MLB STATS Group SIX Astrid AmsallemJoel De Martini Naiwen ChangQi He Wenjie HuangWesley Thibault

MLB STATS

Group SIX

Astrid Amsallem Joel De Martini

Naiwen Chang Qi He

Wenjie Huang Wesley Thibault

1. What affects the WINS of a pitcher?

2. How can these hot pitchers get so much $?!

3. What factors determine the attendance of

the game!

We are interested in…

1.New York Yankees

2.Boston Red Sox

3.Chicago Cubs

4.Philadelphia Phillies

5.New York Mets

6.Detroit Tigers

7.Chicago White Sox

8.Los Angeles Angels

9.San Francisco Giants

10.Los Angeles Dodgers

Top 10 salaries in MLB

http://en.wikipedia.org/wiki/File:NewYorkYankees_caplogo.svg

http://en.wikipedia.org/wiki/File:BostonRedSox_CapLogo.svg

http://en.wikipedia.org/wiki/File:Chicago_Cubs_Cap_Insignia.svg

http://en.wikipedia.org/wiki/File:Philadelphia_Phillies_Insignia.svg

http://en.wikipedia.org/wiki/File:New_York_Mets_Insignia.svg

1. Starter Pitchers

2. ERA: Earned Run Average

3. K9: Strike out per nine innings

4. AVG

5. WINS (player)

6. WHIP: Walks and hits per inning pitched

7. Career Experience

ERA k9 whip avg

Variables Explanation

Regress Wins against other variables

Dependent Variable: LNWINS

Method: Least Squares

Date: 12/01/10 Time: 11:46

Sample: 1 45

Included observations: 45

LNWINS=C(1)+C(2)*K9+C(3)*EXPERIENCE+C(4)*ERA+C(5)*AVG +C(6)*WHIP

Coefficient Std. Error t-Statistic Prob.

C(1) 3.598584 2.481966 1.449893 0.1551

C(2) 0.024904 0.093978 0.265001 0.7924

C(3) 0.114170 0.017685 6.455917 0.0000

C(4) -0.421004 0.271878 -1.548502 0.1296

C(5) 9.148770 8.535001 1.071912 0.2903

C(6) -0.827380 1.297105 -0.637867 0.5273

R-squared 0.583988 Mean dependent var 4.281809

Adjusted R-squared 0.530654 S.D. dependent var 0.616112

S.E. of regression 0.422092 Akaike info criterion 1.236377

Sum squared resid 6.948293 Schwarz criterion 1.477265

Log likelihood -21.81849 F-statistic 10.94948

Durbin-Watson stat 1.656059 Prob(F-statistic) 0.000001

Dropping K9



Date: 12/01/10 Time: 11:46

Sample: 1 45


LNWINS=C(1)+C(3)*EXPERIENCE+C(4)*ERA+C(5)*AVG+C(6)*WHIP


C(1) 4.168256 1.226036 3.399783 0.0015

C(3) 0.113482 0.017289 6.564031 0.0000

C(4) -0.427853 0.267483 -1.599550 0.1176

C(5) 7.299192 4.855053 1.503422 0.1406

C(6) -0.741132 1.240930 -0.597239 0.5537







Dropping WHIP



Date: 12/01/10 Time: 11:47

Sample: 1901 1945


LNWINS=C(1)+C(3)*EXPERIENCE+C(4)*ERA+C(5)*AVG


C(1) 3.727099 0.970836 3.839063 0.0004

C(3) 0.114602 0.017051 6.721023 0.0000

C(4) -0.548733 0.173501 -3.162707 0.0029

C(5) 7.066036 4.801216 1.471718 0.1487







Dropping AVG



Date: 12/01/10 Time: 11:48

Sample: 1 45


LNWINS=C(1)+C(3)*EXPERIENCE+C(4)*ERA


C(1) 4.883349 0.578197 8.445815 0.0000

C(3) 0.115720 0.017269 6.700957 0.0000

C(4) -0.393006 0.139395 -2.819362 0.0073







White Heteroskedasticity Test:F-statistic 0.738906 Probability 0.598933

Obs*R-squared 3.894030 Probability 0.564772

Test Equation:

Dependent Variable: RESID^2


Date: 12/01/10 Time: 12:45

Sample: 1901 1945


Variable Coefficient Std. Error t-Statistic Prob.

C -6.524628 5.254761 -1.241660 0.2218

EXPERIENCE 0.232587 0.195057 1.192409 0.2403

EXPERIENCE^2 0.003262 0.004671 0.698379 0.4891

EXPERIENCE*ERA -0.070934 0.049483 -1.433492 0.1597

ERA 2.998153 2.591449 1.156941 0.2543

ERA^2 -0.317762 0.326293 -0.973855 0.3361


Adjusted R-squared -0.030577 S.D. dependent var 0.449694





a=0.05Chi-Square (5) =11.0705 > 3.894030There is no heteroskedasticity.

We get that experience and ERA are the most important factors in determining how many wins a player has. It is intuitive that the longer the player is in the league, the more wins he will inevitably receive, but it also is important to note that ERA is the most important performance statistic in determining the number of wins.

Conclusion 1

Regress Salary against other variables

Dependent Variable: SALARY


Date: 11/29/10 Time: 22:16

Sample: 1 45


SALARY=C(1)+C(2)*K9+C(3)*ERA+C(4)*EXPERIENCE+C(5)*AVG

+C(6)*WINS+C(7)*WHIP


C(1) 13.26240 26.93882 0.492315 0.6253

C(2) 0.535060 1.019848 0.524647 0.6029

C(3) -5.824067 3.073178 -1.895128 0.0657

C(4) 0.965893 0.298615 3.234570 0.0025

C(5) 39.57241 94.30549 0.419619 0.6771

C(6) 0.010538 0.022938 0.459394 0.6486

C(7) -3.340880 14.13198 -0.236406 0.8144







Dropping WHIP



Date: 11/29/10 Time: 22:17

Sample: 1 45


SALARY=C(1)+C(2)*K9+C(3)*ERA+C(4)*EXPERIENCE+C(5)*AVG

+C(6)*WINS


C(1) 12.77971 26.53421 0.481631 0.6328

C(2) 0.474565 0.975198 0.486634 0.6292

C(3) -6.328931 2.182998 -2.899193 0.0061

C(4) 0.964078 0.294881 3.269375 0.0023

C(5) 33.74487 89.91824 0.375284 0.7095

C(6) 0.011023 0.022568 0.488440 0.6280







Dropping AVG



Date: 11/29/10 Time: 22:18

Sample: 1 45


SALARY=C(1)+C(2)*K9+C(3)*ERA+C(4)*EXPERIENCE+C(6)*WINS


C(1) 21.87751 10.67097 2.050190 0.0469

C(2) 0.184862 0.589466 0.313610 0.7554

C(3) -5.924145 1.877419 -3.155472 0.0030

C(4) 0.941778 0.285714 3.296229 0.0021

C(6) 0.012477 0.021993 0.567331 0.5737







Dropping K9



Date: 11/29/10 Time: 22:18

Sample: 1 45


SALARY=C(1)+C(3)*ERA+C(4)*EXPERIENCE+C(6)*WINS


C(1) 24.48138 6.628971 3.693088 0.0006

C(3) -6.220163 1.604937 -3.875642 0.0004

C(4) 0.950703 0.281150 3.381478 0.0016

C(6) 0.010790 0.021089 0.511631 0.6117







Dropping WINS



Date: 11/29/10 Time: 21:48

Sample: 1 45


SALARY=C(1)+C(3)*ERA+C(4)*EXPERIENCE


C(1) 25.80969 6.045566 4.269194 0.0001

C(3) -6.549164 1.457502 -4.493416 0.0001

C(4) 1.060266 0.180564 5.871959 0.0000







White Heteroskedasticity Test:

F-statistic 3.148422 Probability 0.017555

Obs*R-squared 12.94058 Probability 0.023942

Test Equation:

Dependent Variable: RESID^2


Date: 11/30/10 Time: 16:14

Sample: 1901 1945



C 250.6808 336.6494 0.744634 0.4610

ERA -81.81855 166.0227 -0.492815 0.6249

ERA^2 6.237881 20.90415 0.298404 0.7670

ERA*EXPERIENCE 4.320411 3.170161 1.362836 0.1808

EXPERIENCE -23.21171 12.49642 -1.857468 0.0708

EXPERIENCE^2 0.519519 0.299279 1.735900 0.0905







a=0.05Chi-Square (5) = 11.0705<12.94058So there is Heteroskedasticity.

We come to the same conclusion for salary. Experience and ERA are the main contributing factors to salary as we found before for wins.

Conclusion 2

Offensive 1.BA 2.HR 3.RBIWins 4.Last Wins: previous year’s wins 5.WINSOther 6.City Population 7.Team payroll

Variables Explanation

Are hitting statistics important factors for attendance? 2010 percent home attendance vs. important 2010 batting statistics

Dependent Variable: PERCENT


Date: 11/30/10 Time: 23:21

Sample: 1 30



BA 31.89626 456.5329 0.069866 0.9448

HR -0.067221 0.164119 -0.409587 0.6855

RBI 0.131782 0.090643 1.453856 0.1580

C -18.45951 91.70705 -0.201288 0.8420







Are hitting statistics important factors for attendance?

2010 percent home attendance vs. important 2010 batting statistics



Date: 12/01/10 Time: 12:44

Sample: 1 30



HR -0.071257 0.150760 -0.472652 0.6403

RBI 0.135978 0.066638 2.040545 0.0512

C -12.47021 31.97302 -0.390023 0.6996







Drop BA

Drop Constant



Date: 12/01/10 Time: 12:40

Sample: 1 30



RBI 0.113385 0.032437 3.495558 0.0016

HR -0.052144 0.140397 -0.371400 0.7131







RBI’S ARE THE MOST IMPORTANT OFFENSIVE STATISTIC

Are previous year’s wins significant for attendance? (Yes)



Date: 12/01/10 Time: 12:47

Sample: 1 30



LASTWINS 1.191640 0.225222 5.290966 0.0000

C -27.99286 18.41785 -1.519877 0.1398







Are current year’s wins significant for attendance? (Yes)*



Date: 12/01/10 Time: 12:48

Sample: 1 30



WINS 0.935905 0.279721 3.345851 0.0023

C -7.278343 22.85866 -0.318406 0.7525







*but not as significant as previous years’ record (Bandwagon)

Is city population significant for attendance? (No)



Date: 12/01/10 Time: 12:51

Sample: 1 30



CITYPOP 2.95E-06 1.64E-06 1.797594 0.0830

C 63.59600 4.362240 14.57875 0.0000







-It’s more general baseball enthusiasm rather than population:E.g. Boston population= 645,169 Attendance%=100.9% Arizona population= 1,601,587 Attendance%=51.8%

Is team payroll significant for attendance? (Yes)



Date: 12/01/10 Time: 12:52

Sample: 1 30



TEAMPAY 3.63E-07 6.60E-08 5.503025 0.0000

C 35.48078 6.498276 5.460030 0.0000







High team payroll=big names=more fans.

Are city population and payroll correlated?

Dependent Variable: CITYPOP


Date: 11/30/10 Time: 23:57

Sample: 1 30



TEAMPAY 0.033187 0.008261 4.017155 0.0004

C -1349341. 813618.3 -1.658444 0.1084

R-squared 0.365619 Mean dependent var 1671312.

Adjusted R-squared 0.342963 S.D. dependent var 2099771.

S.E. of regression 1702029. Akaike info criterion 31.59688

Sum squared resid 8.11E+13 Schwarz criterion 31.69029



Even though big cities don’t necessarily lead to high att.%, it is correlated with team payroll. There is an indirect affect from city pop, larger pop=>higher payroll=>attracts big names.

Conclusion

Most Significant Statistics:

Salary Experience, ERA

Wins Experience, ERA, Offense (Constant)

Attendance: RBI, Wins, Team Payroll

Questions?

Documents

MLB STATS Group SIX Astrid AmsallemJoel De Martini Naiwen ChangQi He Wenjie HuangWesley Thibault