Is There Really Racism Among MLB Umpires? Revisiting the Hamermesh Study

1. Is There Really Racism Among MLB Umpires? Revisiting the Hamermesh Study Phil Birnbaum www.philbirnbaum.com

2. The Hamermesh Study • "Strike Three: Umpires' Demand for Discrimination" • By Christopher A. Parsons, Johan Sulaeman, Michael C. Yates, and Daniel S. Hamermesh • Original August, 2007; update December, 2007

3. The Hamermesh Study • Discussed in Time, USA Today, Business Week • Claims to have found widespread discrimination – umpires (unconsciously) discriminate in favor of pitches of their own race • Call more strikes for pitchers of the same race as them • "Basically, it's an expression of deep-down preferences," says Hamermesh. "Am I sure it's there? Oh, yeah." • – Business Week

4. Situations • When looking at all pitches, no discrimination found • But lots of apparent racial bias when QuesTec not in use • And even more apparent racial bias when attendance is low • Study claims: when umpires are not being scrutinized, they discriminate

5. Low Attendance • I don't have the authors' data; I duplicated as best as I could • But my results are similar to the study's • Differences won't affect any conclusions here

6. Low-Attendance Games

7. Is There Racial Bias? • Model each cell as: • Baseline % strikes • Plus effect for the race of the umpire • Plus effect for the race of the pitcher • Plus effect if the umpire's race matches the pitcher's ("UPM") • If the UPM is different from zero, there's racial discrimination

8. The Study's Conclusion • After adjusting for race of umpire and pitcher, the pitch is 0.76 percentage points more likely to be called a strike if the umpire is the same race as the pitcher. • Statistically significant result • (Real study: 0.84, even more significant)

9. Implications • Lots of discrimination apparent • 0.76% of same race pitches: 1 in 130! • Almost 5,000 pitches affected • If only ¼ of pitches are borderline, the 1 in 130 becomes 1 in 30 • Wow!!

10. The Updated Fit

11. But Why Those Three Cells? • There are lots of other ways to modify the matrix to remove discrimination

12. How About This Instead?

13. Why Didn't the Study Do That? • Because the authors insisted that all races of umpires must discriminate the same • Hidden assumption in the regression model • But why? • Discrimination normally goes one way more than the other • Do blacks really discriminate against whites exactly as much as whites discriminate against blacks? • Doesn't seem right to me

14. Alternative Assumptions • There are lots of ways in which to adjust the 3x3 chart to achieve NO discrimination. • The way I chose minimizes the number of pitches affected • But my choice means there's discrimination among minority umps only

15. Number of Pitches Affected

16. Pitches Affected • Total pitches affected: 116 • Fewer than 1 in 4,000 • Original study had 5,000 pitches affected – 43 times as many! • Still statistically significant

17. Assumption • I think it's necessary to consider all possible alternatives to the study's hidden assumption that all groups discriminate equally • If you do, then the only conclusion you can draw is statistical significance • SOMETHING is going on, but we don't know what • We don't know • which races of umpire discriminate • which races they discriminate against • how much they discriminate

18. Another Hidden Assumption • A second hidden assumption: all umpires discriminate equally • Not just that white umpires overall discriminate the same amount as black, but that every white umpire discriminates the same amount as every black umpire

19. Do All Umpires Discriminate Equally? • Different humans have different attitudes towards other races • There are racists, advocates of race-neutrality, and advocates of affirmative action • Why should umpires be any different in how much they discriminate?

20. Checking for Individual Variation • If there were no bias, apparent umpire bias would occur by chance • Just by random luck, some white umpires would see fewer legitimate strikes from black pitchers • We can predict exactly what would happen – a bell curve with a certain spread • It turns out that real life is almost exactly what would occur by chance • In binomial Z-scores, sample variance was 1.04 (expected 1.00). • If there were significant differences in how umpires discriminate, the variance would be much higher

21. Possibilities • The possibilities of umpire bias are: • 1. Many or all umps discriminate: • (1a) a lot, and equally • (1b) a lot, but unequally • (1c) very, very little and equally • (1d) very, very little but unequally • 2. No umps discriminate • 3. At most a few umps discriminate • I argue that (1a) is implausible. The previous slide eliminated (1b). The statistical significance of the findings contradicts (1c), (1d), and (2). • That leaves (3).

22. At Most a Few Umpires Discriminate • It could be that a small number of umpires are responsible for the entire effect! • There were only 2 hispanic umpires and 4 black umpires • Look at individual umpires

23. Umpires vs. Hispanic Pitchers • Individual umpires ranked by how much they appear to favor hispanic pitchers, in descending order of favorable discrimination. • (X's are hispanic umps, hyphens are non-hispanic umps) ---X--------X---------------------------------- • The two hispanic umps favor hispanic pitchers more than most

24. Umpires vs. Black Pitchers • Individual umpires ranked by how much they appear to favor black pitchers, in descending order of favorable discrmination. • (X's are black umps, hyphens are non-black umps) X--------X---------X-------------------X------- • Two of the four black umps favor black pitchers more than most

25. Significance • If there were no racial bias, the Xs would be balanced around the center • If you remove ONE umpire ... • Either hispanic umpire • The most extreme black umpire • ... then the results are no longer statistically significant! • Next step: look closely at those individual umpires (review game tapes, for instance)

26. Two Competing Theories • Hamermesh et al • Assumptions • All races of umpires discriminate equally • Every umpire discriminates equally • Every umpire and race discriminates • Conclusions • Huge numbers of pitches are affected • Because there are so many white umpires, minority pitchers are at a disadvantage

27. Two Competing Theories • Me • Assumptions • Discrimination can vary by umpire • Conclusions • The observed effect is likely caused by a small number of minority umpires, maybe even one • Only a small number of pitches is affected • Because the umpires involved are minorities, minority pitchers are probably beneficiaries of this discrimination

28. Other Explanations • From the Hamermesh authors' FAQ: • "Suppose for example, that youth baseball coaching is different in Latin America than elsewhere, and that Hispanic pitchers consequently develop pitching “styles” that differ from those of Black, Asian, or White pitchers. If Hispanic umpires and pitchers both espouse similar styles that differ from other races/ethnicities, then what appears as discrimination may simply reflect these stylistic differences." • Statistical significance is not proof • There might be something else happening