes post VCA and at the same

intervals for both forwards and backs (see table 4.24). This is in complete contrast to

a previous study on rugby players’ sprint performance that used a comparable 3RM

HBS (versus this study’s 3 reps of 70% 1RM final set) that found no significant

potentiating effects four minutes after VCA. This was attributed to poor sprint

technique but is more likely to have been due to the single set nature of the

experiment design (Comyns et al., 2010; Wilson et al., 2013).This study used a

standardised warm up that incorporated specific biomechanical movements

necessary to facilitate correct movement patterns followed by pre-loading sets of

HBS of 30% and 50% of 1RM prior to a final working set of 70%1RM (Yetter and

Moir, 2008).

Potentiation is also significantly realised when comparing HFS VCA 10m sprint times

with control VCA at both four and ten minutes post VCA (p=0.04935 at four min;

p=0.014124 at ten min Table 4.20). In terms of comparing HFS with HBS, significant

indicative data suggests that the HBS VCA elicits more potentiation during 10m

sprint than HFS VCA at four and seven minutes post VCA (see table 4.28). These

results differ slightly to those realised in the research of Yetter and Moir (2008) that

provides the HFS and HBS control procedures used in this study. In that study, HBS

were found to elicit significant levels of potentiation in comparison to control and HFS

in 10m sprints four minutes post VCA, whereas HFS recorded times slower than

control during the same study. The results realised in Yetter and Moir (2008) may be

48

partially attributed to a wholly inappropriate warm up procedure where a five minute

cycle on an ergometer was followed by a four minute walk (Yetter and Moir, 2008).

Research that reports lack of potentiation commonly regards poor or insufficient

activation levels of muscles by the VCAs employed, when in fact it is likely grounded

in similar lack of thought or understanding in planning a warm up that prepare the

muscles for the VCAs (Batista et al., 2011). Scientific evidence suggests that active

warm ups appear to be of greater benefit than passive warm ups (like five minutes

on a bike). A warm up should involve all the major muscles that will be used during

the training session or in competition (Baechle and Earle, 2008). These muscle

actions should be similar to the activities involved in the main sessions (see fig. 3.3.

for movements employed that activate muscles employed in CMJ performance and

10m sprints). In addition, evidence based research suggests advantageous

increases in body temperature with dynamic stretching when compared with static

stretching, increases in neuromuscular activity and are associated with higher

potentiating responses (Costa et al., 2011). Based on the results of this study it

would be a fair to suggest that its warm up proved far more conducive to elicitation of

PAP than that of Yetter and Moir (2008) (Costa et al., 2011).

The same research also suggests that front squats can be expected to have a

greater PAP effect on maximal velocity and maintenance of maximal velocity than

back squats. This is due to the additional movement of hip extensors afforded a

participant in completion of a front squat in comparison to completion of a back

squat. This study can neither support nor refute these suggestions as the 10m sprint

time employed in this study does not allow participants to reach maximal velocity

(Yetter and Moir, 2008). Rationale for the choice of 10m sprint was purely that out-

acceleration of an opponent on the rugby field is arguably much more important than

49

peak velocity (Comfort et al., 2012). Significant results were also realised when 10m

sprint times for DJ VCA were compared with control VCA at seven and ten minutes

post VCA (Table 4.19, figs. 21, 22 and 23). Several studies suggest that DJ are a

more appropriate biomechanical fit for elicitation of PAP in sprint performance, which

does not support the findings of this study at both four and seven minutes post VCA

or those of Till and Cooke (2009). Turner et al. (2014) criticized their use of vertical

plyometric VCA stimuli in the form of tuck jumps in their attempt to elicit PAP in sprint

performance (horizontal) which ultimately proved unsuccessful (Bomfim Lima et. al,

2011; Byrne et. al, 2014; Chen et. al, 2013; Crewther et. al, 2011). Significant and

thorough thought needs to go into the entire process of designing PAP protocols and

warm ups so that the researcher creates the most conducive environment for the

elicitation of PAP. Further conflicting comparisons to other research are drawn as

HBS VCAs are said to be a better biomechanical fit for potentiation in CMJ

performance rather than 10m sprints (Crewther et al., 2011). Whilst HBS VCA does

elicit PAP versus control and other measures for CMJ performance they clearly

report much stronger PAP effects versus HFS, DJ and control VCAs for 10m sprint.

Importantly though, the DJ VCAs potentiating effects seem to last longer than HBS

VCA for 10m sprint measured and the DJ VCA is the most potentiating VCA at ten

minutes post VCA (fig. 4.23). The DJ VCA produces a significant potentiating

comparison versus HFS VCA at ten minutes post VCA in addition to the improved

time versus HBS VCA (p=0.049727, table 4.27). This does not provide definitive

evidence that DJ or plyometric stimuli are more potent in elicitation of PAP because

there is a uniquely fine balance between fatigue and potentiation which may in this

case have resulted in reduced levels of PAP being elicited following HBS and HFS at

ten minutes post VCA in this study for 10m sprint (Wilson et al., 2013). Numerous

50

factors might also be valid explanations for some of the results: intensity of the VCA

employed in this study, volume/load, rest period and level of experience of

participants (Kilduff et al., 2007; Kilduff et al., 2008; Sale, 2002). For example,

instead of using the recommended moderate loads of 30%, 50% and 70% of 1RM

and rest periods of between seven and ten minutes (used in this study), participants

might have employed loads for HBS and HFS VCAs of nearer 90% 1RM in line with

the study of McBride et al. (2005). Their study failed to significantly elicit PAP over

10m sprints despite the heavier loads which was a determining factor in selecting

rest periods (between seven and ten minutes) that would optimise potential

potentiating effects (Seitz et al., 2014).

Several studies have used DJ or plyometric interventions to elicit PAP in subsequent

sprint or CMJ performance but have only adopted time intervals post VCA of

between one and five minutes (Byrne et al., 2014; Tobin and Delahunt 2014). Based

upon the recent research in rugby that suggests intervals approaching eight minutes

may be required before significant performance improvements are realised and the

results of this study it may be necessary to extend the time intervals to almost twenty

minutes (Bevan et al., 2010; Kilduff et al., 2008). Bomfim Lima et al (2011)

employed a DJ protocol to measure PAP responses in CMJ and 50m sprints. This

study lends part of its experimental framework from that research. They found that

the DJ potentiation protocol was effective in inducing PAP and improving

performance in both CMJ height only at a time interval of 15 minutes in the

experimental condition (measurements taken at five, ten, fifteen minutes) and sprint

speed was improved at the 10 and 15 minute intervals only (Bomfim Lima et al.,

2011). This finding demands that further research needs conducting past ten

minutes post VCA to establish how long under these conditions it takes for the

51

potentiating effects to dissipate (HBS and HFS already seem to be dissipating under

these conditions). Importantly though, comparisons were made only of DJ VCA

versus control which is why this research may serve as a useful barometer to

measure progress versus traditional heavily loaded resistance exercises, and with

additional corroborative research challenge established research that suggests HBS

and HFS are superior VCAs for elicitation of PAP (Chatzopolous et al., 2007;

Gourgoulis et al., 2003; Kilduff et al., 2008; McBride et al., 2005; Rixon et al., 2007;

Yetter and Moir, 2008; Young et al., 1998).

Individual stand out results in this study also allude to facts that at four minutes post

VCA, the HBS VCA has greater potentiating effects, and that by ten minute post

VCA the DJ VCA elicits higher levels of PAP (figs. 4.29, 4.30, 4.31 and 4.32). Due to

intersubjective variability wide ranges of potentiating results were recovered from the

data for both CMJ performance and 10m sprint speed (appendices x-xiv). PAP

protocols that prove responsive for one player in eliciting PAP will prove ineffective

for others (Weber et al., 2008). Interestingly the most significant individual

performances in the study and in the examples highlighted (figs. 4.16, 4.17, 4.29,

4.30, 4.31 and 4.32) all involve subjects B2, B4 and B5 who are ranked numbers 1, 2

and 3 in relative strength in relation to this study (see appendix x). This corroborates

current research that suggests that relatively more strong subjects should show

greater signs of potentiation in both CMJ and sprint performance (Crewther et al.,

2011; Keiner et al., 2013; Seitz et al., 2014). Typically greater potentiation responses

occurs in these instances may be due to the stronger individuals having higher type

two content than relatively weaker individuals (Tillin and Bishop, 2009).

More comparisons were drawn from sprint performances during this study versus

WRU physical performance test data that measured both Coleg sir Gar academy

52

members and all nationally tracked players (WRU, 2015). Based upon the raw data

(fig. 4.26) the participants of this study are significantly slower (using mean control

VCA 1.84 seconds) than the other recorded groups (CSG =1.73 seconds, WRU

national average =1.73 seconds). However, the test conditions were markedly

different with the WRU testing taking place in the afternoon in a warmer facility with a

surface more conducive to quicker sprint times (National Centre of Excellence, Vale

of Glamorgan). The gymnasium employed during this study was quite cool in

comparison and all PAP sessions were conducted at the same time early each

morning (sessions commencing at eight a.m).

The use of a smartphone application in MyJump on the iPad recording CMJ

performance provided the researchers with unique use of an emerging valid, reliable

and inexpensive technology-based measurement system that can inform current and

future research (Balsalobre-Fernandez et al., 2014).). Embracing new technologies

and opportunities breaks down barriers and opens doors for strength and

conditioning practitioners in terms of location of testing, and also gives learning

opportunities in terms of immediate, objective feedback to participants of sessions

which can be used as an extremely powerful tool (Hughes et. al, 2007; Franks and

Ngelkerke). Use of force platforms or jump mats is often restricted to elite coaching

environments and university laboratories (Balsalobre-Fernandez et al., 2014).). The

interface of the application is extremely easy to use (see fig. 3.5.). Flight time is

measured and calculations can be made that determine height achieved using the

following formula: height = flight time squared times 1.22625 (Bosco et al., 1983).

When testing took place on MyJump to establish validity and reliability of the

application in 2014, observers and video analysts had no prior experience in using

the application, which goes some way to demonstrating the useability of the

53

application (Balsalobre-Fernandez et al., 2014). Indeed, given the advancement of

new technology, smartphones and iPads will have more high powered cameras that

reduce the measurement errors of MyJump that already boasts impressive degrees

of reliability (r=0.997) and validity (r=0.995) when compared to a force platform

(Balsalobre-Fernandez et al., 2014). Developing technologies such as these will

soon be the norm for measurement of variables like CMJ height because of their

advanced nature, portability and affordability and may potentially revolutionise how

and by whom research is conducted (Bort-Roig et al., 2014). This means that both

athletes and coaches can monitor performance metrics in a valid and economic way

never before seen (Balsalobre-Fernandez et al., 2014).

Whilst use of smart applications for this study is an extremely appropriate and the

most current method to use for CMJ height there is a time delay in processing a CMJ

performance, which was estimated at approximately twenty seconds per attempt.

This significantly reduces as the observer becomes more familiar with the interface

of the application (Balsalobre-Fernandez et al., 2014)..

Although several significant data have been recorded during this study from all VCAs

versus control and when compared to each other, there are several limiting factors

which may have affected purported outcomes. Considering the small sample size in

this study, interpretation of these results should be performed with caution (Hoffman

et. al, 2009). Baseline data for participant mass was taken from WRU data from April

testing and no monitoring of diet or lifestyle took place during the course of this study

(WRU, 2015).

Based on prior research on biomechanical suitability it was expected that HBS VCA

would see significantly superior comparisons versus DJ VCA for CMJ performance

54

which was not realised in this case (Crewther et al., 2011). This may be down to the

standardised warm up being extremely efficient and effective in activation of muscles

required for sprint performance (where HBS VCA elicited superior levels of PAP

compared to other VCAs at both four and seven minutes post VCA) and less

effective for eliciting PAP for CMJ performance.

55

Conclusion

Use of smart applications provide strength and conditioners, coaches and athletes

unique learning opportunities and ever more valid and reliable methods are now

available for all to use. More research needs to be conducted to determine optimal

individual rest periods for DJ protocols and post VCA ten minutes and more following

plyometric VCAs (Byrne et al., 2014; Chen et al., 2013) which could theoretically

provide a more optimal environment for the elicitation of PAP. Investigations should

be conducted to determine whether significant levels of potentiation remain perhaps

up to thirty minutes post-VCA (Rixon et al., 2007; Seitz et al., 2014; Wilson et al.,

2013). Due to the individual nature of response to PAP stimuli, this outcome may not

be achieved. The strength and conditioning professional must also devise other

plyometric interventions so they can test and identify respondents to stimuli,

prescribing specific optimal loads (if any), rest periods between VCA and

performance activity so maximal potentiation can occur prior to a game and

performance can be positively affected (Seitz et al., 2014; Talpey et al., 2014; Tillin

and Bishop, 2009; Turner et al., 2014; Zemkova et al., 2014). Some elite youth rugby

players as a direct result of a well-planned pre-game PAP protocol will perform at a

higher level than previously due to enhanced potentiation (Batista et al., 2011).

56

References

Aagaard, P., Simonsen, E. B., Andersen, J. L., Magnusson, P. and Dyhre-Poulsen, P. (2002) “Increased rate of force development and neural drive of human skeletal muscle following resistance training” in Journal of Applied Physiology, Vol. 93, no.4 , pp. 1318-1326.

Aagaard, P., Simonsen, E. B., Andersen, J. L., Magnusson, P. and Dyhre-Poulsen, P. (2002) “Neural adaptation to resistance training: changes in evoked V-wave and H-reflex responses” in Journal of Applied Physiology, Vol. 92, no. 6, pp. 2309-2318.

Aagaard, P. (2003) “Training-induced changes in neural function” in Exercise and Sport Sciences Reviews Vol. 31, no. 2, pp. 61-67.

Adams, K., O'Shea, J. P., O'Shea, K. L. and Climstein, M. (1992) “The effect of six weeks of squat, plyometric and squat-plyometric training on power production” in Journal of Strength and Conditioning Research Vol. 6, no. 1, pp. 36-41.

Ajan, T and Baroga, L. (1988) Weightlifting. Budapest: International Weightlifting Federation/Medicina.

Arampatzis, A., Schade, F., Walsh, M. and Bruggeman, G.P. (2001) “Influence of leg stiffness and its effect on myodynamic jumping performance” in Journal of Electromyography and Kinesiology Vol. 11, pp. 355-364.

Argus, C. K., Gill, N. D. and Keogh, J. W. (2012) “Characterization of the differences in strength and power between different levels of competition in rugby union athletes” Journal of Strength and Conditioning Research Vol. 26, no. 10, pp. 2698-2704.

Baechle, T.R., Earle, R.W. and Wathen, D. (2008) “Resistance Training” in Baechle, T.R. and Earle, R.W. (eds.) Essentials of Strength Training and Conditioning 3rd ed. (2008) Champaign, IL: Human Kinetics.

57

Baker, D. (2001) “A series of studies on the training of high-intensity muscle power in rugby league football players” in Journal of Strength and Conditioning Research Vol. 15, no. 2, pp. 198-209.

Baker, D. G. (2013) “10-Year Changes in Upper Body Strength and Power in Elite Professional Rugby League Players—The Effect of Training Age, Stage, and Content” in Journal of Strength and Conditioning Research Vol. 27, no. 22, pp. 285-292.

Baker, D. (2003) “Acute effect of alternating heavy and light resistances on power output during upper-body complex power training” in Journal of Strength and Conditioning Research Vol. 17, no. 3, pp. 493-497.

Baker, D. and Nance, S. (1999) “The relation between running speed and measures of strength and power in professional rugby league players” in Journal of Strength and Conditioning Research, Vol. 13, no. 3, pp. 230-235.

Balsalobre-Fernández, C., Glaister, M. and Lockey, R. A. (2015) “The validity and reliability of an iPhone app for measuring vertical jump performance” in Journal of Sports Sciences accessed ahead of publication, pp. 1-6.

Balsalobre-Fernández, C., Tejero-González, C. M. and del Campo-Vecino, J. (2014) “Hormonal and neuromuscular responses to high-level middle-and long-distance competition” in InternationalJournal of Sports Physiology and Performance Vol. 9, no. 5, pp. 839-844.

Balsalobre-Fernández, C., Tejero-González, C. M., del Campo-Vecino, J. and Bavaresco, N. (2014) “The concurrent validity and reliability of a low-cost, high-speed camera-based method for measuring the flight time of vertical jumps” in Journal of Strength and Conditioning Research Vol. 28, no. 2, pp. 528-533.

Bangsbo, J., Mohr, M. and Krustrup, P. (2006) “Physical and metabolic demands of training and match-play in the elite football player” in Journal of Sports Sciences, Vol. 24, no. 7, pp. 665-674.

58

Batista, M. A. B., Ugrinowitsch, C., Roschel, H., Lotufo, R., Ricard, M. D. and Tricoli, V. (2007) “Intermittent exercise as a conditioning activity to induce postactivation potentiation” in Journal of Strength and Conditioning Research Vol. 21, pp. 837-840.

Bellar, D., Judge, L. W., Turk, M. and Judge, M. (2012) “Efficacy of potentiation of performance through overweight implement throws on male and female collegiate and elite weight throwers” in Journal of Strength and Conditioning Research Vol. 26, no. 6, pp. 1469-1474.

Bevan, H.R., Cunningham, D.J., Tooley, E.P., Owen, N.J., Cook, C.J. and Kilduff, L.P. (2010) “Influence of postactivation potentiation on sprinting performance in professional rugby players” in Journal of Strength and Conditioning Research Vol. 24, pp. 701-705.

Bishop, D. (2003) “Warm up II” in Sports Medicine Vol. 33, no. 7, pp. 483-498.

Bland, J. M. and Altman, D. (1986) “Statistical methods for assessing agreement between two methods of clinical measurement” in The Lancet Vol. 327, no. 8476, pp. 307-310.

Bogdanis, G. C., Tsoukos, A., Veligekas, P., Tsolakis, C. and Terzis, G. (2014) “Effects of Muscle Action Type with Equal impulse of Conditioning Activity on Post Activation Potentiation” in Journal of Strength and Conditioning Research/National Strength and Conditioning Association Vol. 28, no. 9, pp. 2521-2528

Bomfim Lima, J., Marin, D., Barquilha, G., Da Silva, L., Puggina, E., Pithon-Curi, T. and Hirabara, S. (2011) “Acute effects of drop jump potentiation protocol on sprint and countermovement vertical jump performance” in Human Movement Vol. 12, no. 4, pp. 324-330.

Bompa, T. (2000) Total Training for Young Champions Champaign, IL: Human Kinetics pp. 1-20.

59

Bort-Roig, J., Gilson, N. D., Puig-Ribera, A., Contreras, R. S. and Trost, S. G. (2014) “Measuring and influencing physical activity with smartphone technology: a systematic review” in Sports Medicine Vol. 44, no. 5, pp. 671-686.

Bosco, C., Luhtanen, P. and Komi, P. V. (1983) “A simple method for measurement of mechanical power in jumping” in European Journal of Applied Physiology and Occupational Physiology Vol. 50, no. 2, pp. 273-282.

Brechue, W. F., Mayhew, J. L. and Piper, F. C. (2010) “Characteristics of sprint performance in college football players” in Journal of Strength and Conditioning Research Vol. 24, no. 5, pp. 1169-1178.

Bullock, N. and Comfort, P. (2011) “An investigation into the acute effects of depth jumps on maximal strength performance” in Journal of Strength and Conditioning Research Vol. 25, no. 11, pp. 3137-3141.

Byrne, P.J., Kenny, J. and O’Rourke, B. (2014) “Acute potentiating effect on depth jumps on sprint performance” in Journal of Strength and Conditioning Research Vol. 28, pp. 610-615.

Byrne, P. J., Moran, K., Rankin, P. and Kinsella, S. (2010) “A comparison of methods used to identify 'optimal' drop height for early phase adaptations in depth jump training” in Journal of Strength and Conditioning Research Vol. 24, no. 8, pp. 2050-2055.

Carter, J. and Greenwood, M. (2014) “Complex Training Reexamined: Review and Recommendations to Improve Strength and Power” in Strength and Conditioning Journal Vol. 36, no. 2, pp. 11-19.

Casartelli, N., Müller, R. and Maffiuletti, N. A. (2010) “Validity and reliability of the myotest accelerometric system for the assessment of vertical jump height” in Journal of Strength and Conditioning Research Vol. 24, no. 11, pp. 3186-3193.

Caterisano, A., Moss, R. E., Pellinger, T. K., Woodruff, K., Lewis, V. C., Booth, W., and Khadra, T. (2002) “The effect of back squat depth on the EMG activity of 4 superficial hip and thigh muscles” in Journal of Strength and Conditioning Research Vol.16, no. 3, pp. 428-432.

60

Chandler, T. J. and Stone, M. H. (1992) “The squat exercise in athletic conditioning: A position statement and review of the literature” in Chiropractic Sports Medicine, Vol.6, pp. 105-105.

Chatzopoulos, D. E., Michailidis, C. J., Giannakos, A. K., Alexiou, K. C., Patikas, D. A., Antonopoulos, C. B. and Kotzamanidis, C. M. (2007) “Postactivation potentiation effects after heavy resistance exercise on running speed” in Journal of Strength and Conditioning Research Vol. 21, no. 4, pp. 1278-1281.

Chen, Z. R., Wang, Y. H., Peng, H. T., Yu, C. F., and Wang, M. H. (2013) “The Acute Effect of Drop Jump Protocols With Different Volumes and Recovery Time on Countermovement Jump Performance” in Journal of Strength and Conditioning Research Vol. 27, no.1, pp. 154-158.

Chiu, L. Z. and Barnes, J. L. (2003) “The fitness-fatigue model revisited: Implications for planning short-and long-term training” in Strength and Conditioning Journal Vol. 25, no.6, pp. 42-51.

Chiu, L. Z. F., Fry, C., Weiss, L. W., Schilling, B.K., Brown, L.E. and Smith, S.L. (2003) “Postactivation potentiation response in athletic and recreationally trained individuals” in Journal of Strength and Conditioning Research Vol. 17, pp. 671-677.

Chu, D. A. (1996) Explosive power and strength: complex training for maximum results Champaign, IL: Human Kinetics.

Clark, D. R., Lambert, M. I. and Hunter, A. M. (2012) “Muscle activation in the loaded free barbell squat: A brief review” in Journal of Strength and Conditioning Research Vol. 26, no. 4, pp. 1169-1178.

Comfort, P., Haigh, A. and Matthews, M. J. (2012) “Are changes in maximal squat strength during preseason training reflected in changes in sprint performance in rugby league players?” in Journal of Strength and Conditioning Research, Vol. 26, no. 3, pp. 772-776.

61

Comfort, P. and Kasim, P. (2007) “Optimising squat technique” in Strength and Conditioning Journal Vol. 29 p. 13.

Comyns, T. M., Harrison, A. J. and Hennessy, L. K. (2010) “Effect of squatting on sprinting performance and repeated exposure to complex training in male rugby players” in Journal of Strength and Conditioning Research Vol. 24, no. 3, pp. 610-618.

Costa, P. B., Medeiros, H. B. and Fukuda, D. H. (2011) “Warm-up, stretching, and cool-down strategies for combat sports” in Strength and Conditioning Journal Vol. 33, no. 6, pp. 71-79.

Cowell, J. F., Cronin, J. and Brughelli, M. (2012) “Eccentric muscle actions and how the strength and conditioning specialist might use them for a variety of purposes” in Strength and Conditioning Journal, Vol. 34, no. 3, pp. 33-48.

Crewther, B. T., Kilduff, L. P., Cook, C. J., Middleton, M. K., Bunce, P. J. and Yang, G. Z. (2011) “The acute potentiating effects of back squats on athlete performance” in Journal of Strength and Conditioning Research Vol. 25, no. 12, pp. 3319-3325.

Cunniffe, B., Proctor, W., Baker, J. S. and Davies, B. (2009) “An evaluation of the physiological demands of elite rugby union using global positioning system tracking software” in Journal of Strength and Conditioning Research Vol. 23, no. 4, pp. 1195-1203.

Dellaserra, C. L., Gao, Y. and Ransdell, L. (2014) “Use of Integrated Technology in Team Sports: A Review of Opportunities, Challenges, and Future Directions for Athletes” in Journal of Strength and Conditioning Research Vol. 28, no. 2, pp. 556-573.

DeRenne, C. (2010) “Effects of postactivation potentiation warm-up in male and female sport performances: A brief review” in Strength and Conditioning Journal, Vol32, no. 6, pp. 58-64.

62

De Villarreal, E. S. S., González-Badillo, J. J. and Izquierdo, M. (2007) “Optimal warm-up stimuli of muscle activation to enhance short and long-term acute jumping performance” in European Journal of Applied Physiology Vol. 100, no. 4, pp. 393-401.

Docherty, D., Robbins, D. and Hodgson, M. (2004) “Complex training revisited: A review of its current status as a viable training approach” in Strength and Conditioning Journal Vol. 26, no. 6, pp. 52-57.

Duncan, M. J., Lyons, M. and Nevill, A. M. (2008) “Evaluation of peak power prediction equations in male basketball players” in Journal of Strength and Conditioning Research Vol. 22, no. 4, pp. 1379-1381.

Duthie, G. M. (2006) “A framework for the physical development of elite rugby union players” in International Journal of Sports Physiology and Performance Vol.1, no. 1 p. 2.

Duthie, G. M., Pyne, D. B., Ross, A. A., Livingstone, S. G. and Hooper, S. L. (2006) “The reliability of ten-meter sprint time using different starting techniques” in Journal of Strength and Conditioning Research Vol. 20, no. 2, p. 251.

Duthie, G. M., Young, W. B. and Aitken, D. A. (2002) “The acute effects of heavy loads on jump squat performance: An evaluation of the complex and contrast methods of power development” in Journal of Strength and Conditioning Research Vol. 16, no. 4, pp. 530-538.

Ebben, W. P., Jensen, R. L. and Blackard, D. O. (2000) “Electromyographic and kinetic analysis of complex training variables” in Journal of Strength and Conditioning Research Vol. 14, no.4, pp. 451-456.

Esformes, J. I. and Bampouras, T. M. (2013) “Effect of back squat depth on lower-body postactivation potentiation” in Journal of Strength and Conditioning Research Vol. 27, no. 11, pp. 2997-3000.

63

Esformes, J.I., Keenan, M., Moody, J. and Bampouras,T.M. (2011) “Effect of different types of conditioning contraction on upper body postactivation potentiation” in Journal of Strength and Conditioning Research Vol. 25, pp. 143-148.

Evans, S. (1998a) “Notation analysis with the national squads-The Badminton Association of England Limited” in Coaches Bulletin Vol. 105, pp. 7-8.

Evans, S. (1998b) “Winners and errors-The Badminton Association of England Limited” in Coaches Bulletin “Courtside” Vol. 108, pp. 8-9.

Evetovich, T. K., Conley, D. S. and McCawley, P. F. (2014) “Post-Activation Potentiation Enhances Upper and Lower Body Athletic Performance in Collegiate Men and Women Athletes” in Journal of Strength and Conditioning Research/National Strength and Conditioning Association.

Flanagan, E. P. and Comyns, T. M. (2008) “The use of contact time and the reactive strength index to optimize fast stretch-shortening cycle training” in Strength and Conditioning Journal Vol. 30, no. 5, pp. 32-38.

Franks, I.M., and Nagelkerke, P. (1988) "The use of computer interactive video in sport analysis" in Ergonomics Vol. 31, no. 11, pp. 1593-1603.

Fry, A. C., Ciroslan, D., Fry, M. D., LeRoux, C. D., Schilling, B. K. and Chiu, L. Z. (2006) “Anthropometric and performance variables discriminating elite American junior men weightlifters” in Journal of Strength and Conditioning Research Vol. 20, no. 4, pp. 861-866.

Fukutani, A., Takei, S., Hirata, K., Miyamoto, N., Kanehisa, H. and Kawakami, Y. (2014) “Influence of the intensity of squat exercises on the subsequent jump performance” in Journal of Strength and Conditioning Research Vol. 28, no. 8, pp. 2236-2243.

Gabbett, T., Georgieff, B. and Domrow, N. (2007) “The use of physiological, anthropometric, and skill data to predict selection in a talent-identified junior volleyball squad” in Journal of Sports Sciences Vol. 25, no. 12, pp. 1337-1344.

64

Gamble, P. (2008) “Approaching physical preparation for youth team-sports players” in Strength and Conditioning Journal Vol. 30, no. 1, pp. 29-42.

Gilbert, G. and Lees, A. (2005) “Changes in the force development characteristics of muscle following repeated maximum force and power exercise” in Ergonomics Vol. 48, no’s 11-14, pp. 1576-1584.

Glatthorn, J. F., Gouge, S., Nussbaumer, S., Stauffacher, S., Impellizzeri, F. M. and Maffiuletti, N. A. (2011) “Validity and reliability of Optojump photoelectric cells for estimating vertical jump height” in Journal of Strength and Conditioning Research Vol. 25, no. 2, pp. 556-560.

Gossen, E. R. and Sale, D. G. (2000) “Effect of postactivation potentiation on dynamic knee extension performance” in European Journal of Applied Physiology Vol. 83, no. 6, pp. 524-530.

Gourgoulis, V., Aggeloussis, N., Kasimatis, P., Mavromatis, G. and Garas, A. (2003) “Effect of a submaximal half-squats warm-up program on vertical jumping ability” in Journal of Strength and Conditioning Research Vol. 17, no. 2, pp. 342-344.

Gullich, A. and Schmidtbleicher, D. (1996) “MVC induced short-term potentiation of explosive force” in New Studies in Athletics Vol. 11, no.4, pp. 67-81.

Gullett, J. C., Tillman, M. D., Gutierrez, G. M. and Chow, J. W. (2009) “A biomechanical comparison of back and front squats in healthy trained individuals” in Journal of Strength and Conditioning Research Vol. 23, no. 1, pp. 284-292.

Haekkinen, K. and Komi, P. V. (1985) “Effect of explosive type strength training on electromyographic and force production characteristics of leg extensor muscles during concentric and various stretch-shortening cycle exercises” in Scandinavian Journal of Sports Science Vol. 7, no. 2, pp. 65-76.

Hamada, T., Sale, D. G., MacDougall, J. D. and Tarnopolsky, M. A. (2000) “Postactivation potentiation, fiber type, and twitch contraction time in human knee extensor muscles” in Journal of Applied Physiology Vol. 88, no.6, pp. 2131-2137.

65

Harman, E. (2008) “Principles of Test Selection and Administration” in Baechle, T.R. and Earle, R.W. (eds.) Essentials of Strength Training and Conditioning 3rd ed. (2008) Champaign, IL: Human Kinetics.

Harman, E. and Garhammer, J. (2008) “Administration, Scoring, and interpretation of selected tests” in Baechle, T.R. and Earle, R.W. (eds.) Essentials of Strength Training and Conditioning 3rd ed. (2008) Champaign, IL: Human Kinetics.

Hartwig, T. B., Naughton, G. and Searl, J. (2011) “Motion analyses of adolescent rugby union players: a comparison of training and game demands” in Journal of Strength and Conditioning Research Vol. 25, no. 4, pp. 966-972.

Hilfiker, R., Huebner, K., Lorenz, T. and Marti, B. (2007) “Effects of drop jumps added to the warm-up of elite sport athletes with a high capacity for explosive force development” in Journal of Strength and Conditioning Research Vol. 21, no.2, pp. 550-555.

Hodgson, M., Docherty, D. and Robbins, D. (2005) “Post-activation potentiation: Underlying physiology and implication for motor performance” in Sports Medicine Vol. 35, no. 7, pp. 585-595.

Hodgson, M. J., Docherty, D. and Zehr, E. P. (2008) “Postactivation potentiation of force is independent of h-reflex excitability” in International Journal of Sports Physiology and Performance Vol. 3, no. 2, p. 219.

Hoffman, J. R., Ratamess, N.A., Klatt, M., Faigenbaum, A.D., Ross, R.E., Tranchina, N.M., McCurley, R.C., Kang, J. and Kraemer, W.J. (2009) "Comparison between different off-season resistance training programs in Division III American college football players" in Journal of Strength and Conditioning Research  Vol. 23, no. 1, pp/ 11-19.

Hughes, M.D. (1998) “The application of notational analysis to racket sports” in Lees, A., Maynard, I., Hughes, M.D. and Reilly, T. (eds.)Science and Racket Sports II pp. 211-220. London: E & FN Spon.

66

Hughes, M., Hughes, M.T. and Behan, H. (2007) "The evolution of computerised notational analysis through the example of racket sports" in International Journal of Sports Science and Engineering Vol. 1, no. 1, pp. 3-28.

Ishikawa, M. and Komi, P. V. (2004) “Effects of different dropping intensities on fascicle and tendinous tissue behavior during stretch-shortening cycle exercise” in Journal of Applied Physiology Vol. 96, no.3, pp. 848-852.

Jeffreys, I. (2007). Total Soccer Fitness Coaches Choice: Monterey, California

Jensen, R. L. and Ebben, W. P. (2003) “Kinetic analysis of complex training rest interval effect on vertical jump performance” in Journal of Strength and Conditioning Research, Vol. 17, no.2, pp. 345-349.

Jo, E., Judelson, D. A., Brown, L. E., Coburn, J. W. and Dabbs, N. C. (2010) “Influence of recovery duration after a potentiating stimulus on muscular power in recreationally trained individuals” in Journal of Strength and Conditioning Research, Vol. 24, no. 2, pp. 343-347.

Jones, P. and Lees, A. (2003) “A biomechanical analysis of the acute effects of complex training using lower limb exercises” in Journal of Strength and Conditioning Research Vol. 17, pp. 694-700.

Keiner, M., Sander, A., Wirth, K., Caruso, O., Immesberger, P. and Zawieja, M. (2013) “Strength Performance in Youth: Trainability of adolescents and children in the back and front squats” in Journal of Strength and Conditioning Research Vol. 27, no. 2, pp. 357-362.

Kilduff, L. P., Bevan, H. R., Kingsley, M. I., Owen, N. J., Bennett, M. A., Bunce, P. J. Hore, A.M., Maw, J.R. and Cunningham, D. J.(2007) “Postactivation potentiation in professional rugby players: optimal recovery” in Journal of Strength and Conditioning Research, Vol. 21, no. 4, pp. 1134-1138.

67

Kilduff, L. P., Owen, N., Bevan, H., Bennett, M., Kingsley, M. I. and Cunningham, D. (2008) “Influence of recovery time on post-activation potentiation in professional rugby players” in Journal of Sports Sciences, Vol. 26, no. 8, pp. 795-802.

Komi, P. V. (2008) “Stretch-shortening cycle” in Strength and power in Sport Vol. 2, Blackwell Scientific: Oxford, UK. pp. 184-202.

Lim, J. J. and Kong, P. W. (2013) “Effects of isometric and dynamic postactivation potentiation protocols on maximal sprint performance” in Journal of Strength and Conditioning Research Vol. 27, no. 10, pp. 2730-2736.

Linder, E. E., Prins, J. H., Murata, N. M., Derenne, C., Morgan, C. F. and Solomon, J. R. (2010) “Effects of preload 4 repetition maximum on 100-m sprint times in collegiate women” in Journal of Strength and Conditioning Research Vol. 24, no.5. pp. 1184-1190.

Lombard, W.P, Durandt, J.J., Masimla, H., Green, M. and Lambert, M. I. (2014) “Changes in body size and physical characteristics of South African u20 rugby union players over a 13 year period” in Journal of Strength and Conditioning Research (accessed ahead of publication).

Lowery, R. P., Duncan, N. M., Loenneke, J. P., Sikorski, E. M., Naimo, M. A., Brown, L. E., Wilson, F.G. and Wilson, J. M. (2012) “The effects of potentiating stimuli intensity under varying rest periods on vertical jump performance and power “ in Journal of Strength and Conditioning Research Vol. 26, no. 12, pp. 3320-3325.

Macintosh, B. R. and Rassier, D. E. (2002) “What is fatigue?” in Canadian Journal of Applied Physiology, Vol. 27, no. 1, pp. 42-55.

Mangus, B. C., Takahashi, M., Mercer, J. A., Holcomb, W. R., McWhorter, J. W. and Sanchez, R. (2006) “Investigation of vertical jump performance after completing heavy squat exercises” in Journal of Strength and Conditioning Research, Vol. 20, no. 3, pp. 597-600.

68

Markovic, G., Jukic, I., Milanovic, D., and Metikos, D. (2007) “Effects of sprint and plyometric training on muscle function and athletic performance” in Journal of Strength and Conditioning Research Vol. 21, no. 2, pp. 543-549.

Massamoto, N., Larson, R., Gates, T. and Faigenbaum, A. (2003) “Acute effects of plyometric exercise on maximum squat performance in male athletes” in Journal of Strength and Conditioning Research Vol. 17, no. 1, pp. 68-71.

McBride, J. M., Nimphius, S. and Erickson, T. M. (2005) “The acute effects of heavy-load squats and loaded countermovement jumps on sprint performance” in Journal of Strength and Conditioning Research Vol. 19, no.4, pp. 893-897.

McCann, M. R. and Flanagan, S. P. (2010) “The effects of exercise selection and rest interval on postactivation potentiation of vertical jump performance” in Journal of Strength and Conditioning Research Vol. 24, no. 5, pp. 1285-1291.

Meir, R. (2012) “Training for and competing in sevens rugby: practical considerations from experience in the International Rugby Board World Series” in Strength and Conditioning Journal Vol. 34, no. 4, pp. 76-86.

Mero, A., Komi, P. V. and Gregor, R. J. (1992) “Biomechanics of sprint running” in Sports Medicine Vol. 13, no. 6, pp. 376-392.

Meylan, C., McMaster, T., Cronin, J., Mohammad, N. I. and Rogers, C. (2009) “Single-leg lateral, horizontal, and vertical jump assessment: reliability, interrelationships, and ability to predict sprint and change-of-direction performance” in Journal of Strength and Conditioning Research, Vol. 23, no. 4, pp. 1140-1147.

Mina, M. A., Blazevich, A.J., Giakas, G. and Kay, A.D. (2014) “Influence of variable resistance loading on subsequent free weight maximal back squat performance” in Journal of Strength and Conditioning Research Vol. 28, no. 10, pp. 2988-2995.

69

Moir, G. L., Mergy, D., Witmer, C. A. and Davis, S. E. (2011) “The acute effects of manipulating volume and load of back squats on countermovement vertical jump performance” in Journal of Strength and Conditioning Research, Vol. 25, no. 6, pp. 1486-1491.

Mola, J. N., Bruce-Low, S. S. and Burnet, S. J. (2014) “Optimal Recovery Time for Postactivation Potentiation in Professional Soccer Players” in Journal of Strength and Conditioning Research Vol. 28, no. 6, pp. 1529-1537.

Morana, C. and Perrey, S. (2009) “Time course of postactivation potentiation during intermittent submaximal fatiguing contractions in endurance-and power-trained athletes” in Journal of Strength and Conditioning Research Vol. 23, no. 5, pp. 1456-1464.

Murray, O.T. and Olcese, N.R. (2011) "Teaching and learning with iPads, ready or not?" in TechTrends 55, no. 6, pp. 42-48.

NSCA Position Stance (1993) “Explosive/Plyometric exercise” in National Strength and Conditioning Association Journal Vol. 15, no. 3, p. 16.

Okuno, N. M., Tricoli, V., Silva, S. B., Bertuzzi, R., Moreira, A. and Kiss, M. A. (2013) “Postactivation Potentiation on Repeated-Sprint Ability in Elite Handball Players” in Journal of Strength and Conditioning Research, Vol. 27, no. 3, pp. 662-668.

Oliver, J. L., Lloyd, R. S. and Meyers, R. W. (2011) “Training elite child athletes: Promoting welfare and well-being” in Strength and Conditioning Journal Vol. 33, no. 4, pp. 73-79.

Parry, S., Hancock, S., Shiells, M., Passfield, L., Davies, B. and Baker, J. S. (2008) “Physiological effects of two different postactivation potentiation training loads on power profiles generated during high intensity cycle ergometer exercise” in Research in Sports Medicine, Vol. 16, no. 1, pp. 56-67.

70

Peng, H. T. (2011) “Changes in biomechanical properties during drop jumps of incremental height” in Journal of Strength and Conditioning Research Vol. 25, no.9, pp. 2510-2518.

Rassier, D. E. and Macintosh, B. R. (2000) “Coexistence of potentiation and fatigue in skeletal muscle” in Brazilian Journal of Medical and Biological Research Vol. 33, no.5, pp. 499-508.

Reilly, T. and Thomas, V. (1976) "A motion analysis of work-rate in different positional roles in professional football match-play" in Journal of Human Movement Studies Vol. 2, no. 2, pp. 87-97.

Requena, B., de Villarreal, E. S. S., Gapeyeva, H., Ereline, J., García, I. and Pääsuke, M. (2011) “Relationship between postactivation potentiation of knee extensor muscles, sprinting and vertical jumping performance in professional soccer players” in Journal of Strength and Conditioning Research, Vol. 25, no. 2, pp. 367-373.

Requena, B., García, I., Requena, F., de Villarreal, E. S. S. and Pääsuke, M. (2012) “Reliability and validity of a wireless microelectromechanicals based system (Keimove™) for measuring vertical jumping performance” in Journal of Sports Science and Medicine Vol. 11, no. 1, p.115.

Rhea, M. R., Peterson, M. D., Oliverson, J. R., Ayllón, F. N. and Potenziano, B. J. (2008) “An examination of training on the VertiMax resisted jumping device for improvements in lower body power in highly trained college athletes” in Journal of Strength and Conditioning Research Vol. 22, no. 3, pp. 735-740.

Rimmer, E. and Sleivert, G. (2000) “Effects of a plyometrics intervention program on sprint performance” in Journal of Strength and Conditioning Research Vol. 14, no. 3, pp. 295-301.

Rixon, K. P., Lamont, H. S. and Bemben, M. G. (2007) “Influence of type of muscle contraction, gender, and lifting experience on postactivation potentiation performance” in Journal of Strength and Conditioning Research Vol. 21, no. 2, pp. 500-505.

71

Ruben, R. M., Molinari, M. A., Bibbee, C. A., Childress, M. A., Harman, M. S., Reed, K. P. and Haff, G. G. (2010) “The acute effects of an ascending squat protocol on performance during horizontal plyometric jumps” in Journal of Strength and Conditioning Research, Vol. 24, no.2, pp. 358-369.

Sale, D. G. (1988) “Neural adaptation to resistance training” in Medicine and Science in Sports and Exercise Vol. 20, no. 5, pp. 135-45.

Sale, D. G. (2002) “Postactivation potentiation: role in human performance” in Exercise and Sport Sciences Reviews, Vol. 30, no. 3, pp. 138-143.

Sale, D.G. (2004) “Postactivation potentiation: Role in human performance” in British Journal of Sports Medicine Vol. 38, pp. 386–387.

Saltin, B. (1969) “Physiological effects of physical conditioning” in Medicine and Science in Sports and Exercise Vol. 1, no. 1, pp. 50-56.

Sayers, S. P., Harackiewicz, D. V., Harman, E. A., Frykman, P. and Rosenstein, M. T. (1999) “Cross-validation of three jump power equations” in Medicine and Science in Sports and Exercise Vol. 31, no. 4, pp. 572-577.

Scott, S. L. and Docherty, D. (2004) “Acute effects of heavy preloading on vertical and horizontal jump performance” in Journal of Strength and Conditioning Research Vol. 18, no. 2, pp. 201-205.

Sedeaud, A., Marc, A., Schipman, J., Schaal, K., Danial, M., Guillaume, M., Berthelot, G. and Toussaint, J. F. (2014) “Secular trend: morphology and performance” in Journal of Sports Sciences, Vol. 32, no.12, pp. 1146-1154.

Seitz, L. B., de Villarreal, E. S. and Haff, G. G. (2014) “The Temporal Profile of Postactivation Potentiation Is Related to Strength Level” in Journal of Strength and Conditioning Research, Vol. 28, no. 3, pp. 706-715.

72

Smith, J. C. and Fry, A. C. (2007) “Effects of a ten-second maximum voluntary contraction on regulatory myosin light-chain phosphorylation and dynamic performance measures” in Journal of Strength & Conditioning Research Vol. 21, no. 1, pp. 73-76.

Smith, J. C., Fry, A. C., Weiss, L. W., Li, Y. and Kinzey, S. J. (2001) “The effects of high-intensity exercise on a 10-second sprint cycle test” in Journal of Strength and Conditioning Research Vol. 15, no. 3, pp. 344-348.

Stieg, J. L., Faulkinbury, K. J., Tran, T. T., Brown, L. E., Coburn, J. W. and Judelson, D. A. (2011) “Acute effects of depth jump volume on vertical jump performance in collegiate women soccer players” in Kineziologija, Vol. 43, no. 1, pp. 25-30.

Stone, M. H., Sands, W. A., Pierce, K. C., Ramsey, M. W. and Haff, G. G. (2008) “Power and power potentiation among strength-power athletes: preliminary study” in International Journal of Sports Physiology and Performance Vol. 3, no. 1, pp. 55.

Sweeney, H. L., Bowman, B. F. and Stull, J. T. (1993) “Myosin light chain phosphorylation in vertebrate striated muscle: regulation and function” in American Journal of Physiology Vol. 264, C1085-C1085.

Talpey, S. W., Young, W. B. and Saunders, N. (2014) “The Acute Effects of Conventional, Complex, and Contrast Protocols on Lower-Body Power” in Journal of Strength & Conditioning Research, Vol. 28, no. 2, pp. 361-366.

Terzis, G., Spengos, K., Karampatsos, G., Manta, P. and Georgiadis, G. (2009) “Acute effect of drop jumping on throwing performance” in Journal of Strength and Conditioning Research, Vol. 23, no. 9, pp. 2592-2597.

Till, K. A. and Cooke, C. (2009) “The effects of postactivation potentiation on sprint and jump performance of male academy soccer players” in Journal of Strength and Conditioning Research Vol. 23, no.7, pp. 1960-1967.

73

Tillin, M. N. A. and Bishop, D. (2009) “Factors modulating post-activation potentiation and its effect on performance of subsequent explosive activities” in Sports Medicine, Vol. 39, no. 2, pp. 147-166.

Tobin, D.P. and Delahunt, E. (2014) “The acute effect of a plyometric stimulus on jump performance in professional rugby players” in Journal of Strength and Conditioning Research Vol. 28, no. 2, pp. 367-372.

Turner, A. (2011) “The science and practice of periodization: a brief review” in Strength and Conditioning Journal Vol. 33, no. 1, pp. 34-46.

Turner, A.P., Bellhouse, S., Kilduff, L.P. and Russell, M. (2014) “Postactivation potentiation of sprint performance using plyometric exercise” in Journal of Strength and Conditioning Research (accessed ahead of publication).

Turner, A. N., and Jeffreys, I. (2010) “The stretch-shortening cycle: Proposed mechanisms and methods for enhancement” in Strength and Conditioning Journal Vol. 32, no. 4, pp. 87-99.

Verkhoshansky, Y. (1986) “Speed-strength preparation and development of strength endurance of athletes in various specializations” in Soviet Sports Review Vol. 21, no. 3, pp. 120-124.

Verkhoshansky, Y. V. (1996) “Quickness and velocity in sports movements” in New Studies in Athletics Vol. 11, pp. 29-38.

Voigt, M., Bojsen-Møller, F. Simonsen, E. B., and Dyhre-Poulsen, P. (1995) “The influence of tendon Youngs modulus, dimensions and instantaneous moment arms on the efficiency of human movement” in Journal of Biomechanics Vol. 28, no. 3, pp. 281-291.

Wadley, G. and Le Rossignol, P. (1998) “The relationship between repeated sprint ability and the aerobic and anaerobic energy systems” in Journal of Science and Medicine in Sport Vol. 1, no. 2, pp. 100-110.

74

Waller, M., Gersick, M., Holman, D. (2013) “Various Jump Training Styles for Improvement of Vertical Jump Performance” in Strength and Conditioning Journal Vol. 35, no. 1, pp. 82-89.

Walsh, M., Arampatzis, A., Schade, F. and Bruggemann, G. P. (2004) “The effect of drop jump starting height and contact time on power, work performed, and moment of force” in Journal of Strength and Conditioning Research Vol. 18, no. 3, pp. 561-566.

Weber, K. R., Brown, L. E., Coburn, J. W. and Zinder, S. M. (2008) “Acute effects of heavy-load squats on consecutive squat jump performance” in Journal of Strength and Conditioning Research Vol. 22, no. 3, pp. 726-730.

Wilson, J. M., Duncan, N. M., Marin, P. J., Brown, L.E., Loenneke, J. P., Wilson, S., Jo, E., Lowery, R.P. and Ugrinowitsch, C. (2013) "Meta-analysis of postactivation potentiation and power: effects of conditioning activity, volume, gender, rest periods, and training status." in Journal of Strength and Conditioning Research Vol. 27, no. 3, pp. 854-859.

Woods, K., Bishop, P. and Jones, E. (2007) “Warm-up and stretching in the prevention of muscular injury” in Sports Medicine Vol. 37, no. 12, pp. 1089-1099.

Yetter, M. and Moir, G. L. (2008) “The acute effects of heavy back and front squats on speed during forty-meter sprint trials” in Journal of Strength and Conditioning Research, Vol. 22, no. 1, pp. 159-165.

WRU (2015) Unpublished data.

Young, W. B. (2006) “Transfer of strength and power training to sports performance” in International Journal of Sports Physiology and Performance Vol. 1, no. 2, p. 74.

Young, W. B., Jenner, A. and Griffiths, K. (1998) “Acute enhancement of power performance from heavy load squats” in Journal of Strength and Conditioning Research, Vol. 12, no. 2, pp. 82-84.

75

Young, W., Russell, A., Burge, P., Clarke, A., Cormack, S. and Stewart, G. (2008) “The use of sprint tests for assessment of speed qualities of elite Australian rules footballers” in International Journal of Sports Physiology and Performance Vol. 3, no. 2, p.201.

Yule, S. (2005) “The back squat” in Professional Strength and Conditioning Vol. 1, pp. 11-15.

Zatsiorsky, V. M. and Kraemer, W. J. (2006) Science and Practice of Strength Training Human Kinetics: Champaign, IL pp. 89-108.

Zemková, E., Jelen, M. N., KováCiková, Z. C., Ollé, G., Vilman, T. S., and Hamar, D. S. (2014) “Enhancement of Peak and Mean Power in Concentric Phase of Resistance Exercises” in Journal of Strength and Conditioning Research Vol. 28, no. 10, pp. 2919-2926.

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Appendix i

Risk Assesment

Coleg Sir Gâr Risk Assessment Form

Boy’s gym and Weights Gym

Member of Staff Undertaken the Risk Assessment Sam Collins Date Undertaken November 2014

Hazard Person(s) at Risk

Existing Controls

Risk Rating (see matrix) High Med Low

Any additional control measures required.

Ceiling mounted bars which hold climbing ropes, rope ladder and gymnastic rings

Benches & all other gym equipment

Heavy Back Squat

Heavy Front Squat

Depth Jump

ParticipantsStaffCoach

ParticipantsStaffCoach

ParticipantsstaffCoach

ParticipantsstaffCoach

ParticipantsstaffCoach

All climbing equipment to be checked and securely fixed away

All equipment not in use will be stored in an appropriate spot.

All equipment not in use will be stored in an appropriate spot.

All equipment not in use will be stored in an appropriate spot.

All equipment not in use will be stored in an appropriate spot.

Likelihood Severity Risk Score

1

3

3

3

3

1

3

3

3

3

1

3

3

3

3

Low

Likely

Likely

Likely

Likely

Ensure spotters are used and familiarization sessions have taken place on technique

Ensure spotters are used and familiarization sessions have taken place on technique

Ensure spotters are used and familiarization sessions have taken place on technique

Ensure correct technique is employed and only one participant using at once.

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Equipment

Clutter / waste / surfaces

Participants

Activities

First aider

ParticipantsstaffCoach

ParticipantsStaffCoach

Participants

Participants

Participants

Equipment being used will be checked & collected before activity

All surfaces to be checked before activity takes placeClear area to be marked out with no obstructionsArea to be tidied after activity takes place

All participants are aware of activity taking place & are happy to take part & are wearing appropriate clothing for activity. All participants will sign form before hand

All activities are suitable for the participants & for the area that participants are working in

Know who & where the first aider can be contacted on that campus if staff is not afirst aider

2

3

2

2

3

2

2

2

2

3

2

2

2

2

3

Unlikely

Unlikely

Unlikely

Unlikely

Likely

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Person completing risk assessment Subject Specialist (where applicable)

Curriculum Area Head / Non Teaching College Manager

Name: Sam Collins Name: Name:

Signature: Signature: Signature:

Date: November 2014 Date: Date:

Review date: November 2015 Review date: Review date

Recommendations for additional control measures identified

Are additional control measures to be implemented? (Y/N).If they are not to be implemented please state reasons below.

Additional control measures to be implemented. Action to be Taken and by whom?

Date Action completed.

Signature

Risk Rating Score MethodThis example is known as a 5 x 5 matrix where values are given to the likelihood and the severity of each hazard identified. These are then multiplied to determine the hazard’s overall risk rating, whilst taking into account the controls in place.

LIKELIHOOD SCORE SEVERITY OF INJURY RISKVery unlikely (difficult to see how this can occur)

1 Very minor Injury (no treatment or absence from work)

1

Unlikely (not expected to happen, but can’t be ruled out)

2 Minor Injury (requiring first aid treatment / possible absence of up to 3 days)

2

Likely (could happen at some stage) 3 Injury (requiring over 3 days absence) 3

Very likely (may occur on a regular basis) 4 Severe Injury (Major injury as described under RIDDOR / long term absence / health effect)

4

Certain (no doubt about it) 5 Fatality 5

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Level of Risk IdentifiedPlease refer to the matrix below in determining whether or not the existing controls implemented are classified as High, Medium or Low.Please note that any hazards described as High must be reduced.

Likelihood of injury

Severity

Appendix ii

Informed Consent Form

Coleg Sir Gar Rugby Academy

Informed Consent for Participation in Dissertation Project

80

1 1 2 3 4 5

2 2 4 6 8 10

3 3 6 9 12 15

4 4 8 12 16 20

5 5 10 15 20 25

1 2 3 4 5

High

Medium

Low

1. Purpose and explanation of the project.

The aim of the study is to show the effects of post activation potentiation (PAP) on performance in elite youth rugby players. Potentiation is the enhancement of muscular force and/or power using a voluntary conditioning activity (VCA). This study will involve participants performing a number of VCAs (back squat, front squat and depth jumps) before performance is measured on countermovement jump (CMJ) and 10m sprint speed for each VCA. This will involve a number of sessions (6-8) lasting under 30 minutes each as part of the regular morning Academy session. The study hopes to find that by performing these actions, participant’s performance will be increased significantly. The application of PAP in team sports and specifically warm up protocols has great implications in team sports especially if performance can be enhanced just prior to kick off.

2. Risk and Discomfort

The test procedure(s) involve a standardized warm-up, performing a VCA and then being measured on (CMJ) and 10m sprint. As with any form of exercise, there exists the possibility of certain physiological changes occurring during the test procedure. Every effort will be made to minimize these risks by evaluation of preliminary information related to the health and fitness of the proposed participant using a physical activity readiness questionnaire (PARQ) which is attached to this form. Based on a proposed/potential participant’s current health status and level of risk for the test procedures the researcher reserves the right to not conduct the procedures on proposed/potential participant.

3. Responsibility of the participant

Information that you possess about your health or previous experience of exercise-related or heart related symptoms may affect the safety of the test procedure. Your prompt reporting of these and any other unusual feelings with effort during the tests is of great importance. You are also expected to report to the testing staff all medications (including non-prescription) taken recently.

4. Benefits to be expected from study

The results obtained from the study will be used to calculate specific response to PAP and which method works for each individual. This will then allow more specific exercises relevant to the individual to be worked into training programmes and therefore maximize transfer to performance

5. Enquiries

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Any questions about the procedures used in the study or the results of your particular results are encouraged. If you have any concerns or questions, please ask the researcher for further explanations

6. Use of Test Results

All personal information collected for this test will be treated as confidential and privileged. It is not to be released to anyone, but may be used by the Academy staff to tailor your training programme. All information and data may be used for statistical analysis or scientific purposes with your right of privacy maintained.

7. Voluntary Consent

I hereby consent to engage in the study which will investigate which VCA is most appropriate for me in eliciting PAP. My permission to take part in this process is given voluntarily. I understand that I may stop the test at any point, if I so desire.

I have read this form and the appendices and understand the test procedures that I will perform and the attendant risks and discomforts. Knowing these risks and discomforts, and having an opportunity to ask questions that have been answered to my satisfaction, I consent to participate in the study.

…………………………………………………………………………… …………………

Signature of Participant Date

…………………………………………………………………………… …………………..

Signature of Parent/Guardian (if under 18) Date

Appendix iv

College Player Registration Form

THE WELSH RUGBY UNION LIMITED

COLLEGE PLAYER REGISTRATION FORM

REGISTRATION OF A PLAYER

82

PLEASE PRINT CLEARLY and COMPLETE ALL DETAILS :

NAME : ...............................................................................................................................*

ADDRESS : ...........................................................................................................................*

............................................................................................................................................

...........................................................................................................................................

POST CODE : ........................................................................................................................*

DATE OF BIRTH : ....................................................................................*

PLAYER SIGNATURE :

............................................................................................................................................*

PARENT SIGNATURE :

..............................................................................................................................*

COLLEGE SIGNATURE : ..........................................................................................*

DATE : ........................................................................

COLLEGE : .............................................................................................................*

CLUB CURRENTLY REGISTERED WITH : ..............................................................................*

To be completed by the Regional Performance Manager:

The Player will be:

1. An Academy Player

2. A Tracked Player

*compulsory fields for completion

SIGNED : ....................................................................................* (Regional Performance Manager)______________________________________________________________________________________

When completed form must be returned to :-Peter Owens

Rugby Operations ManagerMillennium Stadium

Cardiff CF10 1NS

83

Appendix v

WRU April Strength Testing Results

84

Appendix vi

WRU College Strength Rankings

85

Appendix vii.

WRU Physical Testing

86

Appendix viii.

Gymnasium layout during testing

87

Appendix ix

My Jump App pictures

88

Appendix x.

Relative versus absolute strength.

89

90

83.1

100

63.8

100

109.

111

2.5

8190

8085

100.

512

581

.711

593

.811

085

.411

586

.488

8910

5.83

3383

.111

0#N

/A10

012

.399

1412

.247

45

102.

57

113.

29

105.

8333

1.14

1.26

1.23

8469

7 8 4

This

stu

dy

sam

ple

pla

yer

aver

age

rela

tive

str

engt

h (r

=)

Abs

olut

e v

ersu

s re

lati

ve s

tren

gth

com

par

ison

s/ex

ampl

es

WRU

col

lege

s tr

acke

d p

laye

rs re

lati

ve s

tren

gth

(r=)

WRU

Col

lege

s Tr

acke

d P

laye

rs n

ation

al a

vera

ge 1

RM (

kg)

CSG

trac

ked

pla

yer a

vera

ge 1

RM (k

g)

This

stu

dy

sam

ple

pla

yer

aver

age

1RM

(kg

)

CSG

trac

ked

pla

yer a

vera

ge re

lati

ve s

tren

gth

(r=)

32 6

1.24

3781

095

1.40

7588

739

1.17

2707

889

1.34

6604

215

1.23

8469

408

1.20

3369

434

mod

est

anda

rd d

evi

ation

2m

ean

med

ian

#N/A

0.16

4733

54

Rela

tive

str

eng

th

(loa

d/b

odyw

eig

ht)

Rela

tive

str

eng

th

rank

ing

1.20

3369

434

1.56

7398

119

1.03

1164

071 95

1.11

1111

111

1.06

251 2 5

mod

est

anda

rd d

evi

ation

Abs

olut

e S

tren

gth

Rank

ing

6 6 4 8 9

med

ian

F4H

ooke

rB5

Cen

tre

mea

n

B3Fl

y-ha

lfF3

Prop

B4Ce

ntr

e

B2Ba

ck T

hre

eF1

Seco

nd

Row

F2Fl

anke

r

Nam

e (A

-Z)

Posi

tion

Wei

ght

1RM

HFS

B1Ba

ck T

hre

e

109.1 112.581 90

100.5 12593.8 11096.1 109.375

97.15 111.25#N/A #N/A

10.26718 12.54679

102.57

113.29

109.375

1.14

1.26

1.139691

WRU colleges tracked players relative strength (r=)

CSG tracked player average relative strength (r=)

This s tudy sample player forwards average relative strength (r=)

mode mode #N/Astandard deviation standard deviation 0.078293945

WRU Colleges Tracked Players national average 1RM (kg)

CSG tracked player average 1RM (kg)

This study sample player forwards average 1RM (kg)

mean mean 1.139691041median median 1.1419095

F3 Prop 1 1.243781095 1F4 Hooker 3 1.172707889 2

F1 Second Row 2 1.03116407 4F2 Flanker 4 1.111111111 3

Absolute versus relative strength comparisons/examples

Name (A-Z) Position Weight 1RM HFS Absolute Strength

RankingRelative strength load/bodyweight

Relative strength ranking

83.1 10063.8 100

80 8581.7 11585.4 11578.8 10381.7 100

#N/A 1007.705842 11.22497

102.57

113.29

103

1.14

1.26

1.317492

mean mean 1.317492102

This study sample player backs average relative strength (r=)

This study sample player backs average 1RM (kg)

WRU colleges tracked players relative strength (r=)

CSG tracked player average relative strength (r=)

CSG tracked player average 1RM (kg)

median median 1.346604215mode mode #N/A

standard deviation standard deviation 0.172894703

WRU Colleges Tracked Players national average 1RM (kg)

B5 Centre 1 1.346604215 3

5B4 Centre 1 1.407588739 2B3 Fly-half 5 1.0625

B1 Back Three 3 1.203369434 4B2 Back Three 3 1.567398119 1

Absolute versus relative strength comparisons/examples

Name (A-Z) Position Weight 1RM HFS Absolute Strength

RankingRelative strength

(load/bodyweight)Relative strength

ranking

91

Appendix xi.

Control Results.

92

83.1

0.567

39.422

590.5

739.

84086

0.559

38.317

9863.

80.5

6439.

00652

0.56

38.455

20.5

6338.

88683

109.1

0.576

40.684

030.5

7840.

96705

0.569

39.701

1981

0.548

36.824

780.5

537.

09406

0.565

39.144

9780

0.54

35.757

450.5

4336.

15586

0.555

37.771

57100

.50.4

6526.

51459

0.482

28.488

730.4

8328.

60706

81.7

0.567

39.422

590.6

1546.

37984

0.615

46.379

8493.

80.5

3535.

09834

0.537

35.361

250.5

2934.

3155

85.4

0.583

41.678

890.6

44.145

0.644.

145

86.488

890.5

49444

37.156

640.5

59444

38.543

090.5

59778

38.585

5583.

10.5

6439.

00652

0.56

38.455

20.5

6338.

88683

na0.5

6739.

42259

#N/A

#N/A

#N/A

#N/A

12.399

140.0

33483

4.2965

360.0

36588

4.9371

20.0

35987

4.8516

261.9

0.0698

85268

mode

stand

ard de

viatio

n

1.83

1.83

0.0648

83561

1.82

#N/A

0.0860

23253

mean

media

n1.8

31.8

411111

111.8

333333

331.8

377777

78

1.83

1.76

1.78

1.87

1.89

1.91.7

71.7

71.7

91.9

91.9

71.8

31.7

91.8

41.8

21.8

1.92

1.93

1.91.7

1.73

1.91

1.85

1.83

Centr

eHo

oker

Centr

e

1.71

1.83 1.9

B4 F4 B5

Back T

hree

Back T

hree

Secon

d Row

Flanke

rFly

-half

B1 B2 F1 F2 B3Pro

p

Contr

ol VC

A reco

rding

sheet

Name

(A-Z)

Positi

onWe

ight

(kg)

CMJ aft

er 4 m

in (ms

and

cm)

10m sp

rint aft

er 4

min (

secs)

CMJ aft

er 7 m

in (ms

and

cm)

10m sp

rint aft

er 7

min (

secs)

CMJ aft

er 10

min

(ms a

nd cm

)10m

sprin

t after

10 mi

n (sec

s)

109.1 0.576 40.68403 0.578 40.96705 0.569 39.7011981 0.548 36.82478 0.55 37.09406 0.565 39.14497

100.5 0.465 26.51459 0.482 28.48873 0.483 28.6070693.8 0.535 35.09834 0.537 35.36125 0.529 34.315596.1 0.531 34.78044 0.53675 35.47777 0.5365 35.44218

97.15 0.5415 35.96156 0.5435 36.22766 0.547 36.73023#N/A #N/A #N/A #N/A #N/A #N/A #N/A

10.26718 0.040884 5.183074 0.03491 4.51677 0.034594 4.467606mode #N/A #N/A 1.9

standard deviation 0.03391165 0.061796035 0.060570207

mean 1.88 1.9075 1.8925median 1.885 1.91 1.9

F3 Prop 1.9 1.99 1.97F4 Hooker 1.87 1.89 1.9

F1 Second Row 1.92 1.93 1.9F2 Flanker 1.83 1.82 1.8

Forwards Control VCA recording sheet

Name (A-Z) Position Weight (kg)

CMJ after 4 min (ms and cm)

10m sprint after 4 min (secs)

CMJ after 7 min (ms and cm)

10m sprint after 7 min (secs)

CMJ after 10 min (ms and cm)

10m sprint after 10 min (secs)

83.1 0.567 39.42259 0.57 39.84086 0.559 38.3179863.8 0.564 39.00652 0.56 38.4552 0.563 38.88683

80 0.54 35.75745 0.543 36.15586 0.555 37.7715781.7 0.567 39.42259 0.615 46.37984 0.615 46.3798485.4 0.583 41.67889 0.6 44.145 0.6 44.145

#NAME? 0.5642 39.05761 0.5776 40.99535 0.5784 41.1002480.85 0.567 39.42259 0.57 39.84086 0.563 38.88683

#N/A 0.567 39.42259 #N/A #N/A #N/A #N/A7.705842 0.013819 1.89901 0.026326 3.744834 0.024361 3.18499

1.76 1.78

standard deviation 0.066932802 0.048414874 0.039293765

median 1.83 1.77 1.79mode 1.83 #N/A #N/A

mean 1.81 1.774 1.794

B3 Fly-half 1.83 1.79 1.84B4 Centre 1.77 1.77 1.79B5 Centre 1.83

B1 Back Three 1.91 1.85 1.83B2 Back Three 1.71 1.7 1.73

Backs Control VCA recording sheet

Name (A-Z) Position Weight (kg)

CMJ after 4 min (ms and cm)

10m sprint after 4 min (secs)

CMJ after 7 min (ms and cm)

10m sprint after 7 min (secs)

CMJ after 10 min (ms and cm)

10m sprint after 10 min (secs)

93

Appendix xii.

Heavy Back Squats Results.

94

3050

7083.

1125

37.5

62.5

87.5

0.571

39.998

080.5

7340.

26134

0.565

39.144

9763.

8125

37.5

62.5

87.5

0.573

40.261

340.5

6739.

42259

0.56

38.455

2109

.1140

.625

42.187

570.

3198.

440.5

7640.

68403

0.585

41.965

340.5

739.

84086

81112

.533.

7556.

2578.

750.5

841.

25105

0.579

41.108

930.5

5537.

77157

80106

.2531.

87553.

12574.

3750.5

6539.

14497

0.564

39.006

520.5

4836.

82478

100.5

12546.

87578.

125109

.375

0.523

33.541

490.5

334.

44536

0.512

32.145

4181.

7143

.7543.

12571.

875100

.625

0.619

46.985

120.6

3549.

44547

0.623

47.594

3293.

8137

.541.

2568.

7596.

250.5

5637.

9078

0.569

39.701

190.5

5738.

04428

85.4

143.75

43.125

71.875

100.62

50.5

9343.

12096

0.614

46.229

130.6

0745.

18106

86.488

89128

.8194

39.687

566.

14556

92.604

440.5

72889

40.321

650.5

79556

41.287

320.5

66333

39.444

7283.

1125

41.25

68.75

96.25

0.573

40.261

340.5

7340.

26134

0.56

38.455

2#N/

A125

37.5

62.5

87.5

#N/A

#N/A

#N/A

#N/A

#N/A

#N/A

12.399

1412.

82374

4.5927

937.6

54504

10.716

670.0

24565

3.4403

350.0

2848

4.0896

680.0

30594

4.2876

07

Heavy

Back S

quat V

CA rec

ording

sheet

Name (A

-Z)Pos

ition

Weigh

t1RM

HBS

(calc.)

HFS%R

MCM

J after 4

min (m

s and

cm)

10m spr

int aft

er 4

min (se

cs)CM

J after 7

min (m

s and

cm)

10m spr

int aft

er 7

min (se

cs)CM

J after 1

0 min

(ms an

d cm)

10m spr

int aft

er 10

min (se

cs)B1

Back T

hree

1.78

1.73

1.78

F1Sec

ond Ro

w1.8

21.8

11.9

B2Bac

k Thre

e1.6

41.6

71.7

4

B3Fly

-half

1.81.7

61.7

8F2

Flanke

r1.8

1.75

1.75

B4Cen

tre1.6

91.7

51.7

4F3

Prop

1.87

1.86

1.88

B5Cen

tre1.7

11.7

1.76

F4Hoo

ker1.7

31.8

21.8

1.7922

22222

media

n1.7

81.7

51.7

8mo

de1.8

1.75

1.78

standa

rd devi

ation

0.0683

13005

0.0566

55772

0.0557

3305

mean

1.76

1.7611

11111

95

3050

70109

.1140

.625

42.187

570.

3198.

440.5

7640.

68403

0.585

41.965

340.5

739.

84086

81112

.533.

7556.

2578.

750.5

841.

25105

0.579

41.108

930.5

5537.

77157

100.5

12546.

87578.

125109

.375

0.523

33.541

490.5

334.

44536

0.512

32.145

4193.

8137

.541.

2568.

7596.

250.5

5637.

9078

0.569

39.701

190.5

5738.

04428

96.1

128.90

6341.

01563

68.358

7595.

70375

0.5587

538.

34609

0.5657

539.

30521

0.5485

36.950

5397.

15131

.2541.

71875

69.53

97.345

0.566

39.295

920.5

7440.

40506

0.556

37.907

92#N

/A#N

/A#N

/A#N

/A#N

/A#N

/A#N

/A#N

/A#N

/A#N

/A#N

/A10.

26718

11.131

14.7

05045

7.8415

8710.

97859

0.0225

543.0

48812

0.0214

172.9

19958

0.0218

462.8

85897

Forwa

rds He

avy Ba

ck Squ

at VCA

recor

ding s

heet

Name

(A-Z)

Positi

onWe

ight

1RM H

BS (ca

lc.)HF

S%RM

CMJ aft

er 4 m

in (ms

and

cm)

10m sp

rint aft

er 4

min (s

ecs)

CMJ aft

er 7 m

in (ms

and

cm)

10m sp

rint aft

er 7

min (s

ecs)

CMJ aft

er 10

min

(ms a

nd cm

)10m

sprin

t after

10 mi

n (secs

)F1

Secon

d Row

1.82

1.81

1.9

1.805

1.81

1.8325

F2Fla

nker

1.81.7

51.7

5F3

Prop

1.87

1.86

1.88

F4Ho

oker

1.73

1.82

1.8

stand

ard de

viatio

n0.0

502493

780.0

393700

390.0

605702

07

media

n1.8

11.8

151.8

4mo

de#N

/A#N

/A#N

/A

mean

3050

7083.

1125

37.5

62.5

87.5

0.571

39.998

080.5

7340.

26134

0.565

39.144

9763.

8125

37.5

62.5

87.5

0.573

40.261

340.5

6739.

42259

0.56

38.455

280

106.25

31.875

53.125

74.375

0.565

39.144

970.5

6439.

00652

0.548

36.824

7881.

7143

.7543.

12571.

875100

.625

0.619

46.985

120.6

3549.

44547

0.623

47.594

3285.

4143

.7543.

12571.

875100

.625

0.593

43.120

960.6

1446.

22913

0.607

45.181

0678.

8128

.7538.

62564.

37590.

1250.5

84241.

90209

0.5906

42.873

010.5

80641.

44006

80.85

126.87

538.

0625

63.437

588.

8125

0.5786

41.081

720.5

7340.

26134

0.623

47.594

32#N

/A125

37.5

62.5

87.5

#N/A

#N/A

#N/A

#N/A

#N/A

#N/A

7.7058

4214.

03122

4.2093

657.0

15608

9.8218

510.0

19783

2.8719

980.0

28612

4.1984

920.0

2907

4.1796

57

Heavy

Back S

quat V

CA rec

ording

sheet

Name

(A-Z)

Positio

nWe

ight

1RM HB

S (ca

lc.)HFS

%RM

CMJ aft

er 4 mi

n (ms

and cm

)10m

sprin

t after 4

min

(secs)

CMJ aft

er 7 mi

n (ms

and cm

)10m

sprin

t after 7

min

(secs)

CMJ aft

er 10 m

in (m

s and c

m)10m

sprin

t after 1

0 min

(secs)

B1Bac

k Thre

e1.7

81.7

31.7

8

B3Fly

-half

1.81.7

61.7

8B2

Back T

hree

1.64

1.67

1.74

B5Cen

tre1.7

11.7

1.76

B4Cen

tre1.6

91.7

51.7

4

mean

1.724

1.722

1.76

media

n1.7

11.7

31.7

6mo

de#N

/A#N

/A1.7

8sta

ndard d

eviatio

n0.0

588557

560.0

331058

910.0

178885

44

Appendix xiii.

Heavy Front Squat Results.

96

3050

7083.1

10030

5070

0.5734

0.26134

0.5784

0.96705

0.5693

9.70119

63.8100

3050

700.57

640.68

4030.56

539.14

4970.56

238.73

037109

.1112

.533.7

556.2

578.7

50.58

41.2510

50.58

341.67

8890.57

640.68

40381

9027

4563

0.5834

1.67889

0.5984

3.85119

0.5653

9.14497

8085

25.542.5

59.50.55

237.36

4330.56

38.4552

0.5573

8.04428

100.5

12537.5

62.587.5

0.5173

2.77631

0.5173

2.77631

0.5173

2.77631

81.7115

34.557.5

80.50.61

746.68

1990.63

349.13

4490.61

546.37

98493.8

11033

5577

0.547

36.6905

0.5523

7.36433

0.5433

6.15586

85.4115

34.557.5

80.50.6

44.145

0.6174

6.68199

0.644.1

4586.4

88891

05.8333

31.755

2.91667

74.0833

30.571

66740.1

7038

0.57811

141.11

7160.56

7111

39.5291

83.1110

3355

770.57

640.68

4030.57

840.96

7050.56

539.14

497#N/

A100

3050

70#N/

A#N/

A#N/

A#N/

A#N/

A#N/

A12.3

99141

2.24745

3.67423

56.123

7248.57

32140

.028008

3.89543

7.

4.68670

60.02

7253.79

432

1.80444

4444

1.79 1.80.06

326507

standar

d devi

ation

0.08633

8410.07

274172

1

mean

1.80111

1111

1.79444

4444

media

n1.79

1.78mo

de1.83

1.68

Heavy F

ront Sq

uat VC

A recor

ding sh

eet

Name (A

-Z)Pos

ition

Weigh

t1RM

HFS

(tested

)HFS

%RM

CMJ aft

er 4 mi

n (ms

and cm

)10m

sprint

after 4

min (se

cs)CM

J after 7

min (m

s and

cm)

10m spr

int afte

r 7 min

(secs)

CMJ aft

er 10 m

in (ms

and cm

)10m

sprint

after 10

min

(secs)

F3B1 B2 F1 F2 B3

Hooker

Centre

B4 F4 B5

1.771.68

1.75

Prop

Centre

Back Th

reeBac

k Three

Second

Row

Flanker

Fly-hal

f

1.841.86

1.81.65

1.681.73

1.781.79

1.831.77

1.77

1.751.78

1.781.83

1.881.87

1.761.87

1.81.99

1.851.95

1.79

30 50 70109.1 112.5 33.75 56.25 78.75 0.58 41.25105 0.583 41.67889 0.576 40.68403

81 90 27 45 63 0.583 41.67889 0.598 43.85119 0.565 39.14497100.5 125 37.5 62.5 87.5 0.517 32.77631 0.517 32.77631 0.517 32.77631

93.8 110 33 55 77 0.547 36.6905 0.552 37.36433 0.543 36.1558696.1 109.375 32.8125 54.6875 76.5625 0.55675 38.09919 0.5625 38.91768 0.55025 37.19029

97.15 111.25 33.375 55.625 77.875 0.5635 38.97078 0.5675 39.52161 0.554 37.65041#N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A

10.26718 12.54679 3.764036 6.273394 8.782751 0.026948 3.642316 0.031068 4.245382 0.026069 3.024079

Forwards Heavy Front Squat VCA recording sheet

Name (A-Z) Position Weight 1RM HFS(tested)

HFS%RM CMJ after 4 min (ms and cm)

10m sprint after 4 min (secs)

CMJ after 7 min (ms and cm)

10m sprint after 7 min (secs)

CMJ after 10 min (ms and cm)

10m sprint after 10 min (secs)

F1 Second Row 1.84 1.86 1.8

1.84 1.8475

F2 Flanker 1.83 1.77 1.77F3 Prop 1.99 1.85 1.95F4 Hooker 1.83 1.88 1.87

standard deviation 0.067961386 0.041833001 0.069417217

median 1.835 1.855 1.835mode 1.83 #N/A #N/A

mean 1.8725

30 50 7083.1 100 30 50 70 0.573 40.26134 0.578 40.96705 0.569 39.7011963.8 100 30 50 70 0.576 40.68403 0.565 39.14497 0.562 38.73037

80 85 25.5 42.5 59.5 0.552 37.36433 0.56 38.4552 0.557 38.0442881.7 115 34.5 57.5 80.5 0.617 46.68199 0.633 49.13449 0.615 46.3798485.4 115 34.5 57.5 80.5 0.6 44.145 0.617 46.68199 0.6 44.14578.8 103 30.9 51.5 72.1 0.5836 41.82734 0.5906 42.87674 0.5806 41.4001481.7 100 30 50 70 0.576 40.68403 0.578 40.96705 0.569 39.70119

#N/A 100 30 50 70 #N/A #N/A #N/A #N/A #N/A #N/A7.705842 11.22497 3.367492 5.612486 7.857481 0.022597 3.243983 0.029138 4.260573 0.022791 3.274371

Backs Heavy Front Squat VCA recording sheet

Name (A-Z) Position Weight 1RM HFS(tested)

HFS%RM CMJ after 4 min (ms and cm)

10m sprint after 4 min (secs)

CMJ after 7 min (ms and cm)

10m sprint after 7 min (secs)

CMJ after 10 min (ms and cm)

10m sprint after 10 min (secs)

B1 Back Three 1.77 1.68 1.75

B3 Fly-half 1.79 1.78 1.79B2 Back Three 1.65 1.68 1.73

B5 Centre 1.75 1.78 1.78B4 Centre 1.76 1.87 1.8

mean 1.744 1.758 1.77median 1.76 1.78 1.78mode #N/A 1.68 #N/A

standard deviation 0.048826222 0.071665891 0.02607681

97

Appendix xiv.

Depth Jump Results.

83.1 0.565 39.14497 0.576 40.68403 0.569 39.7011963.8 0.617 46.68199 0.633 49.13449 0.648 51.49073

109.1 0.6 44.145 0.592 42.97565 0.585 41.9653481 0.582 41.53603 0.565 39.14497 0.665 54.2784180 0.617 46.68199 0.567 39.42259 0.565 39.14497

100.5 0.523 33.54149 0.583 41.67889 0.517 32.7763181.7 0.617 46.68199 0.617 46.68199 0.583 41.6788993.8 0.566 39.28365 0.538 35.49307 0.572 40.1209485.4 0.615 46.37984 0.633 49.13449 0.665 54.22784

86.48889 0.589111 42.67522 0.589333 42.70557 0.596556 43.9316283.1 0.6 44.145 0.583 41.67889 0.583 41.67889

na 0.617 46.68199 0.633 49.13449 0.665 #N/A12.39914 0.031018 4.380958 0.030786 4.461733 0.048328 7.140806

Depth Jump VCA recording sheet

Name (A-Z) Position Weight (kg)

CMJ after 4 min (ms and cm)

10m sprint after 4 min (secs)

CMJ after 7 min (ms and cm)

10m sprint after 7 min (secs)

CMJ after 10 min (ms and cm)

10m sprint after 10 min (secs)

B4F4B5

Back ThreeBack Three

Second RowFlankerFly-half

B1B2F1F2B3F3 Prop

CentreHookerCentre

1.8

1.79

1.92

1.71 1.681.81 1.8 1.79

1.78 1.791.86 1.83 1.82

1.74 1.691.87 1.85

1.84 1.74 1.79

meanmedian 1.79

1.817777778 1.791111111 1.7688888891.77 1.83 1.731.84 1.82 1.781.73

1.790.054046162

modestandard deviation

1.811.84

0.052020888

1.81.83

0.049541104

83.1 0.565 39.14497 0.576 40.68403 0.569 39.7011963.8 0.617 46.68199 0.633 49.13449 0.648 51.49073

80 0.617 46.68199 0.567 39.42259 0.565 39.1449781.7 0.617 46.68199 0.617 46.68199 0.583 41.6788985.4 0.615 46.37984 0.633 49.13449 0.665 54.2278478.8 0.268444 19.9101 0.265889 19.54701 0.262778 19.1128681.7 0.617 46.68199 0.5965 43.68301 0.576 40.69004

#N/A 0.617 46.68199 #N/A #N/A #N/A #N/A7.705842 0.022517 3.263627 0.027526 4.046693 0.033439 4.989517

1.83 1.73

standard deviation 0.040311289 0.032691742 0.052618913

median 1.805 1.74 1.74mode #N/A 1.74 1.79

mean 0.797777778 0.776666667 0.772222222

B3 Fly-half 1.84 1.74 1.79B4 Centre 1.73 1.74 1.69B5 Centre 1.77

B1 Back Three 1.81 1.8 1.79B2 Back Three 1.8 1.71 1.68

Backs Depth Jump VCA recording sheet

Name (A-Z) Position Weight (kg)

CMJ after 4 min (ms and cm)

10m sprint after 4 min (secs)

CMJ after 7 min (ms and cm)

10m sprint after 7 min (secs)

CMJ after 10 min (ms and cm)

10m sprint after 10 min (secs)

109.1 0.6 44.145 0.592 42.97565 0.585 41.9653481 0.582 41.53603 0.565 39.14497 0.665 54.27841

100.5 0.523 33.54149 0.583 41.67889 0.517 32.7763193.8 0.566 39.28365 0.538 35.49307 0.572 40.1209496.1 0.56775 39.62654 0.5695 39.82314 0.58475 42.28525

97.15 0.574 40.40984 0.574 40.41193 0.5785 41.04314#N/A #N/A #N/A #N/A #N/A #N/A #N/A

10.26718 0.028499 3.911777 0.020622 2.85445 0.052898 7.730496mode #N/A #N/A #N/A

standard deviation 0.04656984 0.032015621 0.027386128

mean 1.8525 1.825 1.81median 1.85 1.83 1.805

F3 Prop 1.92 1.87 1.85F4 Hooker 1.84 1.82 1.78

F1 Second Row 1.86 1.83 1.82F2 Flanker 1.79 1.78 1.79

Forwards Depth Jump VCA recording sheet

Name (A-Z) Position Weight (kg)

CMJ after 4 min (ms and cm)

10m sprint after 4 min (secs)

CMJ after 7 min (ms and cm)

10m sprint after 7 min (secs)

CMJ after 10 min (ms and cm)

10m sprint after 10 min (secs)

98

Appendix xv.

CMJ Results DJ VCA vs Control VCA t-tests

99

100

t-Test: Paired Two Sample for Means

CMJ DJ vs Control @4 mins post VCA Control DJMean 37.15664 42.67522Variance 20.76775 21.5919Observations 9 9Pearson Correlation 0.763747Hypothesized Mean Difference 0df 8t Stat -5.23179P(T<=t) one-tail 0.000396t Critical one-tail 1.859548P(T<=t) two-tail 0.000791t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

CMJ DJ vs Control @7 mins post VCA Control DJMean 38.54309 42.70557Variance 27.42205 22.39544Observations 9 9Pearson Correlation 0.559441Hypothesized Mean Difference 0df 8t Stat -2.65691P(T<=t) one-tail 0.014472t Critical one-tail 1.859548P(T<=t) two-tail 0.028944t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

CMJ DJ vs Control @10 mins post VCA Control DJMean 38.58555 43.93162Variance 26.48056 57.365Observations 9 9Pearson Correlation 0.571416Hypothesized Mean Difference 0df 8t Stat -2.55823P(T<=t) one-tail 0.01687t Critical one-tail 1.859548P(T<=t) two-tail 0.03374t Critical two-tail 2.306004

Appendix xvi.

CMJ Results HFS VCA vs Control t-tests.

t-Test: Paired Two Sample for Means

CMJ HFS vs Control @4 mins post VCA Control HFSMean 37.15664 40.17038Variance 20.76775 17.07124Observations 9 9Pearson Correlation 0.84294Hypothesized Mean Difference 0df 8t Stat -3.66201P(T<=t) one-tail 0.003191t Critical one-tail 1.859548P(T<=t) two-tail 0.006383t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

CMJ HFS vs Control @7 mins post VCA Control HFSMean 38.54309 41.11716Variance 27.42205 24.71086Observations 9 9Pearson Correlation 0.929363Hypothesized Mean Difference 0df 8t Stat -3.98875P(T<=t) one-tail 0.002006t Critical one-tail 1.859548P(T<=t) two-tail 0.004012t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

CMJ HFS vs Control @10 mins post VCA Control HFSMean 38.58555 39.5291Variance 26.48056 16.19647Observations 9 9Pearson Correlation 0.982876Hypothesized Mean Difference 0df 8t Stat -2.01834P(T<=t) one-tail 0.03913t Critical one-tail 1.859548P(T<=t) two-tail 0.07826t Critical two-tail 2.306004

101

Appendix xvii.

CMJ Results HBS vs Control t-tests.

t-Test: Paired Two Sample for Means

CMJ HBS vs Control @4 mins post VCA Control HBSMean 37.15664 40.32165Variance 20.76775 13.31539Observations 9 9Pearson Correlation 0.801698Hypothesized Mean Difference 0df 8t Stat -3.48575P(T<=t) one-tail 0.004125t Critical one-tail 1.859548P(T<=t) two-tail 0.00825t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

CMJ HBS vs Control @7 mins post VCA Control HBSMean 38.54309 41.28732Variance 27.42205 18.81606Observations 9 9Pearson Correlation 0.944496Hypothesized Mean Difference 0df 8t Stat -4.51182P(T<=t) one-tail 0.000986t Critical one-tail 1.859548P(T<=t) two-tail 0.001971t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

CMJ HBS vs Control @10 mins post VCA Control HBSMean 38.58555 39.44472Variance 26.48056 20.68152Observations 9 9Pearson Correlation 0.938206Hypothesized Mean Difference 0df 8t Stat -1.42971P(T<=t) one-tail 0.095333t Critical one-tail 1.859548P(T<=t) two-tail 0.190667t Critical two-tail 2.306004

102

Appendix xviii.

CMJ Results HBS vs DJ t-tests.

t-Test: Paired Two Sample for Means

CMJ HBS vs DJ @4 mins post VCA HBS DJMean 40.32165 42.67522Variance 13.31539 21.5919Observations 9 9Pearson Correlation 0.75817Hypothesized Mean Difference 0df 8t Stat -2.32831P(T<=t) one-tail 0.024144t Critical one-tail 1.859548P(T<=t) two-tail 0.048289t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

CMJ HBS vs DJ @7 mins post VCA HBS DJMean 41.28732 42.70557Variance 18.81606 22.39544Observations 9 9Pearson Correlation 0.49364Hypothesized Mean Difference 0df 8t Stat -0.92969P(T<=t) one-tail 0.189865t Critical one-tail 1.859548P(T<=t) two-tail 0.37973t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

CMJ HBS vs DJ @10 mins post VCA HBS DJMean 39.44472 43.93162Variance 20.68152 57.365Observations 9 9Pearson Correlation 0.4199Hypothesized Mean Difference 0df 8t Stat -1.9206P(T<=t) one-tail 0.045515t Critical one-tail 1.859548P(T<=t) two-tail 0.091031t Critical two-tail 2.306004

103

Appendix xix.

CMJ Results HBS vs HFS t-tests.

t-Test: Paired Two Sample for Means

CMJ HBS vs HFS @4 mins post VCA HBS HFSMean 40.32165 40.17038Variance 13.31539 17.07124Observations 9 9Pearson Correlation 0.97902Hypothesized Mean Difference 0df 8t Stat 0.487745P(T<=t) one-tail 0.319408t Critical one-tail 1.859548P(T<=t) two-tail 0.638816t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

CMJ HBS vs HFS @7 mins post VCA HBS HFSMean 41.28732 41.11716Variance 18.81606 24.71086Observations 9 9Pearson Correlation 0.960694Hypothesized Mean Difference 0df 8t Stat 0.352596P(T<=t) one-tail 0.36675t Critical one-tail 1.859548P(T<=t) two-tail 0.733501t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

CMJ HBS vs HFS @10 mins post VCA HBS HFSMean 39.44472 39.5291Variance 20.68152 16.19647Observations 9 9Pearson Correlation 0.970195Hypothesized Mean Difference 0df 8t Stat -0.21669P(T<=t) one-tail 0.416938t Critical one-tail 1.859548P(T<=t) two-tail 0.833876t Critical two-tail 2.306004

104

Appendix xx.

CMJ Results DJ vs HFS t-tests.

t-Test: Paired Two Sample for Means

CMJ DJ vs HFS @4 mins post VCA DJ HFSMean 42.67522 40.17038Variance 21.5919 17.07124Observations 9 9Pearson Correlation 0.719545Hypothesized Mean Difference 0df 8t Stat 2.262205P(T<=t) one-tail 0.026769t Critical one-tail 1.859548P(T<=t) two-tail 0.053538t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

CMJ DJ vs HFS @7 mins post VCA DJ HFSMean 42.70557 41.11716Variance 22.39544 24.71086Observations 9 9Pearson Correlation 0.478578Hypothesized Mean Difference 0df 8t Stat 0.960972P(T<=t) one-tail 0.182354t Critical one-tail 1.859548P(T<=t) two-tail 0.364707t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

CMJ DJ vs HFS @10 mins post VCA DJ HFSMean 43.93162 39.5291Variance 57.365 16.19647Observations 9 9Pearson Correlation 0.46903Hypothesized Mean Difference 0df 8t Stat 1.969568P(T<=t) one-tail 0.0422t Critical one-tail 1.859548P(T<=t) two-tail 0.0844t Critical two-tail 2.306004

105

Appendix xxi.

CMJ Single factor Anova Results.

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 9 334.4098 37.15664 20.76775DJ 9 384.077 42.67522 21.5919HFS 9 361.5334 40.17038 17.07124HBS 9 362.8948 40.32165 13.31539

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 138.1296 3 46.04319 2.531714 0.074548 2.90112Within Groups 581.9702 32 18.18657

Total 720.0998 35

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 9 346.8879 38.54309 27.42205DJ 9 384.3502 42.70557 22.39544HFS 9 370.0544 41.11716 24.71086HBS 9 371.5859 41.28732 18.81606

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 81.10408 3 27.03469 1.158492 0.340767 2.90112Within Groups 746.7553 32 23.3361

Total 827.8594 35

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 9 347.2699 38.58555 26.48056DJ 9 395.3846 43.93162 57.365HFS 9 355.7619 39.5291 16.19647HBS 9 355.0024 39.44472 20.68152

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 156.8941 3 52.29802 1.732819 0.180004 2.90112Within Groups 965.7884 32 30.18089

Total 1122.682 35

4 mins post VCA

7 mins post VCA

10 mins post VCA

106

Appendix xxii.

Sprint Results DJ vs Control t-tests.

t-Test: Paired Two Sample for Means

Sprint DJ vs C @4minpost VCA Control DJMean 1.841111 1.817778Variance 0.004736 0.003044Observations 9 9Pearson Correlation 0.613023Hypothesized Mean Difference 0df 8t Stat 1.252198P(T<=t) one-tail 0.122933t Critical one-tail 1.859548P(T<=t) two-tail 0.245865t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

Sprint DJ vs C @7minpost VCA Control DJMean 1.833333 1.791111Variance 0.008325 0.002761Observations 9 9Pearson Correlation 0.820402Hypothesized Mean Difference 0df 8t Stat 2.232399P(T<=t) one-tail 0.028043t Critical one-tail 1.859548P(T<=t) two-tail 0.056087t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

Sprint DJ vs C @10minPost VCA Control DJMean 1.837778 1.768889Variance 0.005494 0.003286Observations 9 9Pearson Correlation 0.861281Hypothesized Mean Difference 0df 8t Stat 5.406657P(T<=t) one-tail 0.00032t Critical one-tail 1.859548P(T<=t) two-tail 0.000641t Critical two-tail 2.306004

107

Appendix xxiii.

Sprint Results HFS vs Control t-tests.

t-Test: Paired Two Sample for Means

Sprint HFS vs C @4minpost VCA Control HFSMean 1.841111 1.801111Variance 0.004736 0.008386Observations 9 9Pearson Correlation 0.713818Hypothesized Mean Difference 0df 8t Stat 1.868397P(T<=t) one-tail 0.049325t Critical one-tail 1.859548P(T<=t) two-tail 0.09865t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

Sprint HFS vs C @7minpost VCA Control HFSMean 1.833333 1.794444Variance 0.008325 0.005953Observations 9 9Pearson Correlation 0.540982Hypothesized Mean Difference 0df 8t Stat 1.429465P(T<=t) one-tail 0.095368t Critical one-tail 1.859548P(T<=t) two-tail 0.190736t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

Sprint HFS vs C @10minpost VCA Control HFSMean 1.837778 1.804444Variance 0.005494 0.004503Observations 9 9Pearson Correlation 0.864223Hypothesized Mean Difference 0df 8t Stat 2.672612P(T<=t) one-tail 0.014124t Critical one-tail 1.859548P(T<=t) two-tail 0.028248t Critical two-tail 2.306004

108

Appendix xxiv.

Sprint Results HBS vs Control t-tests.

t-Test: Paired Two Sample for Means

Sprint HBS vs C @4minpost VCA Control HBSMean 1.841111 1.76Variance 0.004736 0.00525Observations 9 9Pearson Correlation 0.804682Hypothesized Mean Difference 0df 8t Stat 5.494782P(T<=t) one-tail 0.000289t Critical one-tail 1.859548P(T<=t) two-tail 0.000577t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

Sprint HBS vs C @7minpost VCA Control HBSMean 1.833333 1.761111Variance 0.008325 0.003611Observations 9 9Pearson Correlation 0.92484Hypothesized Mean Difference 0df 8t Stat 5.114782P(T<=t) one-tail 0.000457t Critical one-tail 1.859548P(T<=t) two-tail 0.000913t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

Sprint HBS vs C @10 minpost VCA Control HBSMean 1.837778 1.792222Variance 0.005494 0.003494Observations 9 9Pearson Correlation 0.868495Hypothesized Mean Difference 0df 8t Stat 3.681909P(T<=t) one-tail 0.003101t Critical one-tail 1.859548P(T<=t) two-tail 0.006202t Critical two-tail 2.306004

109

Appendix xxv.

Sprint Results HBS vs DJ t-tests.

t-Test: Paired Two Sample for Means

Sprint HBS vs DJ @4 minpost VCA HBS DJMean 1.76 1.817778Variance 0.00525 0.003044Observations 9 9Pearson Correlation 0.731631Hypothesized Mean Difference 0df 8t Stat -3.50584P(T<=t) one-tail 0.004005t Critical one-tail 1.859548P(T<=t) two-tail 0.00801t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

Sprint HBS vs DJ @7 minpost VCA HBS DJMean 1.761111 1.791111Variance 0.003611 0.002761Observations 9 9Pearson Correlation 0.668573Hypothesized Mean Difference 0df 8t Stat -1.94099P(T<=t) one-tail 0.044106t Critical one-tail 1.859548P(T<=t) two-tail 0.088211t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

Sprint HBS vs DJ @10 minpost VCA HBS DJMean 1.792222 1.768889Variance 0.003494 0.003286Observations 9 9Pearson Correlation 0.786525Hypothesized Mean Difference 0df 8t Stat 1.83829P(T<=t) one-tail 0.051658t Critical one-tail 1.859548P(T<=t) two-tail 0.103316t Critical two-tail 2.306004

110

Appendix xxvi.

Sprint Results HFS vs DJ t-tests.

t-Test: Paired Two Sample for Means

Sprint HFS vs DJ @4 minpost VCA HFS DJMean 1.801111 1.817778Variance 0.008386 0.003044Observations 9 9Pearson Correlation 0.730339Hypothesized Mean Difference 0df 8t Stat -0.78567P(T<=t) one-tail 0.227348t Critical one-tail 1.859548P(T<=t) two-tail 0.454696t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

Sprint HFS vs DJ @7 minpost VCA HFS DJMean 1.794444 1.791111Variance 0.005953 0.002761Observations 9 9Pearson Correlation 0.439534Hypothesized Mean Difference 0df 8t Stat 0.139347P(T<=t) one-tail 0.44631t Critical one-tail 1.859548P(T<=t) two-tail 0.89262t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

Sprint HFS vs DJ @10minpost VCA HFS DJMean 1.804444 1.768889Variance 0.004503 0.003286Observations 9 9Pearson Correlation 0.586371Hypothesized Mean Difference 0df 8t Stat 1.863112P(T<=t) one-tail 0.049727t Critical one-tail 1.859548P(T<=t) two-tail 0.099454t Critical two-tail 2.306004

111

Appendix xxvii.

Sprint Results HFS vs HBS t-tests.

t-Test: Paired Two Sample for Means

Sprint HFS vs HBS @4 minpost VCA HFS HBSMean 1.801111 1.76Variance 0.008386 0.00525Observations 9 9Pearson Correlation 0.864695Hypothesized Mean Difference 0df 8t Stat 2.653029P(T<=t) one-tail 0.014559t Critical one-tail 1.859548P(T<=t) two-tail 0.029119t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

Sprint HFS vs HBS @7 minpost VCA HFS HBSMean 1.794444 1.761111Variance 0.005953 0.003611Observations 9 9Pearson Correlation 0.756395Hypothesized Mean Difference 0df 8t Stat 1.980295P(T<=t) one-tail 0.041505t Critical one-tail 1.859548P(T<=t) two-tail 0.083011t Critical two-tail 2.306004

t-Test: Paired Two Sample for Means

Sprint HFS vs HBS @10 minpost VCA HFS HBSMean 1.804444 1.792222Variance 0.004503 0.003494Observations 9 9Pearson Correlation 0.630597Hypothesized Mean Difference 0df 8t Stat 0.670059P(T<=t) one-tail 0.260844t Critical one-tail 1.859548P(T<=t) two-tail 0.521688t Critical two-tail 2.306004

112

Appendix xxviii.

Sprint single factor Anova results.

113

Appendix xxix

CMJ Forwards DJ vs. control t-tests.

t-Test: Paired Two Sample for Means

CMJ Forwards DJ vs Control @4min post VCA Control DJMean 34.78044 39.62654Variance 35.81901 20.40266Observations 4 4Pearson Correlation 0.995903Hypothesized Mean Difference 0df 3t Stat -6.28724P(T<=t) one-tail 0.004063t Critical one-tail 2.353363P(T<=t) two-tail 0.008126t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

CMJ Forwards DJ vs Control @7min post VCA Control DJMean 35.47777 39.82314Variance 27.20162 10.86384Observations 4 4Pearson Correlation 0.072589Hypothesized Mean Difference 0df 3t Stat -1.45719P(T<=t) one-tail 0.120557t Critical one-tail 2.353363P(T<=t) two-tail 0.241115t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

CMJ Forwards DJ vs Control @10min post VCA Control DJMean 35.44218 42.28525Variance 26.61268 79.68075Observations 4 4Pearson Correlation 0.799717Hypothesized Mean Difference 0df 3t Stat -2.39552P(T<=t) one-tail 0.048131t Critical one-tail 2.353363P(T<=t) two-tail 0.096262t Critical two-tail 3.182446

114

Appendix xxx.

CMJ Forwards HFS vs. Control t-tests.

t-Test: Paired Two Sample for Means

CMJ Forwards HFS vs Control @4min post VCA Control HFSMean 34.78044 38.09919Variance 35.81901 17.68862Observations 4 4Pearson Correlation 0.920042Hypothesized Mean Difference 0df 3t Stat -2.47528P(T<=t) one-tail 0.044822t Critical one-tail 2.353363P(T<=t) two-tail 0.089644t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

CMJ Forwards HFS vs Control @7min post VCA Control HFSMean 35.47777 38.91768Variance 27.20162 24.03103Observations 4 4Pearson Correlation 0.863531Hypothesized Mean Difference 0df 3t Stat -2.58624P(T<=t) one-tail 0.040669t Critical one-tail 2.353363P(T<=t) two-tail 0.081338t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

CMJ Forwards HFS vs Control @10min post VCA Control HFSMean 35.44218 37.19029Variance 26.61268 12.19341Observations 4 4Pearson Correlation 0.989111Hypothesized Mean Difference 0df 3t Stat -1.96347P(T<=t) one-tail 0.072181t Critical one-tail 2.353363P(T<=t) two-tail 0.144362t Critical two-tail 3.182446

115

Appendix xxxi.

CMJ Forwards HBS vs. control t-tests

t-Test: Paired Two Sample for Means

CMJ Forwards HFS vs Control @4min post VCA Control HBSMean 34.78044 38.34609Variance 35.81901 12.39367Observations 4 4Pearson Correlation 0.938408Hypothesized Mean Difference 0df 3t Stat -2.42208P(T<=t) one-tail 0.046997t Critical one-tail 2.353363P(T<=t) two-tail 0.093994t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

CMJ Forwards HFS vs Control @7min post VCA Control HBSMean 35.47777 39.30521Variance 27.20162 11.3682Observations 4 4Pearson Correlation 0.975016Hypothesized Mean Difference 0df 3t Stat -3.70079P(T<=t) one-tail 0.01713t Critical one-tail 2.353363P(T<=t) two-tail 0.034259t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

CMJ Forwards HFS vs Control @10min post VCA Control HBSMean 35.44218 36.95053Variance 26.61268 11.10454Observations 4 4Pearson Correlation 0.910594Hypothesized Mean Difference 0df 3t Stat -1.19156P(T<=t) one-tail 0.159554t Critical one-tail 2.353363P(T<=t) two-tail 0.319109t Critical two-tail 3.182446

116

Appendix xxxii.

CMJ Forwards HBS vs. DJ t-tests.

t-Test: Paired Two Sample for Means

CMJ Forwards HbS vs DJ @4min post VCA HBS DJMean 38.34609 39.62654Variance 12.39367 20.40266Observations 4 4Pearson Correlation 0.953722Hypothesized Mean Difference 0df 3t Stat -1.63119P(T<=t) one-tail 0.100674t Critical one-tail 2.353363P(T<=t) two-tail 0.201347t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

CMJ Forwards HbS vs DJ @7min post VCA HBS DJMean 39.30521 39.82314Variance 11.3682 10.86384Observations 4 4Pearson Correlation -0.10709Hypothesized Mean Difference 0df 3t Stat -0.2088P(T<=t) one-tail 0.423989t Critical one-tail 2.353363P(T<=t) two-tail 0.847978t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

CMJ Forwards HBS vs DJ @10min post VCA HBS DJMean 36.95053 42.28525Variance 11.10454 79.68075Observations 4 4Pearson Correlation 0.585476Hypothesized Mean Difference 0df 3t Stat -1.42635P(T<=t) one-tail 0.124513t Critical one-tail 2.353363P(T<=t) two-tail 0.249027t Critical two-tail 3.182446

117

Appendix xxxiii.

CMJ Forwards HBS vs. HFS t-tests.

t-Test: Paired Two Sample for Means

CMJ Forwards HFS vs HBS @4 min post VCA HBS HFSMean 38.34609 38.09919Variance 12.39367 17.68862Observations 4 4Pearson Correlation 0.989655Hypothesized Mean Difference 0df 3t Stat 0.560567P(T<=t) one-tail 0.307116t Critical one-tail 2.353363P(T<=t) two-tail 0.614232t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

CMJ Forwards HFS vs HBS @7min post VCA HBS HFSMean 39.30521 38.91768Variance 11.3682 24.03103Observations 4 4Pearson Correlation 0.917102Hypothesized Mean Difference 0df 3t Stat 0.343783P(T<=t) one-tail 0.376847t Critical one-tail 2.353363P(T<=t) two-tail 0.753694t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

CMJ Forwards HFS vs HBS @10 min post VCA HBS HFSMean 36.95053 37.19029Variance 11.10454 12.19341Observations 4 4Pearson Correlation 0.910409Hypothesized Mean Difference 0df 3t Stat -0.33008P(T<=t) one-tail 0.381522t Critical one-tail 2.353363P(T<=t) two-tail 0.763044t Critical two-tail 3.182446

118

Appendix xxxiv.

CMJ Forwards DJ vs. HFS t-tests.

t-Test: Paired Two Sample for Means

CMJ Forwards HFS vs DJ @4 min post VCA DJ HFSMean 39.62654 38.09919Variance 20.40266 17.68862Observations 4 4Pearson Correlation 0.946627Hypothesized Mean Difference 0df 3t Stat 2.095675P(T<=t) one-tail 0.063544t Critical one-tail 2.353363P(T<=t) two-tail 0.127088t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

CMJ Forwards HFS vs DJ @7 min post VCA DJ HFSMean 39.82314 38.91768Variance 10.86384 24.03103Observations 4 4Pearson Correlation 0.014198Hypothesized Mean Difference 0df 3t Stat 0.308599P(T<=t) one-tail 0.388908t Critical one-tail 2.353363P(T<=t) two-tail 0.777815t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

CMJ Forwards HFS vs DJ @10min post VCA DJ HFSMean 42.28525 37.19029Variance 79.68075 12.19341Observations 4 4Pearson Correlation 0.711536Hypothesized Mean Difference 0df 3t Stat 1.478247P(T<=t) one-tail 0.117935t Critical one-tail 2.353363P(T<=t) two-tail 0.235871t Critical two-tail 3.182446

119

Appendix xxxv.

CMJ Forwards Single factor Anovas.

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 4 139.1217 34.78044 35.81901DJ 4 158.5062 39.62654 20.40266HFS 4 152.3968 38.09919 17.68862HBS 4 153.3844 38.34609 12.39367

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 51.24616 3 17.08205 0.791716 0.521572 3.490295Within Groups 258.9119 12 21.57599

Total 310.1581 15

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 4 141.9111 35.47777 27.20162DJ 4 159.2926 39.82314 10.86384HFS 4 155.6707 38.91768 24.03103HBS 4 157.2208 39.30521 11.3682

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 46.60274 3 15.53425 0.845808 0.494935 3.490295Within Groups 220.3941 12 18.36617

Total 266.9968 15

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 4 141.7687 35.44218 26.61268DJ 4 169.141 42.28525 79.68075HFS 4 148.7612 37.19029 12.19341HBS 4 147.8021 36.95053 11.10454

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 106.6339 3 35.54463 1.09713 0.388044 3.490295Within Groups 388.7741 12 32.39784

Total 495.408 15

at four mins post VCA

at seven mins post VCA

at ten mins post VCA

120

Appendix xxxvi.

CMJ Backs DJ vs. Control t-tests.

t-Test: Paired Two Sample for Means

Backs CMJ DJ vs Control @4 mins post VCA Control DJMean 39.05761 45.11415Variance 4.507797 11.15187Observations 5 5Pearson Correlation -0.12492Hypothesized Mean Difference 0df 4t Stat -3.24375P(T<=t) one-tail 0.015781t Critical one-tail 2.131847P(T<=t) two-tail 0.031562t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Backs CMJ DJ vs Control @7mins post VCA Control DJMean 40.99535 45.01152Variance 17.52973 21.68788Observations 5 5Pearson Correlation 0.558398Hypothesized Mean Difference 0df 4t Stat -2.1503P(T<=t) one-tail 0.048975t Critical one-tail 2.131847P(T<=t) two-tail 0.097949t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Backs CMJ DJ vs Control @10mins post VCA Control DJMean 41.10024 45.24872Variance 15.21624 50.09045Observations 5 5Pearson Correlation 0.275538Hypothesized Mean Difference 0df 4t Stat -1.31065P(T<=t) one-tail 0.130081t Critical one-tail 2.131847P(T<=t) two-tail 0.260162t Critical two-tail 2.776445

121

Appendix xxxvii.

CMJ Backs HFS vs. control t-tests.

t-Test: Paired Two Sample for Means

Backs CMJ HFS vs Control @4 mins post VCA Control HFSMean 39.05761 41.82734Variance 4.507797 13.15428Observations 5 5Pearson Correlation 0.716276Hypothesized Mean Difference 0df 4t Stat -2.40514P(T<=t) one-tail 0.036972t Critical one-tail 2.131847P(T<=t) two-tail 0.073943t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Backs CMJ HFS vs Control @7 mins post VCA Control HFSMean 40.99535 42.87674Variance 17.52973 22.6906Observations 5 5Pearson Correlation 0.987293Hypothesized Mean Difference 0df 4t Stat -4.59191P(T<=t) one-tail 0.005046t Critical one-tail 2.131847P(T<=t) two-tail 0.010092t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Backs CMJ HFS vs Control @10 mins post VCA Control HFSMean 41.10024 41.40014Variance 15.21624 13.40188Observations 5 5Pearson Correlation 0.988339Hypothesized Mean Difference 0df 4t Stat -1.07295P(T<=t) one-tail 0.171856t Critical one-tail 2.131847P(T<=t) two-tail 0.343712t Critical two-tail 2.776445

122

Appendix xxxviii.

CMJ Backs HBS vs. control t-tests.

t-Test: Paired Two Sample for Means

Backs CMJ HBS vs Control @4 mins post VCA Control HBSMean 39.05761 41.90209Variance 4.507797 10.31046Observations 5 5Pearson Correlation 0.496449Hypothesized Mean Difference 0df 4t Stat -2.24187P(T<=t) one-tail 0.044219t Critical one-tail 2.131847P(T<=t) two-tail 0.088438t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Backs CMJ HBS vs Control @7 mins post VCA Control HBSMean 40.99535 42.87301Variance 17.52973 22.03417Observations 5 5Pearson Correlation 0.972501Hypothesized Mean Difference 0df 4t Stat -3.62952P(T<=t) one-tail 0.011085t Critical one-tail 2.131847P(T<=t) two-tail 0.022169t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Backs CMJ HBS vs Control @10 mins post VCA Control HBSMean 41.10024 41.44006Variance 15.21624 21.83691Observations 5 5Pearson Correlation 0.990726Hypothesized Mean Difference 0df 4t Stat -0.78608P(T<=t) one-tail 0.237887t Critical one-tail 2.131847P(T<=t) two-tail 0.475773t Critical two-tail 2.776445

123

Appendix xxxix.

CMJ Backs HBS vs. DJ t-tests.

t-Test: Paired Two Sample for Means

Backs CMJ HBS vs DJ @4 mins post VCA HBS DJMean 41.90209 45.11415Variance 10.31046 11.15187Observations 5 5Pearson Correlation 0.325992Hypothesized Mean Difference 0df 4t Stat -1.88807P(T<=t) one-tail 0.066023t Critical one-tail 2.131847P(T<=t) two-tail 0.132045t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Backs CMJ HBS vs DJ @7 mins post VCA HBS DJMean 42.87301 45.01152Variance 22.03417 21.68788Observations 5 5Pearson Correlation 0.497497Hypothesized Mean Difference 0df 4t Stat -1.02016P(T<=t) one-tail 0.182666t Critical one-tail 2.131847P(T<=t) two-tail 0.365332t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Backs CMJ HBS vs DJ @10 mins post VCA HBS DJMean 41.44006 45.24872Variance 21.83691 50.09045Observations 5 5Pearson Correlation 0.256194Hypothesized Mean Difference 0df 4t Stat -1.14855P(T<=t) one-tail 0.157377t Critical one-tail 2.131847P(T<=t) two-tail 0.314754t Critical two-tail 2.776445

124

Appendix xl.

CMJ Backs HBS vs. HFS t-tests.

t-Test: Paired Two Sample for Means

Backs CMJ HBS vs HFS @4 mins post VCA HBS HFSMean 41.90209 41.82734Variance 10.31046 13.15428Observations 5 5Pearson Correlation 0.95879Hypothesized Mean Difference 0df 4t Stat 0.157051P(T<=t) one-tail 0.441407t Critical one-tail 2.131847P(T<=t) two-tail 0.882813t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Backs CMJ HBS vs HFS @7mins post VCA HBS HFSMean 42.87301 42.87674Variance 22.03417 22.6906Observations 5 5Pearson Correlation 0.993507Hypothesized Mean Difference 0df 4t Stat -0.01534P(T<=t) one-tail 0.494247t Critical one-tail 2.131847P(T<=t) two-tail 0.988494t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Backs CMJ HBS vs HFS @10mins post VCA HBS HFSMean 41.44006 41.40014Variance 21.83691 13.40188Observations 5 5Pearson Correlation 0.997698Hypothesized Mean Difference 0df 4t Stat 0.085001P(T<=t) one-tail 0.468172t Critical one-tail 2.131847P(T<=t) two-tail 0.936345t Critical two-tail 2.776445

125

Appendix xli.

CMJ backs DJ vs. HFS t-tests.

t-Test: Paired Two Sample for Means

Backs CMJ DJ vs HFS @4mins post VCA DJ HFSMean 45.11415 41.82734Variance 11.15187 13.15428Observations 5 5Pearson Correlation 0.229171Hypothesized Mean Difference 0df 4t Stat 1.697086P(T<=t) one-tail 0.082459t Critical one-tail 2.131847P(T<=t) two-tail 0.164917t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Backs CMJ DJ vs HFS @7mins post VCA DJ HFSMean 45.01152 42.87674Variance 21.68788 22.6906Observations 5 5Pearson Correlation 0.492844Hypothesized Mean Difference 0df 4t Stat 1.006067P(T<=t) one-tail 0.185652t Critical one-tail 2.131847P(T<=t) two-tail 0.371304t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Backs CMJ DJ vs HFS @10mins post VCA DJ HFSMean 45.24872 41.40014Variance 50.09045 13.40188Observations 5 5Pearson Correlation 0.194072Hypothesized Mean Difference 0df 4t Stat 1.177255P(T<=t) one-tail 0.15218t Critical one-tail 2.131847P(T<=t) two-tail 0.30436t Critical two-tail 2.776445

126

Appendix xlii.

CMJ Backs single factor anovas.

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 5 195.288 39.05761 4.507797DJ 5 225.5708 45.11415 11.15187HFS 5 209.1367 41.82734 13.15428HBS 5 209.5105 41.90209 10.31046

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 91.96294 3 30.65431 3.134034 0.054742 3.238872Within Groups 156.4977 16 9.781104

Total 248.4606 19

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 5 204.9768 40.99535 17.52973DJ 5 225.0576 45.01152 21.68788HFS 5 214.3837 42.87674 22.6906HBS 5 214.3651 42.87301 22.03417

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 40.40663 3 13.46888 0.641815 0.599187 3.238872Within Groups 335.7695 16 20.98559

Total 376.1761 19

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 5 205.5012 41.10024 15.21624DJ 5 226.2436 45.24872 50.09045HFS 5 207.0007 41.40014 13.40188HBS 5 207.2003 41.44006 21.83691

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 58.418 3 19.47267 0.774681 0.525021 3.238872Within Groups 402.1819 16 25.13637

Total 460.5999 19

at four mins post VCA

at seven mins post VCA

at ten mins post VCA

127

Appendix xliii.

Sprint Forwards DJ vs. Control t-tests.

t-Test: Paired Two Sample for Means

Forwards DJ vs Control @4min post VCA Control DJMean 1.88 1.8525Variance 0.001533 0.002892Observations 4 4Pearson Correlation 0.77568Hypothesized Mean Difference 0df 3t Stat 1.616017P(T<=t) one-tail 0.102254t Critical one-tail 2.353363P(T<=t) two-tail 0.204508t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

Forwards DJ vs Control @7min post VCA Control DJMean 1.9075 1.825Variance 0.005092 0.001367Observations 4 4Pearson Correlation 0.991944Hypothesized Mean Difference 0df 3t Stat 4.714286P(T<=t) one-tail 0.009036t Critical one-tail 2.353363P(T<=t) two-tail 0.018072t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

Forwards DJ vs Control @10 min post VCA DJ DJMean 1.81 1.825Variance 0.001 0.001367Observations 4 4Pearson Correlation 0.826886Hypothesized Mean Difference 0df 3t Stat -1.44115P(T<=t) one-tail 0.122597t Critical one-tail 2.353363P(T<=t) two-tail 0.245194t Critical two-tail 3.182446

128

Appendix xliv.

Sprint forwards HFS vs. control t-tests.

t-Test: Paired Two Sample for Means

Forwards HFS vs Control @4min post VCA HFS DJMean 1.8725 1.825Variance 0.006158 0.001367Observations 4 4Pearson Correlation 0.833017Hypothesized Mean Difference 0df 3t Stat 1.831104P(T<=t) one-tail 0.082244t Critical one-tail 2.353363P(T<=t) two-tail 0.164489t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

Forwards HFS vs Control @7min post VCA HFS DJMean 1.84 1.825Variance 0.002333 0.001367Observations 4 4Pearson Correlation 0.653322Hypothesized Mean Difference 0df 3t Stat 0.811503P(T<=t) one-tail 0.238242t Critical one-tail 2.353363P(T<=t) two-tail 0.476484t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

Forwards HFS vs Control @10min post VCA Control HFSMean 1.8925 1.8475Variance 0.004892 0.006425Observations 4 4Pearson Correlation 0.887418Hypothesized Mean Difference 0df 3t Stat 2.434508P(T<=t) one-tail 0.046477t Critical one-tail 2.353363P(T<=t) two-tail 0.092955t Critical two-tail 3.182446

129

Appendix xlv.

Sprint forwards HBS vs. control t-tests.

t-Test: Paired Two Sample for Means

Forwards HBS vs Control @4min post VCA Control HBSMean 1.88 1.805Variance 0.001533 0.003367Observations 4 4Pearson Correlation 0.42546Hypothesized Mean Difference 0df 3t Stat 2.753955P(T<=t) one-tail 0.035254t Critical one-tail 2.353363P(T<=t) two-tail 0.070508t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

Forwards HBS vs Control @7min post VCA Control HBSMean 1.9075 1.81Variance 0.005092 0.002067Observations 4 4Pearson Correlation 0.945369Hypothesized Mean Difference 0df 3t Stat 6.090777P(T<=t) one-tail 0.004444t Critical one-tail 2.353363P(T<=t) two-tail 0.008889t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

Forwards HBS vs Control @10min post VCA Control HBSMean 1.8925 1.8325Variance 0.004892 0.004892Observations 4 4Pearson Correlation 0.788756Hypothesized Mean Difference 0df 3t Stat 2.639648P(T<=t) one-tail 0.03884t Critical one-tail 2.353363P(T<=t) two-tail 0.077679t Critical two-tail 3.182446

130

Appendix xlvi.

Sprint forwards HBS vs. HFS t-tests.

t-Test: Paired Two Sample for Means

Forwards HBS v HFS @4min post VCA HBS HFSMean 1.805 1.8725Variance 0.003367 0.006158Observations 4 4Pearson Correlation 0.772324Hypothesized Mean Difference 0df 3t Stat -2.70451P(T<=t) one-tail 0.036752t Critical one-tail 2.353363P(T<=t) two-tail 0.073505t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

Forwards HBS v HFS @7min post VCA HBS HFSMean 1.81 1.84Variance 0.002067 0.002333Observations 4 4Pearson Correlation 0.77415Hypothesized Mean Difference 0df 3t Stat -1.89737P(T<=t) one-tail 0.077015t Critical one-tail 2.353363P(T<=t) two-tail 0.154031t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

Forwards HBS v HFS @10min post VCA HBS HFSMean 1.8325 1.8475Variance 0.004892 0.006425Observations 4 4Pearson Correlation 0.435533Hypothesized Mean Difference 0df 3t Stat -0.37403P(T<=t) one-tail 0.366627t Critical one-tail 2.353363P(T<=t) two-tail 0.733255t Critical two-tail 3.182446

131

Appendix xlvii.

Sprint forwards HBS vs. DJ t-tests.

t-Test: Paired Two Sample for Means

Forwards HBS vs DJ @4min post VCA HBS DJMean 1.805 1.8525Variance 0.003367 0.002892Observations 4 4Pearson Correlation 0.614289Hypothesized Mean Difference 0df 3t Stat -1.92916P(T<=t) one-tail 0.074645t Critical one-tail 2.353363P(T<=t) two-tail 0.14929t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

Forwards HBS vs DJ @7min post VCA HBS DJMean 1.81 1.825Variance 0.002067 0.001367Observations 4 4Pearson Correlation 0.97187Hypothesized Mean Difference 0df 3t Stat -2.32379P(T<=t) one-tail 0.051364t Critical one-tail 2.353363P(T<=t) two-tail 0.102728t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

Forwards HBS vs DJ @10min post VCA Control HBSMean 1.8925 1.8325Variance 0.004892 0.004892Observations 4 4Pearson Correlation 0.788756Hypothesized Mean Difference 0df 3t Stat 2.639648P(T<=t) one-tail 0.03884t Critical one-tail 2.353363P(T<=t) two-tail 0.077679t Critical two-tail 3.182446

132

Appendix xlviii.

Sprint forwards DJ vs. HFS t-tests.

t-Test: Paired Two Sample for Means

Forwards HFS vs DJ @4min post VCA DJ HFSMean 1.8525 1.8725Variance 0.002892 0.006158Observations 4 4Pearson Correlation 0.859018Hypothesized Mean Difference 0df 3t Stat -0.94281P(T<=t) one-tail 0.207666t Critical one-tail 2.353363P(T<=t) two-tail 0.415333t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

Forwards HFS vs DJ @7min post VCA DJ HFSMean 1.825 1.84Variance 0.001367 0.002333Observations 4 4Pearson Correlation 0.653322Hypothesized Mean Difference 0df 3t Stat -0.8115P(T<=t) one-tail 0.238242t Critical one-tail 2.353363P(T<=t) two-tail 0.476484t Critical two-tail 3.182446

t-Test: Paired Two Sample for Means

DJ HFSMean 1.81 1.8475Variance 0.001 0.006425Observations 4 4Pearson Correlation 0.591772Hypothesized Mean Difference 0df 3t Stat -1.12747P(T<=t) one-tail 0.170788t Critical one-tail 2.353363P(T<=t) two-tail 0.341576t Critical two-tail 3.182446

133

Appendix xlix.

Sprint forwards single factor anovas.

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 4 7.52 1.88 0.001533DJ 4 7.41 1.8525 0.002892HFS 4 7.49 1.8725 0.006158HBS 4 7.22 1.805 0.003367

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 0.01365 3 0.00455 1.304659 0.318096 3.490295Within Groups 0.04185 12 0.003488

Total 0.0555 15

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 4 7.63 1.9075 0.005092DJ 4 7.3 1.825 0.001367HFS 4 7.36 1.84 0.002333HBS 4 7.24 1.81 0.002067

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 0.022219 3 0.007406 2.728319 0.090433 3.490295Within Groups 0.032575 12 0.002715

Total 0.054794 15

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 4 7.57 1.8925 0.004892DJ 4 7.24 1.81 0.001HFS 4 7.39 1.8475 0.006425HBS 4 7.33 1.8325 0.004892

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 0.014569 3 0.004856 1.128814 0.376381 3.490295Within Groups 0.051625 12 0.004302

Total 0.066194 15

4 minutes post VCA

at 7 minutes post VCA

at 10 min post VCA

134

Appendix L.

Sprint backs DJ vs. control t-tests.

t-Test: Paired Two Sample for Means

Forwards DJ vs Control @4min post VCA Control DJMean 1.81 1.79Variance 0.0056 0.00175Observations 5 5Pearson Correlation 0.319438Hypothesized Mean Difference 0df 4t Stat 0.611418P(T<=t) one-tail 0.286982t Critical one-tail 2.131847P(T<=t) two-tail 0.573965t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Forwards DJ vs Control @7min post VCA Control DJMean 1.774 1.764Variance 0.00293 0.00243Observations 5 5Pearson Correlation 0.51718Hypothesized Mean Difference 0df 4t Stat 0.438529P(T<=t) one-tail 0.341824t Critical one-tail 2.131847P(T<=t) two-tail 0.683648t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Forwards DJ vs Control @10min post VCA Control DJMean 1.794 1.736Variance 0.00193 0.00278Observations 5 5Pearson Correlation 0.893654Hypothesized Mean Difference 0df 4t Stat 5.432198P(T<=t) one-tail 0.002786t Critical one-tail 2.131847P(T<=t) two-tail 0.005572t Critical two-tail 2.776445

135

Appendix li.

Sprint backs HFS vs. control t-tests.

t-Test: Paired Two Sample for Means

Forwards HFS vs Control @4min post VCA Control HFSMean 1.81 1.744Variance 0.0056 0.00298Observations 5 5Pearson Correlation 0.758856Hypothesized Mean Difference 0df 4t Stat 3.025105P(T<=t) one-tail 0.019484t Critical one-tail 2.131847P(T<=t) two-tail 0.038967t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Forwards HFS vs Control @7min post VCA Control HFSMean 1.774 1.758Variance 0.00293 0.00642Observations 5 5Pearson Correlation -0.03228Hypothesized Mean Difference 0df 4t Stat 0.364579P(T<=t) one-tail 0.366941t Critical one-tail 2.131847P(T<=t) two-tail 0.733882t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Forwards HFS vs Control 10min post VCA Control HFSMean 1.794 1.77Variance 0.00193 0.00085Observations 5 5Pearson Correlation 0.487969Hypothesized Mean Difference 0df 4t Stat 1.371989P(T<=t) one-tail 0.120991t Critical one-tail 2.131847P(T<=t) two-tail 0.241982t Critical two-tail 2.776445

136

Appendix lii.

Sprint backs HBS vs. control t-tests.

t-Test: Paired Two Sample for Means

Forwards HBS vs Control @4min post VCA Control HBSMean 1.81 1.724Variance 0.0056 0.00433Observations 5 5Pearson Correlation 0.842772Hypothesized Mean Difference 0df 4t Stat 4.763099P(T<=t) one-tail 0.004442t Critical one-tail 2.131847P(T<=t) two-tail 0.008885t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Forwards HBS vs Control @7min post VCA Control HBSMean 1.774 1.722Variance 0.00293 0.00137Observations 5 5Pearson Correlation 0.656344Hypothesized Mean Difference 0df 4t Stat 2.845313P(T<=t) one-tail 0.023308t Critical one-tail 2.131847P(T<=t) two-tail 0.046616t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Forwards HBS vs Control @10min post VCA Control HBSMean 1.794 1.76Variance 0.00193 0.0004Observations 5 5Pearson Correlation 0.853596Hypothesized Mean Difference 0df 4t Stat 2.638912P(T<=t) one-tail 0.028823t Critical one-tail 2.131847P(T<=t) two-tail 0.057646t Critical two-tail 2.776445

137

Appendix liii.

Sprint backs HBS vs. DJ t-tests.

t-Test: Paired Two Sample for Means

Forwards HBS vs DJ @4min post VCA HBS DJMean 1.724 1.79Variance 0.00433 0.00175Observations 5 5Pearson Correlation 0.581242Hypothesized Mean Difference 0df 4t Stat -2.75P(T<=t) one-tail 0.025687t Critical one-tail 2.131847P(T<=t) two-tail 0.051374t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Forwards HBS vs DJ @7min post VCA HBS DJMean 1.722 1.764Variance 0.00137 0.00243Observations 5 5Pearson Correlation 0.008221Hypothesized Mean Difference 0df 4t Stat -1.52955P(T<=t) one-tail 0.100433t Critical one-tail 2.131847P(T<=t) two-tail 0.200865t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Forwards HBS vs DJ @10min post VCA HBS DJMean 1.76 1.736Variance 0.0004 0.00278Observations 5 5Pearson Correlation 0.995719Hypothesized Mean Difference 0df 4t Stat 1.632993P(T<=t) one-tail 0.088904t Critical one-tail 2.131847P(T<=t) two-tail 0.177808t Critical two-tail 2.776445

138

Appendix liv.

Sprint backs HBS vs. HFS t-tests.

t-Test: Paired Two Sample for Means

Forwards HFS vs HBS @4min post VCA HBS HFSMean 1.724 1.744Variance 0.00433 0.00298Observations 5 5Pearson Correlation 0.85047Hypothesized Mean Difference 0df 4t Stat -1.29099P(T<=t) one-tail 0.133132t Critical one-tail 2.131847P(T<=t) two-tail 0.266265t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Forwards HFS vs HBS @7min post VCA HBS HFSMean 1.722 1.758Variance 0.00137 0.00642Observations 5 5Pearson Correlation 0.583335Hypothesized Mean Difference 0df 4t Stat -1.22333P(T<=t) one-tail 0.144172t Critical one-tail 2.131847P(T<=t) two-tail 0.288343t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Forwards HFS vs HBS @10min post VCA HBS HFSMean 1.76 1.77Variance 0.0004 0.00085Observations 5 5Pearson Correlation 0.085749Hypothesized Mean Difference 0df 4t Stat -0.65938P(T<=t) one-tail 0.27284t Critical one-tail 2.131847P(T<=t) two-tail 0.54568t Critical two-tail 2.776445

139

Appendix lv.

Sprint backs DJ vs. HFS t-tests.

t-Test: Paired Two Sample for Means

Forwards HFS vs DJ @4min post VCA DJ HFSMean 1.79 1.744Variance 0.00175 0.00298Observations 5 5Pearson Correlation 0.08758Hypothesized Mean Difference 0df 4t Stat 1.563144P(T<=t) one-tail 0.096529t Critical one-tail 2.131847P(T<=t) two-tail 0.193057t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Forwards HFS vs DJ @7min post VCA DJ HFSMean 1.764 1.758Variance 0.00243 0.00642Observations 5 5Pearson Correlation -0.02279Hypothesized Mean Difference 0df 4t Stat 0.141186P(T<=t) one-tail 0.447274t Critical one-tail 2.131847P(T<=t) two-tail 0.894548t Critical two-tail 2.776445

t-Test: Paired Two Sample for Means

Forwards HFS vs DJ @10min post VCA DJ HFSMean 1.736 1.77Variance 0.00278 0.00085Observations 5 5Pearson Correlation 0.130106Hypothesized Mean Difference 0df 4t Stat -1.33771P(T<=t) one-tail 0.125991t Critical one-tail 2.131847P(T<=t) two-tail 0.251982t Critical two-tail 2.776445

140

Appendix lvi.

Sprint backs single factor anovas.

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 5 9.05 1.81 0.0056DJ 5 8.95 1.79 0.00175HFS 5 8.72 1.744 0.00298HBS 5 8.62 1.724 0.00433

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 0.02378 3 0.007927 2.162801 0.132338 3.238872Within Groups 0.05864 16 0.003665

Total 0.08242 19

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 5 8.87 1.774 0.00293DJ 5 8.82 1.764 0.00243HFS 5 8.79 1.758 0.00642HBS 5 8.61 1.722 0.00137

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 0.007695 3 0.002565 0.780228 0.522109 3.238872Within Groups 0.0526 16 0.003288

Total 0.060295 19

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Control 5 8.97 1.794 0.00193DJ 5 8.68 1.736 0.00278HFS 5 8.85 1.77 0.00085HBS 5 8.8 1.76 0.0004

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 0.00866 3 0.002887 1.93736 0.164265 3.238872Within Groups 0.02384 16 0.00149

Total 0.0325 19

at 4 min post VCA

at 7 mins post VCA

at ten mins post VCA

141

Appendix lvii

CMJ VCA heights worst, best and mean.

142

143

Appendix lviii.

VCA 10m sprint times worst, best and mean.

144

145

Appendix lix.

CMJ mean times forwards and backs per VCA.

146

Appendix lx.

10m Sprint performance forwards and backs per VCA.

147

Appendix lxi.

Map of gymnasium.

148

10m sprint

CMJ

DJ

Deceleration area

GYM