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2013  Year-­End  Newsletter  

 

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 Table of Contents.............................................................................................................. p. 2 About ................................................................................................................................ p. 2 The Importance and Difficulty of Scouting Make-up...................................................... p. 3 Mike Moustakas and Why Royals Fans Shouldn’t Panic ................................................ p. 6 Player Report: Marcust Stroman, RHP (TOR, AA) ......................................................... p. 11 Oakland A’s and Winning Without Good Starting Pitching............................................ p. 12 What’s the Deal with Shutdown Innings? ....................................................................... p. 14 Who is the Best Catcher in the American League? ........................................................ p. 16 Comparing the 3 WAR Measures ................................................................................... p. 20 A Belated Farewell to the Toddfather .............................................................................. p. 28 Chris Denorfia and the Importance of Making Contact .................................................. p. 31 How Much is a First Round Pick Worth? ........................................................................ p. 33 Batting Leadoff Contact Info .......................................................................................... p. 39    

About  Batting Leadoff is a website dedicated to providing readers with premium baseball content. From analyzing Pitch F/X data, to providing extensive scouting reports on coveted prep players, to breaking down your favorite team’s strengths and weaknesses, Batting Leadoff covers it all. Our collection of blogs is comprehensively researched and skillfully written to give readers the most informative and entertaining insights on the web.

Further, Batting Leadoff serves as a showcase for talented people looking to start careers in baseball operations, statistical analysis, and scouting. Through the Batting Leadoff space, writers are able to show professional personnel how they could one day contribute to an MLB Front Office.

 

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The Importance and Difficulty of Scouting Make-up (9/18/13)

By Hudson Belinsky

Makeup is without a doubt the most

fascinating aspect of scouting. There are so

many different ideas about what constitutes

good makeup, and scouts rarely have the

opportunity to truly tap into a player’s

makeup. The learning curve of scouting

players’ tools, in my opinion, takes

approximately five years to master. After five

years, a scout should quickly be able to

develop a very strong understanding of a

player’s tools profile. I’ve been scouting for

about three years, and I

feel like I’m almost there.

But makeup presents a

unique challenge to

scouts, regardless of how

long they’ve been

scouting. Today, I’ll offer

up what I’ve gathered

about makeup so far, but

my conception of makeup

is likely to change

throughout my scouting

career. This will serve as a jumping off point

for a further discussion of evaluating the

most important aspect of baseball player

projection.

Makeup has several components. Among

these components are overall character,

coachability, the ability to successfully

manage and cope with failure, respect for

the game, baseball instinct, hustle, pre-game

preparation, desire to succeed,

understanding of work ethic, and willingness

to put in hard work. It’s very difficult to

evaluate all of these for a player, but you can

usually draw conclusions from a few of

them.

At minor league games, there is always a

group of pitchers charting the game.

Examining these pitchers, from a distance,

provides excellent insight. You can quickly

determine who is a baseball rat and who is

just a person who plays baseball. This

summer, I sat in on a series between the

Reading Fightin’ Phils and the Trenton

Thunder. I had heard good things about

Phillies pitching prospect Jesse Biddle for

quite some time, and my observations of

 

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Biddle during that series make me believe he

will have little trouble actualizing his

physical gifts.

During that series, I constantly overheard

Biddle discussing what exactly was

happening on the field, noting that Pitcher X

needed to establish this or that

pitch, was flying open, was creating

velocity separation between his

fastball and changeup correctly or

incorrectly, etc. Biddle has a desire

to understand the game as best as he

possibly can so that he can apply what he

sees to his own game. He understands what

he needs to do to make himself a better

baseball player, and has an intense desire to

develop himself. Biddle is a baseball rat.

Furthermore, I got to see how Biddle

interacted with fans in search of his

autograph. The 21-year-old would toy with

the annoying dudes who go around getting

prospects’ autographs before reluctantly

signing a ball or a card. At one point, Biddle

interacted with a young child and the child’s

parents. Not only did Biddle sign

autographs, he inquired about the family’s

experience at the game and thanked them

for their fandom.

Before I had gone to see Biddle, one scout

told me about how he saw Biddle show up to

the field extremely early one day to work on

his hitting. This is a player who thoroughly

understands what being a Major League

baseball player entails, and he passes every

single makeup test. People throw around the

term “80 makeup” rather liberally, but

Biddle is one of the few players whose

makeup would receive the highest mark on

my scouting report. I have little doubt about

Biddle’s ability to reach the majors and

succeed

there.

In that

same

series, I

saw a very enigmatic example of makeup

that still makes me uneasy. Slade Heathcott,

first of all, is a lunatic. He’s very passionate

about his faith, yet very easily pushed to

extreme anger. His desire to succeed is

undeniable, but he has issues dealing with

failure that will follow him throughout his

career, and may prevent him from

actualizing the physical gifts that made him

the Yankees’ first round pick in 2009.

At one point in the series, Slade took a called

third strike that he didn’t think should have

been called. He quickly erupted at the

umpire and got himself ejected from the

game, but his ejection did not stop his

screaming. Slade struggles to contextualize

each at-bat as just another at-bat, and hates

failure perhaps more than he loves success.

In that same series, Slade chased down every

fly ball as if his life depended on catching it,

and quickly reacted to the game as it came to

him.

“Biddle,  has  a  desire  to  understand  the  game  as  best  as  he  possibly  can  so  that  he  can  apply  what  he  sees  to  his  own  game”    

 

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Judging by the way Slade plays the game (as

well as his muscular composition), I don’t

think there’s any issue with work ethic or

desire to succeed. The issues are with

baseball instincts, failure management, and

perhaps coachability, given his well below-

average approach to hitting. These may be

issues that he can overcome; players are

kids, and kids grow up. But these issues

could also prevent Heathcott from being

anything more than a fourth outfielder.

There are people that would conclude that

Slade has poor makeup. I can see the

argument, but this is where evaluating

makeup gets really difficult. There’s also an

argument that his raw personality traits

could turn him into an All-Star some day.

It’s up to each scout to

place value in certain

traits over others, and

players like Biddle are

a very rare breed;

most of the time a

player has faults in

one way or another

that cloud the overall

judgment of his

makeup. I’m still in

the process of valuing

makeup traits, and I

believe that this will

be an ever-evolving process throughout my

career.

Before I close this article, I’m going to offer a

few thoughts on everyone’s favorite

prospect, Byron Buxton. As a scout, it’s

usually wise to search for a player’s flaws;

Buxton doesn’t have any. Buxton has varying

degrees of strength, but every one of his

tools will be above-average, and, in my

opinion, he has a chance for three 80 tools

(arm, speed, defense), a 70 hit tool, and a 60

power tool. I firmly believe that Buxton will,

in short order, be in the conversation as the

best player in baseball, but that has as much

to do with his makeup as it does his tools.

I had at least three chances to observe

Buxton up close in 2013. In Spring Training,

Buxton showed me every tool and spent

plenty of time discussing the game with his

coaches, probably becoming something of a

nuisance to seasoned baseball minds like

Paul Molitor and Doug Mientkiewicz. I saw

about 20 games in Cedar Rapids, and spent

 

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a decent amount of time close to the Twins’

Low-A affiliate there. Buxton was always

around, waiting for someone to throw to him

in the cages. When I spoke to him, he

expressed a burning desire to get out of

Low-A; not because he wanted to escape

the City of Five Smells, but because he

wanted to reach the Major Leagues as soon

as he possibly could. My final interaction

with Buxton was a brief encounter that took

place in the Team USA clubhouse at the

Futures Game. As I walked by him, sure that

he wouldn’t remember me, I asked him if he

was happy to be out of Cedar Rapids. He

smiled and said, “One step closer.”

Byron Buxton was born to play baseball. Not

football, not basketball. I wonder if scouts

realized this when he was an amateur, or if

the tools alone were what made Buxton the

no. 2 overall pick in 2012. Regardless,

Buxton has the makeup to actualize his

tools, and should serve as an example to

young players everywhere.

Makeup is incredibly difficult to get your

finger on, but it’s so important to really

understand a player’s makeup before

bringing him into your organization. If you

can truly evaluate makeup, you can make

yourself a true scout.

Mike Moustakas and Why Royals Fans Shouldn’t Panic (9/20/13)

By Alex Smith

After hitting 24 homers for Chatsworth High

School and winning Baseball America’s High

School Player of the Year Award, Mike

Moustakas became the 2nd overall selection

of the 2007 Major League Baseball First-

Year Player Draft.

The talented third base prospect then

breezed through the Royals farm system,

clubbing 80 combined home runs in 2010

 

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and 2011 between Double-A and Triple-A. Moustakas had become a staple of top prospect lists

floating around the web and Royals fans were already dreaming of watching him hit 40+ home

runs a year for their favorite team.

Two and half full seasons into his career, that dream has yet to become a reality. After what

seemed to be a promising 2012 campaign, in which Moustakas hit 20 home runs, he has regressed

in 2013- posting a slash line of .234/.288/.362 and only hitting 11 long-balls.

However, while watching Moustakas might be frustrating for the Kansas City faithful, I urge them

to be patient. Moustakas’ struggles are not that uncommon for power hitting prospects.

To prove this point, let’s take a look at Major League Baseball’s current home run leaderboard. At

the top is Chris Davis, a player who like Moustakas was once one of baseball’s most heralded

prospects. With raw power that grades close to 80 on the scout scale, Davis rose to the big

leagues with grand expectations. However, it has taken Davis six years to actually live up to his

potential. Only now, at age 27, has Davis finally been able to transfer his raw power potential into

monstrous in-game power at the big league level.

Davis is not the only player this delay of success applies to. If you keep going down the home run

leaderboard, only Miguel Cabrera, Mark Trumbo, Alfonso Soriano, and Evan Longoria reached

their power potential at an early age amongst the top ten.

Edwin Encarnacion, who has hit 36 home runs so far this year, never posted a slugging

percentage above .486 in his first seven seasons. Paul Goldschmidt only hit 20 home runs last

year in almost 600 at bats before embarking on his gargantuan 2013 campaign. Pedro Alvarez

slugged merely .289 in his second major league season before blowing up in 2012. And even the

burly Adam Dunn had home run totals in the 20s during his first couple of seasons before surging

to hit 46 home runs in 2004. The list of talented sluggers who took time to get used to major

league pitching goes on and on, and its very conceivable that Moustakas will one day make his

way onto that list.

As I was thinking about this phenomenon over the course of the week, I decided to discuss it with

one of my teammates, Ryan Karl. Ryan is a transfer from Louisville and will be one of the premier

sluggers on the Cornell baseball team this spring.

What he said to address the point makes sense. If you’re a power hitter, you’re approach at the

plate every single time is to hit the ball really, really hard. You don’t care if you strike out, because

that’s an acceptable side effect of having this approach.

 

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Because of this, it becomes more difficult to square balls up off the center of the bat, usually a

prerequisite for a home run,

especially if pitchers already

know your weaknesses and

have the stuff and ability to

pitch to them.

As hitters go up in level, the

quality of the pitching

improves, pitchers can locate

more precisely, have

increased movement, and

greater velocity. Additionally,

scouting reports on each

individual hitter become

more advanced. Pitchers not only know more about a hitter, but they also have the ability to use

that knowledge to get the hitter out.

While every level of play has its own learning curve, the learning curve in the majors is the largest

since it’s baseball in its pinnacle form, and major league pitchers are considerably better than

their minor league counterparts.

Let’s say for example, a team figures out a hitter often whiffs at breaking balls outside of the

zone. In the minors, a pitcher may be able to throw a breaking ball that starts in the zone and

breaks out of it, but it will likely have less sharp-breaking action and have looser spin than a

major league breaking ball. This gives a hitter more time to identify the pitch and choose not to

swing.

In the majors though, the pitcher is likely to throw a breaking ball with incredible sharp-breaking

action and much tighter spin, giving a hitter less time to identify the pitch, thus increasing his

likelihood to swing and miss or make weak contact. Even the slightest difference in sharpness and

tightness of spin can make it significantly harder for a batter to get his barrel to the ball.

Pitchers ability to read these scouting reports and pitch to them, makes it ever more important for

hitters to study their own scouting reports of the pitcher and make their own adjustments. And

sometimes, particularly if a hitter has dominated at every previous level, this is not a skill he has

developed and a skill that it will take time for him to develop.

 

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If we go back to the Chris Davis example, he has always had a problem with striking out far too

frequently. However, in the minors, this issue did not take away from him doing everything else

he does well. In the majors though it was a different story, and early in his career it caused him

grave issues. Despite having relatively successful 2008 and 2009 seasons, Davis’ swinging strike

percentages were 17.2% and 19.1% respectively. To put that in perspective, Pedro Alvarez

currently leads the majors with a swinging strike percentage of 16.8%.

In 2010, Davis attempted to make an adjustment to make contact more and his swinging strike

percentage dropped to 11%. This drastic decrease though caused Davis to sacrifice his in-game

power, and he posted a lowly isolated power of .100.

It has taken quite a few years, but Davis has seemingly finally found the right balance between

driving the ball and simply making contact. Learning how to make proper adjustments to your

game is extremely difficult, but Davis through persistence and dedication to his craft has reached

the point where he’s finally tapping into his massive power potential.

Moustakas’ problem, unlike Davis, is not

swinging and missing; it’s chasing bad

pitches out of the zone and hitting the ball

weakly. Brooks Baseball classifies pitches

into three categories, Fastballs (4-seam, 2-

seam, etc.), Breaking Pitches (curveball,

slider, etc.), and Off-Speed pitches

(changeups, splitters, etc.). They also

compute a statistic titled “strike-zone

discrimination” comparing a hitter’s swing

percentages against different pitch types

in and out of the zone. Against breaking

pitches and off-speed pitches, Moustakas’ strike-zone discrimination numbers are 0.54 and 0.82

respectively, well below the target of having a strike-zone discrimination around or greater than 1.

While this issue is plaguing Moustakas horribly right now, it is something he can certainly fix,

since his talent is still there. It’s just going to take a lot of time and hard work. Like Davis, don’t

expect Moustakas’ career to be a progression. His struggles could very well carry over into

2014. But if the Royals are willing to be patient with him and he’s willing to be patient with

himself, it wouldn’t be all to surprising to witness a Mike Moustakas power explosion in Kansas

City in the future. He does have four years of team control left after all.

 

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Links/Partners

 

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Player Report: Marcus Stroman, RHP (TOR, AA) (9/10/13) Each week Batting Leadoff posts two scouting reports on players Featured Blogger Mike Parnell has seen this year along with video of the player. The scouting report features a summary of the player and his grades for each tool or pitch. Occasionally, Mike adds additional comments on the player if he feels it is necessary. Here is Mike’s scouting report on Toronto Blue Jays top prospect Marcus Stroman from earlier this year. For a larger version of the report visit this link: http://battingleadoff.com/2013/09/10/player-report-marcus-stroman-rhp-tor-aa/

 

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The Oakland A’s and Winning Without Good Starting Pitching (9/23/13)

By Chris Moran

The Oakland Athletics starting pitchers have posted a 105 xFIP-, and accumulated 10.7 WAR, figures that are

21st and 16th in MLB, respectively. As the below table shows, pitching independent stats do not show much

love for the Athletics starting pitchers, with their walk rate being the only number not in the bottom half of

the league.

Stat xFIP- K% BB% GB% WAR

Number 105 18.1 6.8 38.8 10.7

Rank T-21st 19th T-7th 30th 16th

However, the A’s starting pitchers fare much better in terms of defense-dependent stats, and with the

exception of Brett Anderson, they have managed to stay healthy.

Stat ERA- BABIP LOB% HR/FB RA9 WAR Innings

Number 96 0.272 73.8 9.6 13.2 927.1

Rank T-6th 1st T-8th T-4th T-7th 6th

Finally, to give you an idea of how pedestrian their staff has been (at least in terms of sabermetric numbers,

more on that later), I prepared a table of the A’s starting pitchers this year.

Pitcher Innings xFIP- K% BB% BABIP LOB% HR/FB WAR

Bartolo Colon 178.1 102 14.0 3.7 0.297 78.9 5.5 3.5

Dan Straily 145.2 112 19.4 9.0 0.263 70.5 8.5 1.6

Jarrod Parker 186.2 110 16.6 8.0 0.262 73.6 9.5 1.6

A.J. Griffin 195 106 20.5 6.6 0.240 77.5 12.4 1.4

 

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Sonny Gray 50 74 24.8 6.9 0.267 71.7 7.7 1.3

Tommy Milone 148 108 17.8 6.0 0.286 72.3 10.7 1.1

Brett Anderson 23.2 91 21.3 11.7 0.366 55.6 17.6 0.1

The Coliseum is the 8th-most difficult park in

terms of hitting home runs, and the A’s fly ball

rate of 41.6% leads MLB (no other team gets a

higher percentage of fly balls than groundballs).

Gray has been excellent in the six starts he has

made, with a 52.2 GB%. Other than Anderson

and Gray, no A’s starting pitcher has a GB%

above 42.4%. Put a team full of fly ball pitchers in

a big ballpark with good outfield defense, and you

have a recipe for

overachieving

peripherals. This helps

explain how the A’s starting

pitchers have managed to

put together a 3.73 ERA

despite a 4.20 xFIP, easily

the biggest positive gap of

any team.

Except for newcomer Gray

(18th overall in 2011), the

A’s have not used high draft picks to get these

pitchers. In fact, since 2003, the A’s have only

selected four pitchers out of their nineteen first

round picks. Colon was an inexpensive free agent

signing. Parker and Anderson were acquired in

trades with the Diamondbacks where the A’s gave

up Haren and Trevor Cahill after getting some

solid years out of those arms. Milone, a former

10th-round pick, was acquired as part of the Gio

Gonzalez trade. Straily was a 24th-round pick in

2009. Griffin was a 13th-round pick in 2010. If

you click on the links, (or just keep reading) you

will find out that one other player from those two

rounds has reached the majors. (Keith Butler,

who managed a 5.44 xFIP in 20 innings with the

Cardinals this year). Most players drafted in

those rounds are no longer playing affiliated

baseball, not starting games for a playoff-bound

team.

As the A’s starting pitchers currently have a 105

xFIP- and they have clinched the AL West, I

thought it would be interesting to see how many

teams had made the playoffs with their starting

pitchers possessing a cumulative xFIP- of 105 or

worse. As xFIP- only goes back to 2002, the

search was restricted to 2002-2013.

The 2011 Diamondbacks finished 94-68, winning

the NL West. Diamondbacks starting pitchers

 

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posted a 107 xFIP-, good for 25th in MLB. Thanks

to some innings eaters, they tallied 12.0 WAR,

15th in MLB. Like the A’s, the Diamondbacks had

a staff of fly ball pitchers, as they posted the

lowest groundball percentage in the league.

Despite playing at cozy Chase Field, their HR/FB

ratio was only 9.8%, due in part to their rotation

getting the fourth-highest infield-fly rate. They

also had the third-lowest walk rate in MLB.

Featuring an outfield of Chris Young, Gerardo

Parra, and Justin Upton, the Diamondbacks led

MLB in UZR. The rotation featured excellent

seasons from Ian Kennedy and Daniel Hudson,

with a side of Josh Collmenter. Nobody else

reached +1 WAR. The Diamondbacks beat their

Pythagorean record by +6 wins. Their 28-16

record in 1-run games was the best in the league.

And that’s it. No other team has made the

playoffs since 2002 after having their starting

pitchers tally a 105 or worse xFIP-. The A’s

success this year isn’t quite unprecedented, but

it’s close. Unlike the Diamondbacks, the A’s have

played to their Pythagorean record. Rather than

emphasizing velocity (A’s starters are 28th in

fastball velocity) Billy Beane has sought

out young strike throwers who can stay

healthy (and Colon, an old strike thrower). By

putting them in a big ballpark with good

outfielders, the A’s have managed to make below-

average starting pitchers look solid. Billy Beane

and the A’s are finding a way to beat sabermetric

pitching stats such as xFIP and FIP. By drafting

pitchers later and making the most out of less

than electric arms, they have managed to insure

themselves against the risks associated with

young pitchers.

What’s the Deal With Shutdown Innings (11/13/13)

By Francesco Padulo

It is the bottom of the third inning in Game Four of the 2013 NLCS. The Cardinals are leading the Dodgers

two games to one and looking

to gain a decisive advantage

by taking Game Five in

LA. Lance Lynn takes the

mound for his third inning of

work after his team has given

him a three run cushion in

the top half of the frame. At

this point, most people

probably feel like the

Cardinals are completely in

 

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control. TBS announcer Ron Darling is not one of those people. He reminds millions of viewers that Lynn

has a National League-worst 6.67 ERA in all-important “shutdown innings.” But, is he right? Is the

shutdown inning really that important? Does the shutdown inning even exist?

In the age of sabermetrics, many people in baseball would probably argue that a shutdown inning is no more

important than any other inning at that given point in the game. You won’t find any win expectancy or

leverage index statistics to support the shutdown inning’s existence; believe me I tried. While

sabermetricians may be correct given the average regular season game, I argue that they are wrong when it

comes to the playoffs.

To begin to analyze

shutdown innings, we

first need to define

them. This can prove

to be easier said than

done, as every situation is slightly different, and

different people place a different amount of

importance on a given situation. However, for the

purposes of this article, I am defining a shutdown

inning as the half inning immediately after a

team scores to tie the game, take the lead, or cut a

deficit to three runs or fewer.

Once we establish this definition, we can begin to

quantify and evaluate shutdown innings. In the

2013 Postseason there were a total of 84

shutdown innings. Out of those innings, pitchers

were successful in shutting down the other team’s

offense 69 times, and they failed just 15 times. Of

the 15 times that a team failed in a shutdown

inning, 11 of them went on to lose the game. In 2

of the 4 games where a team failed in a shutdown

inning and went on to win the game, the

opposing team also failed in a shutdown inning.

Although it is not the largest sample size, these

statistics clearly show that while succeeding in a

shutdown inning does not guarantee a win, not

succeeding in those innings

certainly hurts a team’s

chances.

The reason behind this? I say

it’s mental. As a player myself, I know that guys

don’t think about shutdown innings when they

are out on the field or up at the plate, but in

baseball, it is easier to play when you are ahead.

You can take more risks and be more aggressive

when your team has the lead, and in doing so a

lot more pressure is placed on the opponent to

execute and play fundamentally sound baseball.

Additionally, in the playoffs especially, once a

team falls behind they begin to put extra pressure

on themselves. This combination of pressures

creates a mental environment for the trailing

team that is not conducive to success.

Yasiel Puig’s NLCS performance is a perfect

example of how a team’s position on the

scoreboard can affect a player’s performance. In

the first two games of the series, the Dodgers did

not lead a single inning. Puig was 0-10 with six

strikeouts in those two games after putting

together a very successful division series against

the Braves. It appeared as if he had a relapse of

“In  2  of  the  4  games  where  a  team  failed  in  a  shutdown  inning  and  went  on  to  win  the  game,  the  opposing  team  also  failed  in  a  shutdown  inning.”  

 

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the problems that plagued him mid-season:

taking unnecessarily long and hard swings and

chasing pitches way out of the zone. After Adrian

Gonzalez gave the Dodgers the lead in Game 3,

Puig proceeded to get hits in his next two at bats

including a two-strike opposite field triple. It

appeared that once they got the lead, Puig

relaxed and stopped trying to do too much.

Shutdown innings are what allow teams to

play from ahead. If a pitcher doesn’t have a

shutdown inning, then his team will never come

to bat with the lead, and will never benefit from

the mental advantage that that gives them. In the

playoffs, every inning, every at bat, and every

pitch is magnified, so it is even more important to

have every advantage you can get.

Who’s the Best Catcher in the American League (12/16/13)

By Bryan Robinson

The title of the best backstop in the National League is a discussion between the duo of Buster

Posey and Yadier Molina. They have established themselves as the cream of the crop, with others a clear

step behind. In the American League, this discussion has been easily resolved for the better part of the last

decade, as Joe Mauer has simply dominated. However, with the news that Mauer is moving from behind

the plate to first base, the question becomes surprisingly difficult to answer. That is, who is the best catcher

in the American League?

I’ve narrowed the list of potential candidates to the following handful of players: Jason Castro, Yan

Gomes, Brian McCann, Sal Perez, and Matt Wieters. Certainly, others could breakout and surprise

everyone, but these five are the most likely in my view to be the most productive AL backstops in 2014. Also

worth noting is that I’ve excluded Carlos Santana from the discussion, as there are reports of him

abandoning catching, in order to accommodate Gomes.

 

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Jason Castro

The 26-year-old Castro experienced a breakout campaign in 2013, to the tune of a 4.3 WAR. His year was

largely influenced by an uptick in power, as well as an inflated BABIP (.351). Castro is a major regression-

candidate going forward, especially when considering that his plate discipline took a turn for the worse last

year, resulting in a 26.5% strikeout rate. His defense grades out as roughly average, based on limited MLB

experience. Steamer projects his 2014 offensive numbers to replicate those of his career, coupled with the

improved defense he exhibited last year. Steamer also projects a 3.6 WAR in a full season.

Yan Gomes

Another 26 year-old breakout from 2013, Gomes produced 3.7 WAR with above average offense and defense

in just 322 plate appearances. As I mentioned earlier, his production has led the Indians to experiment

Carlos Santana at third base. As the most

inexperienced player on my list, he is the

hardest to project for 2014. His numbers across

the board were very similar to those of Castro,

except that he doesn’t walk nearly as often.

Steamer projects Gomes to regress from his

spectacular 2013 with a 3.1 WAR in two thirds

of a season. Gomes is definitely the wildcard of

the five players I’ve listed, as he could

conceivably regress further than Steamer

believes he will.

Brian McCann

McCann, fresh off of signing a five-year, $85 million contract with the Bronx Bombers, is the only catcher of

the five presented in this article that has changed teams for 2014. His new home should help his offense,

being a left-handed bat. Steamer agrees, by projecting McCann to match his career high in home runs next

year with 24. About to turn 30, his numbers shouldn’t begin to decline dramatically just yet. Also, his 2013

wRC+ of 122 was dragged down a bit by a suppressed .261 BABIP. Steamer believes that in roughly a full

season, McCann will produce 3.7 WAR, with a 112 wRC+. I find this offensive projection to be a tad

conservative, but it still places his 2014 WAR higher than that of both Castro and Gomes.

 

18  

Salvador Perez

Perez, a 90’s baby, has more than 250 games under his belt even though he is just 23. As a contact hitter, he

has shown consistency in his plate discipline through low walk rates and low strikeout rates. His on-base

percentage will continue to be average-ish, unless he starts to walk or sees a spike in his BABIP. His strength

is his defense, with a cannon for an arm. He has displayed some extra base power, with 27 homers, 49

doubles, and even 5 triples in his brief career (989 plate appearances). By nearly duplicating all of his career

statistics, Steamer projects Perez to produce 4.1 WAR in 2014. With youth on his side and a high-floor in the

event that he takes a step back, Perez looks to be a lock to be one of the American League’s best for years to

come. With a bit more patience and power, he could be the best as early as next year.

Matt Wieters

The final name on the list is the former top-prospect Wieters, who has had an inconsistent career thus far.

Surprisingly, his defense has been stronger than his bat. Wieters’ 2013 saw a dip in his BABIP, which is the

main cause for his sub-.300 OBP, which is .319 for his career. The 27 year-old will be entering his power-

prime, and it would be nice to see him improve on his high-.100’s ISO. Wieters’ numbers have been trending

in the wrong direction for the past two years, so it will be interesting to see if he can improve upon the just-

okay numbers Steamer projects; a slash line of .249/.319/.425 and a 3.2 WAR. He certainly has the ability,

especially when looking at the 4.6 WAR he delivered in 2011. I’m skeptical, but maybe a BABIP-bounce back

will create the spark he needs. He hasn’t had a BABIP greater than .290 since his rookie campaign of 2009.

Of the five names, none seem like slam-dunk picks to be the most productive American League catcher next

year. I would be tempted to bet on one of “the field” to out produce all of them; I’m thinking a surprise

breakout such as Mike Zunino or Hank Conger, or a resurgent Alex Avila, for example. Perhaps even

more likely is that Carlos Santana sticks around at catcher for a little bit longer. This will certainly be fun to

reflect on at the end of next year.

For the record, I’m taking Sal Perez as my pick. His defense is elite, as only Russell Martin and Yadier

Molina graded out better defensively by Fangraphs’

cohesive metric last year. The beautiful thing is that

he doesn’t have to produce any more offense than

he has thus far in his brief career to be a huge asset.

At the very least, he’ll be an average backstop for

the foreseeable future, and carries a tremendous

ceiling. And luckily for the Royals, he’s all theirs for

the next six years.

 

19  

Links/Partners

 

20  

Comparing the 3 WAR Measures (12/31/13)

By Morris Greenberg

Many now treat WAR as a definite measure of success of a player. However, it is important to realize, like

with any other statistic developed in the Sabermetric Revolution, it is not perfect. As Baseball-Reference

explains in its glossary entry on WAR:

There is no one way to determine WAR. There are hundreds of steps to make this

calculation, and dozens of places where reasonable people can disagree on the best way to

implement a particular part of the framework. We have taken the utmost care and study at

each step in the process, and believe all of our choices are well reasoned and defensible.

But WAR is necessarily an approximation and will never be as precise or accurate as one

would like.

We present the WAR values with decimal places because this relates the WAR value back

to the runs contributed (as one win is about ten runs), but you should not take any full

season difference between two players of less than one to two wins to be definitive

(especially when the defensive metrics are included).1

With top Sabermetric websites like Baseball-Reference, Fangraphs, and Baseball Prospectus all having

different calculations of measuring the wins above a replacement player, we do not know the ordinal

rankings of players based on any one version of WAR. Additionally, even if we are 99% sure that one player

is better than another, we are much less sure of the difference in production in terms of WAR between the

two players from one measure alone.

In order to more accurately learn from rWAR (Baseball-Reference version), fWAR (Fangraphs version), and

WARP (Baseball Prospectus version), we need to see the differences in characteristics between the measures.

Note that I am not trying to find which measure is best of the three; to do this would probably be quite

subjective, especially considering that the actual WAR formulas are black boxes to me. I am merely looking

                                                                                                               1  http://www.baseball-­‐reference.com/about/war_explained.shtml  

 

21  

at trends in the data, specifically hitter data in this article. I plan to look at this further with pitchers in a

future article.

Are there relationships between the metrics?

One of the most basic questions to consider is if one measure of WAR can imply something about another

measure. To explore this question, I took all of the hitters who finished in the top 35 for the 2013 season in

any one of the three statistics, and compared their results:

The light blue line represents the 45˚ line, and the black line represents the linear line of best fit when

comparing rWAR and WARP. The y = mx + b form of the black line and the R^2 value for the equation

(indicator of how well the data points map to the line of best fit, where an R^2 of 1 means that every data

point lies on the line of best fit) are written at the top. If a player’s data point lies on the 45˚ line, this means

the rWAR and WARP of the specific player were equal this year. If it lies above the 45˚ line, then the player’s

WARP was greater than his rWAR, and if it lies below the 45˚ line, the player’s WARP was less than his

rWAR.

There are a few key insights to notice. First, rWAR and WARP are not the same, as there are observations

that do not lie on the 45˚ line. Second, we cannot derive either of the two statistics accurately from the other

y  =  0.6588x  +  1.5218  R²  =  0.40611  

0  

2  

4  

6  

8  

10  

12  

0   2   4   6   8   10   12  

WARP  

rWAR  

rWAR  vs.  WARP  

 

22  

(since the R^2 value is small). Third, neither measure is consistently greater or less than the other (since

there are some observations that lie above the 45˚ line and some that lie below the 45˚ line). This implies

that rWAR and WARP use similar replacement level thresholds, but their actual calculations of deriving wins

differs. I have included the other two graphs of this nature below, the same insights apply for them.

y  =  0.7596x  +  1.5647  R²  =  0.70348  

0  

2  

4  

6  

8  

10  

12  

0   2   4   6   8   10   12  

rWAR  

fWAR  

fWAR  vs.  rWAR  

y  =  0.6695x  +  1.6525  R²  =  0.51135  

0  

2  

4  

6  

8  

10  

12  

0   2   4   6   8   10   12  

WARP  

fWAR  

fWAR  vs.  WARP  

 

23  

How differently distributed are the metrics?

Since WAR measures the value of a player over a theoretical replacement level player, it is important to see

how many players outperform the replacement level one. We gathered from the previous section that the

three statistics incorporate similar replacement level players. So, if this is true, we should expect the

measures to have roughly the same amount of people at each win level.

To test this, I looked at the distribution of the top 200, top 100, top 50, and top 25 players according to each

metric. To not clutter the story with graphs, I only included the histograms of the top 200 and top 25 in this

article:

fWAR top 200 rWAR top 200 WARP top 200

Bin Freq Rel % Cum % Bin Freq Rel % Cum % Bin Freq Rel % Cum %

1 0 0.0% 0.0% 1 0 0.0% 0.0% 1 0 0.0% 0.0%

2 70 35.0% 35.0% 2 68 34.0% 34.0% 2 75 37.5% 37.5%

3 49 24.5% 59.5% 3 50 25.0% 59.0% 3 51 25.5% 63.0%

4 38 19.0% 78.5% 4 29 14.5% 73.5% 4 30 15.0% 78.0%

5 18 9.0% 87.5% 5 24 12.0% 85.5% 5 22 11.0% 89.0%

6 14 7.0% 94.5% 6 13 6.5% 92.0% 6 9 4.5% 93.5%

7 6 3.0% 97.5% 7 9 4.5% 96.5% 7 8 4.0% 97.5%

8 3 1.5% 99.0% 8 4 2.0% 98.5% 8 4 2.0% 99.5%

9 1 0.5% 99.5% 9 2 1.0% 99.5% 9 0 0.0% 99.5%

10 0 0.0% 99.5% 10 1 0.5% 100.0% 10 0 0.0% 99.5%

11 1 0.5% 100.0% 11 0 0.0% 100.0% 11 1 0.5% 100.0%

More 0 0.0% 100.0% More 0 0.0% 100.0% More 0 0.0% 100.0%

For the top 200 players, there is little difference in the distribution between the three statistics. There are

slightly more players in the top 200 that have fWAR in the 4-5 bin than there are in the rWAR and WARP 4-

5 bins, but this is minimal given that we know these measures are not the same.

 

24  

When we blow up the image of the top 200 players into the top 25 players, however, there is a clearer

difference in distribution between the 3 statistics.

fWAR top 25 rWAR top 25 WARP top 25

Bin Freq Rel % Cum % Bin Freq Rel % Cum % Bin Freq Rel % Cum %

4.5 0 0.0% 0.0% 4.5 0 0.0% 0.0% 4.5 0 0.0% 0.0%

5.09 0 0.0% 0.0% 5.09 0 0.0% 0.0% 5.09 5 20.0% 20.0%

5.68 11 44.0% 44.0% 5.68 5 20.0% 20.0% 5.68 6 24.0% 44.0%

6.27 5 20.0% 64.0% 6.27 6 24.0% 44.0% 6.27 7 28.0% 72.0%

6.86 3 12.0% 76.0% 6.86 7 28.0% 72.0% 6.86 2 8.0% 80.0%

7.45 1 4.0% 80.0% 7.45 2 8.0% 80.0% 7.45 2 8.0% 88.0%

8.05 3 12.0% 92.0% 8.05 2 8.0% 88.0% 8.05 2 8.0% 96.0%

8.64 1 4.0% 96.0% 8.64 2 8.0% 96.0% 8.64 0 0.0% 96.0%

9.23 0 0.0% 96.0% 9.23 1 4.0% 100.0% 9.23 0 0.0% 96.0%

9.82 0 0.0% 96.0% 9.82 0 0.0% 100.0% 9.82 0 0.0% 96.0%

11 1 4.0% 100.0% 11 0 0.0% 100.0% 11 1 4.0% 100.0%

More 0 0.0% 100.0% More 0 0.0% 100.0% More 0 0.0% 100.0%

WARP is the most bottom heavy of the bunch, having 5 players in the 5.09-5.68 bin while the other

measures have none in their equivalent bins, and consistently has a lesser portion of the data in higher bins.

This gives us some context into Mike Trout’s season; even though he performed similarly according to all

three metrics, WARP actually

gives him the most credit, since

the difference between his

WAR and any other top player’

WAR is greatest when

comparing WARPs.

Additionally, a more theoretical

idea we can take away from this

 

25  

data is that all three WAR approximations approach similar limits (if not the same limit) for their

distribution functions when taking a large enough sample of the population. However, as we magnify the

data enough, we can see differences in the distributions of the three WAR approximations. This means that

fWAR, rWAR, and WARP all have similar processes for determining player value, but not the same.

What types of players seem to differ the most between the statistics? What kinds of players’

WAR values seem to be the most uniform?

Now that we see that we cannot derive the value of one statistic from another very accurately, and all three

distribution functions approach similar limits, where does the breakdown occur between the three? Can we

characterize the differences in results to valuing specific kinds of skill sets differently? To explore this

question, I once again used players who finished in the top 35 in 2013 in any one of the 3 WAR measures in

my sample, but I also used players who finished in the middle 30 for any of the statistics, and players who

finished in the bottom 35, to characterize the entire population of MLB players better. I only used the middle

30 because there was much less overlap in the middle between the three approximations than there was in

the top and bottom tiers, so there was already plenty of a characterization of the middle portion of the

population.

After gathering all of this data, I calculated the standard deviations between the three metrics for every

player. The players who have the lowest standard deviations are the ones that have the most uniform WAR

measures, while the players who have the highest standard deviations are the ones who have WAR measures

that fluctuate the most. The table below shows the players whose standard deviations are more than 1

deviation away from the average deviation for a player in the sample, where the blue group contains the least

variant players in the sample, and the red group contains the most variant.

Name Team fWAR rWAR WARP AVG WAR STDEVA

Laynce Nix Phillies -0.7 -0.7 -0.63 -0.677 0.040

Chris Nelson Angels -0.7 -0.7 -0.78 -0.727 0.046

Alex Gonzalez Brewers -1.1 -1 -1.06 -1.053 0.050

Jeff Francoeur Giants -1.3 -1.4 -1.41 -1.370 0.061

Ryan Ludwick Reds -0.8 -0.9 -0.77 -0.823 0.068

 

26  

Jamey Carroll Royals -0.9 -0.8 -0.95 -0.883 0.076

Billy Butler Royals 1.4 1.5 1.58 1.493 0.090

Jose Lobaton Rays 1.4 1.4 1.57 1.457 0.098

David Wright Mets 6 5.8 5.87 5.890 0.101

J.D. Martinez Astros -1.1 -1.3 -1.16 -1.187 0.103

Jose Tabata Pirates 1.1 1.2 0.99 1.097 0.105

Brent Lillibridge Yankees -1 -0.8 -0.8 -0.867 0.115

Carlos Triunfel Mariners -0.7 -0.9 -0.66 -0.753 0.129

Jayson Werth Nationals 4.6 4.8 4.84 4.747 0.129

Jhonatan Solano Nationals -0.4 -0.4 -0.65 -0.483 0.144

Kelly Johnson Rays 1.2 1.3 1.5 1.333 0.153

Angel Pagan Giants 1.3 1 1.23 1.177 0.157

John Jaso Athletics 1.2 1.1 1.42 1.240 0.164

Scott Hairston Nationals -0.7 -0.9 -0.56 -0.720 0.171

Christian Yelich Marlins 1.4 1.4 1.7 1.500 0.173

Casper Wells Phillies -1 -0.8 -1.15 -0.983 0.176

Adeiny Hechavarria Marlins -1.9 -2.1 -2.26 -2.087 0.180

Carlos Ruiz Phillies 1.4 1.7 1.74 1.613 0.186

Tyler Moore Nationals -1.2 -0.9 -0.85 -0.983 0.189

AVERAGE 0.3 0.3 0.36 0.331 0.1211

Anthony Rendon Nationals 1.5 0 1.47 0.990 0.857

Chris Parmelee Twins -0.2 0.6 -1.12 -0.240 0.861

Jeff Keppinger White Sox -1.5 -2 -0.27 -1.257 0.890

Daniel Descalso Cardinals -0.3 0.1 1.42 0.407 0.900

Lorenzo Cain Royals 2.6 3.2 1.41 2.403 0.911

Robinson Cano Yankees 6 7.6 6.04 6.547 0.912

Alcides Escobar Royals 1.1 0.3 -0.74 0.220 0.923

Marlon Byrd Mets/Pirates 4.1 5 3.12 4.073 0.940

 

27  

Dan Uggla Braves 0.5 -1.3 -0.88 -0.560 0.942

Josh Donaldson Athletics 7.7 8 6.19 7.297 0.970

Dustin Pedroia Red Sox 5.4 6.5 4.53 5.477 0.987

Darwin Barney Cubs 0.4 -0.5 -1.72 -0.607 1.064

Andrelton Simmons Braves 4.7 6.8 5.41 5.637 1.068

Jean Segura Brewers 3.4 3.9 5.55 4.283 1.125

Shin-Soo Choo Reds 5.2 4.2 6.45 5.283 1.127

Ben Zobrist Rays 5.4 5.1 3.31 4.603 1.130

Gerardo Parra Diamondbacks 4.6 6.1 3.73 4.810 1.199

Carlos Gomez Brewers 7.6 8.4 6.04 7.347 1.200

A.J. Pollock Diamondbacks 3.6 3.5 1.47 2.857 1.202

J.J Hardy Orioles 3.4 3.7 1.47 2.857 1.210

Andrew McCutchen Pirates 8.2 8.2 6.03 7.477 1.253

Starling Marte Pirates 4.6 5.4 2.85 4.283 1.304

Ian Kinsler Rangers 2.5 4.9 5.27 4.223 1.504

AVERAGE 3.5 3.8 2.91 3.409 1.064

There are some trends from this subset that would likely carry over to the entire population. The first is that

increased playing time causes an increase in the variation between WAR measurements. Intuitively, this may

seem obvious, since any difference in two WAR formulas should only be accentuated by larger amounts of

data for a player. However, this means something important: for an individual player, the three different

WAR measures do not approach the same limit as his playing time increases, since the results become more

varied.

The second, and probably more interesting trend in these groupings, is the positional similarities in each

group. In the red group, most of the players are middle infielders and very strong defensive outfielders, while

in the blue group, most of the players are catchers, corner infielders, and bat-first outfielders. Note that this

does not necessarily mean that the three different WAR approximations drastically differ in valuing specific

positions. More likely, since different positions are expected to have different skill sets, positional groupings

generally show which areas seem to vary the most across the three formulas for WAR. Middle infielders tend

 

28  

to be faster players that have good gloves, and not as much power. Perhaps the three WAR measures differ

most in these typical strengths of middle infielders. This would explain why a middle infielder like Kelly

Johnson appears in the blue group, since Kelly Johnson is atypical as a middle infielder (above average

power, and below average speed and glove).

This is just a starting point for comparing these three WAR measures. As WAR is used to analyze players so

often now, this is a deep topic that can definitely (and should be!) expanded. The results from this article are

important and a good starting point to answer this large question, and hopefully this inspires you to not just

take a WAR measure at face value, but consider the deeper meaning behind a WAR value of a player.

A Belated Farewell to

the Toddfather

(12/29/13)

By Caleb Pykkonen

I wanted my first piece for Batting Leadoff to

serve as a bit of an introduction to myself as a baseball fan, and as a discuss on a topic relevant to this year's

offseason. Since I grew up in Colorado, the Rockies have played as big a role in my baseball fandom as any

Major League franchise. Therefore, seeing as he retired at the end of this season, and because he was one of

my heroes as a young baseball player in the early 2000s, I decided it would be a good time to think

about Todd Helton’s Hall of Fame credentials.

As a Rockies fan, it has been painful to watch Helton the past four seasons. Injuries have hampered his play

since the 2009 season. Todd hasn’t played more than 124 games in any season since 2010, and he was all the

way down to 69 games in 2012. The Rockies’ front office has been hesitant to bring in a young first baseman

that might be able to replace Todd in the long run, although they did sign Michael Cuddyer two years ago.

All of this adds up to a disappointing finish to a 17-year career that could have ended much differently, and

probably much better, for both Helton and the Rockies.

 

29  

Nevertheless, we shouldn’t let the drawn-out and

disappointing end to Todd Helton’s career make

us forget the player he was in the early 2000s.

Back in his heyday, Helton was one of the most

fearsome first basemen in the game. From 2000-

2004 Helton racked up 34.7 WAR, per

Fangraphs. Also during that five-year span, Todd

managed 186 home runs, had a walk rate higher

than his strikeout rate in all but one of those

years, had two years of .300-plus Isolated Power

(.326 in 2000, and .349 in 2001), and won three

Gold Glove awards and four Silver Sluggers. In

fact, even if Helton’s last four years are included,

he still finished his career with a 14.1 walk

percentage and a 12.4 Strikeout percentage. Todd

also managed nine seasons with an On-Base

Percentage greater than .400, and he finished

with a .414 career OBP.

According the Baseball-Reference.com, Helton

ranks 26th in career OBP, 36th in career Slugging

Percentage, 20th in career OPS, 16th in career

Doubles, 35th all-time in walks, and 28th in career

Win-Probability-Added. If you’re inclined to use

clutch-performance as a measure of player-value,

Helton also finished his career ranked 26th in

Situational Wins Added (WPA/LI), also per

Baseball-Reference.

From the defensive side of things, advanced stats

don’t love Helton’s performance at first base.

However, I believe that we have yet to reach the

point where these advanced statistics tell the

entire defensive story (these all agree). Again

using Baseball-Reference to look at Todd's

defensive work, we can see that he was no scrub.

Todd finished 15th all-time in putouts (13th all-

time among first basemen), 2nd all-time among

first basemen in assists, and 6th all-time in

fielding percentage as a first baseman. Add the

three Gold Gloves he won in the early 2000s, and

you can argue that his defensive work would help

his case for the HOF.

In a vacuum, Todd Helton's resume seems

worthy of a bid to baseball's shrine. But, there are

some important marks against Helton that must

also be taken into consideration. For starters, he

played every single home game of his Major

League career in the hitter-friendly mile-high air

of Coors Field. I would argue that his offensive

statistics would still stand up to the Hall Of Fame

standard even taking into account his home park,

but I do not speak for many of the voters. Of

equal importance is the fact that Helton had the

best years of his career during the peak of

baseball's steroid era. Voters have already shown

that they are hesitant to vote steroid users into

the Hall, and although Helton has never been

accused of or caught using steroids, he might still

be viewed as being guilty by association.

Furthermore, just as I used Helton's defensive

numbers to build his HOF case, critics could

point to some of his advanced defensive statistics

as reasons that he shouldn't be in the Hall.

Finally, despite the fact that the blame should not

be placed on Helton for only making two

postseasons in his life, he was not able to help his

bid by building bountiful playoff success.

 

30  

As is the case for most players with the potential to make it to Cooperstown, valid arguments can be made

both for and against Todd Helton. Although we had to endure a prolonged twilight to his career, I believe

Helton's numbers present a strong case. Todd never won an MVP award, but from 2000 until his lone World

Series run in 2007 he was one of the best players in all of baseball. Although WAR does not tell the entire

story, Fangraphs says Helton was the fifth best position player over that eight year stretch, and the only first

baseman better than him during

that time was Albert Pujols.

Helton might not have

maintained the same sustained

brilliance that was the signature

of many Hall of Famers, but he

was at the top of the sport for

nearly a decade. I believe that

alone is enough to punch him a

ticket.

Thus far I have merely presented

reasons why I think Todd Helton

SHOULD be inducted into

Cooperstown, and I could write

an entirely new post regarding

whether or not he WILL be

inducted. In the post-steroid era,

it's extremely difficult to gauge

the opinions of those in the

BBWAA. Helton's chances of

being inducted will likely take a

hit because he will be grouped

with Bonds, Clemens, Sosa and all the other known-users. Given time, however, we might see him become

the first Rockie voted into the Hall of Fame. We will just have to wait.

In the meantime, fans of the Rockies and fans of baseball can be happy that they got to witness #17 light up

Coors Field for nearly two decades. I hope that someday the members of the BBWAA can find it in their

hearts to give him a place among the other all-time greats. Who knows? They might surprise us.

 

31  

Chris Denorfia and the Importance of Making

Contact (1/7/14)

By Mike Minio

During the 2013-14 offseason, Nick Cafardo of

the Boston Globe reported that the Red Sox have

approached the San Diego Padres and asked

about outfielder Chris Denorfia’s availability.

Assuming that the organization follows through

on their intent to begin the 2014 campaign with

rookie Jackie Bradley in centerfield, Denorfia

would be used primarily as a utility outfielder.

This article is not about the merits or

shortcomings of this potential trade, but rather

an analysis of the type of player Chris Denorfia is

and why

organizations

should look toward

him as well as the

tool he embodies

when assembling

the cohesive self-

contained unit we

call a team. What’s

so great about him?

Your grandfather –

or great-

grandfather –

would be the first to

point out that he doesn’t strike out nearly as

much as other players while still doing other

things on the field consistently.

We currently live in the Age of the Strikeout.

Both hitters’ and pitchers’ strikeout rates have

increased dramatically over the past decade.

Pitchers today – especially relievers – generally

have more explosive stuff than their counterparts

in previous generations. Furthermore, front

offices in the post-Moneyball period have

(rightly, I might add) placed a premium on

patience and power. More and more hitters

appear to follow Matt Stairs’ manual of how to

swing a bat: take the pitch if it’s outside the zone,

but swing as if your life depended on it if you

think you can it. As a result, players like Dunn,

Mark Reynolds, and Ryan Howard feast on

 

32  

fastballs and post high wOBA, home run, and

WAR rates. However, they also have trouble with

breaking and off-speed pitches, thereby leading

to record-setting strikeout rates and abysmally

low batting averages. It is also important to note

that these players represent the top of the pile;

players of the same style are found all over the

major and minor leagues. Consequently, pitchers

have benefited immensely by increasing their

strikeout rates and lowering their Earned Run

Averages considerably. Luck cannot be on a

batter’s side if he is unable to put the ball in play.

Moreover, pitchers are facing the kind of hitters

they do best against: free swingers who have

trouble with pitches that aren’t fastballs.

Some of you might argue that these players in

question were far more productive than league

average during their primes. You’re right, but I’m

not contesting these players’ respective values.

Instead, I’m looking at their style of play – which

has obvious flaws – and viewing it as a generally

accepted tendency in baseball. Wherever there’s

a common trend, there will be players who do not

fit into that particular mold. Those are the

players who represent untapped undervalued

advantages. Chris Denorfia exemplifies the

contact-oriented approach that emphasizes

working the count, putting the bat on the ball,

and avoiding strikeouts. He does not hit for much

power, but he is not incapable of hitting doubles

and home runs either (21 2B and 10 HR in 2013).

His approach at the plate is relatively simple.

Denorfia almost always takes the first pitch

regardless of location (75% of PA) and proceeds

to adjust accordingly. His goal is to put himself in

a position where he can get a good ball to hit.

Below is a list of his career rates of contact,

courtesy of Fangraphs:

Outside-Swing% 25.0%

Zone-Swing% 57.7%

Swing% 40.9%

Outside-Contact% 62.8%

Zone-Contact% 89.3%

Contact% 81.0%

Swing-Strike% 7.6%

This table demonstrates the patient, but

consistent approach that Chris Denorfia has

taken throughout his career (there is very little

fluctuation from season-to-season). He does not

swing particularly often; he is a patient hitter who

would rather take a pitch both inside and outside

the zone if he feels he cannot hit it. His contact

rates stand out the most. Of all the pitches he

swings at, he makes some sort of contact 81.0% of

the time. When he chases something in the zone,

that figure increases to a whopping 89.3%. He

swings and misses in only 7.6% of all his swings.

Although he does not hit for significant power

 

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and struggles against righties, he was still a

consistently solid bat in the Padres’ order for the

past four seasons, batting

.280/.338/.414 and generating 8.2 Wins

Above Replacement. He only made $2

million last season and will make $2.25

million in 2014.

As these rates suggest, Denorfia exemplifies the

contact-oriented approach that has been so

glaringly absent from baseball in the past few

seasons. A player of this mold gives the manager

options when facing different types of pitchers. It

might be more beneficial to pinch hit a player like

Denorfia against some power pitchers with good

breaking stuff if other bench players are

particularly strikeout prone. He might not hit a

home run, but he could start a rally by getting on

base and make the opposing pitcher work in the

process whereas a player like Matt Stairs would

probably walk back to the dugout after three

swings. This

argument does

not suggest that a

general manager

should fill his

bench with five Enrique Wilsons; however, he

should have at least one position player on the

bench who can make contact consistently. Doing

so gives the field manager a potential rally starter

in the late innings against power pitching. So,

should the Red Sox consider trading for Chris

Denorfia? Absolutely. Assuming the price is right

and he will not regress significantly, he could be a

cost-effective addition to their club who adds

diversity to their bench.

How Much is a

First Round Pick

Worth? (11/14/13)

By Matthew

Provenzano

Recently, a couple of friends and I were just throwing the idea around: how often are first-round draft picks

successful? There is so much emphasis on them for either the draft, scouting purposes, or the possibility of

losing or gaining a first-round pick in a qualifying offer or trade situation. So, we decided to flip through the

“A  player  of  this  mold  gives  the  manager  options  when  facing  different  types  of  pitchers.”    

 

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records of first-round picks and found that some of them were not surprising and were successful, some

were decent, and some were just total flops. But there was no visible trend that we could tell other than that

the first overall pick was generally the most successful. However, I wanted to go a bit further and created a

model in calculating first-round pick value. This isn’t meant to be a hard-and-fast rule in how the

draft should go, but it merely gives a general idea of how it usually goes on the aggregate since 1965. And

with that information, a team could use this expected payoff of pick value to either a) determine what type of

value they can get out of their slot or b) determine whether it is worth giving up their pick or, on the

converse, taking a supplemental first-round pick.

Building the Model

To start building this model, I started with a simple statistic that would help me in figuring out the expected

payoff for the first 32 picks in the draft. I first wanted to know what percentage of players in each slot

actually played in Major League Baseball. This can be represented here:

This one’s an important figure because the linear relationship shows that while the first overall pick is nearly

a sure bet that they will play in the Majors, the last pick in the first round is nearly a coin toss.

Next, I created a list of each slot’s total bWAR. This is good at getting a total metric, but it would definitely

get skewed by some outliers (Hall of Famers) and should therefore be controlled for. This need is most

evident in the 31st pick, where the total bWAR is 142.3, but Greg Maddux accounts for nearly 100 of that.

 

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Instead of having these large, successful careers skew the data, I would just create the average value in one

season, such that we can get a snapshot of what the average season looks like from an average player in each

slot. The previous chart has to be considered in respect to this, because it’s quite important the percentage of

players that do play. Thus, the expected payoff for a single season is as follows: bWAR/162 * Pr(playing)

such that “Pr(playing)” is the probability that a player in a particular slot plays in the Majors. This expected

payoff can be shown as:

What’s so fascinating about this chart is that the exact point where the average season eclipses starter-level

quality is just around the 15th pick. However, the deviation between the first and last pick in the round on

average is not very large; it’s the difference between slightly above starter-level and slightly-below starter-

level. This is telling for one season, but the length of careers also play a huge factor.

The fact that nearly half of the picks in the first round are, on average in an average season, starter-level

quality, shows that in the short-term, the difference between these slots aren’t telling of true value. So, I’ve

put in one, simple addition: the average number of seasons, which is merely the total number of games for

each slot divided by 162; it is illustrated here:

 

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While the difference in one season may not be great between picks, the longevity is what truly separates

them. Because once I multiplied the number of seasons and the average season, it now looks more like this:

This completes the model, which is best expressed by the Power relationship shown in the formula above.

We now have a basic model on calculating the expected payoff from each slot in the draft.

 

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What Does This All Mean?

First, it confirms the anecdotal observation that the first overall pick is very successful (ie: A-Rod, Ken

Griffey Jr., Bryce Harper, Daryl Strawberry, Joe Mauer, Adrian Gonzalez, Josh Hamilton, David

Price, etc. and etc. and etc.). Second, it shows that in general, while it definitely is more of a chance, the first

five overall picks are pretty successful. After that, it’s more or less in fate’s hands. Scouting has gotten better

at picking out talent and it’s even clear to see that since 1965, organizations have done an excellent job at

reflecting this Power relationship through their picks. But where they do fail is the extent to which teams

safeguard their draft picks when signing other players or in trades. Some organizations, hoping not to

jeopardize their future, hold on to their draft picks even when a starter-level player (or better) is ripe for the

taking. In this circumstance, it is the responsibility to weigh this expected value of the possible draft pick

against the expected value of a possible signed player. Let’s try a concrete example to put this into practice.

Practical Application & Conclusion

There have been rumors swirling around that the New York Yankees want to acquire Carlos Beltran, and

some have said that a secret offer has already been made. Since Beltran was offered a qualifying offer by the

Cardinals, the Yankees would have to sacrifice the 18th pick in the first round to acquire him. Using our final

model and 2014 Steamer predictions, we can find the better of two scenarios. If we plug 18 into the formula

in the last chart, we get an expected career WAR of 2.45. Beltran, on the other hand, is expected to get a

WAR of 1.8 in the 2014 season. And if he signs to a two or three year deal, it is nearly guaranteed (barring

injury), that he would eclipse the expected payoff from this draft pick. If the Yankees had to make a choice

between the two, the opportunity cost would be lower when acquiring Beltran. Yes, he’d probably be more

expensive, but the key with our model is the expected value. And while this is simplistic and is not the sole

engine behind these types of decisions, it is another tool in the decision making process.

Yes, there is the possibility of giving up an All-Star or

Hall of Famer, but there are many, many, many other

times where that did not happen. The hope of growing

a Hall of Famer is incredibly alluring, but an

organization should never be so blinded as to ignore

the expected payoff and should choose between the

better of two possible worlds.

 

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Links/Partners

 

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Batting Leadoff Contact Information Alex Smith – Founder E-Mail: aws77@cornell.edu Twitter: @RealAlexSmith19 Chris Moran – Featured Blogger E-mail: chris.moran@my.wheaton.edu Twitter: @hangingslurves Mike Parnell – Featured Blogger E-mail: parnellmike.mike@gmail.com Matthew Provenzano – Featured Blogger E-mail: mjp294@cornell.edu Twitter: @mpro6294 Bryan Robinson – Featured Blogger E-mail: bryrob58@gmail.com Twitter: @ProProjections Caleb Pykkonen – Featured Blogger E-mail: caleb.pykkonen@tufts.edu Twitter: @cpikeonen Morris Greenberg – Featured Blogger E-mail: morris.greenberg@tufts.edu Twitter: @Morris_Gberg Mike Minio – Featured Blogger E-mail: michael.minio90@gmail.com Hudson Belinsky – Contributor E-mail: halosdaily@gmail.com Twitter: @hudsonbelinsky Francesco Padulo – Contributor

 

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E-mail: fep23@cornell.edu Jesse Sherman – Editor E-mail: jws238@cornell.edu Adam Kirsch – Editor E-mail: ajk266@cornell.edu James Goldstein – Editor/Advisor E-mail: james.goldstein@nyu.edu

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