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Baseball Statistics: Just for Fun!

Baseball Statistics: Just for Fun!. Issues, Theory, and Data. Home Run hitters: more strikeouts and four balls, and less steals?. Hypothesis. Korea Baseball Organization and US Major League Home Pages. Data collection. y1=#strikeouts,y2=#steals,y3=#4Bs, x=#HRs. Regress y on constant, x.

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Baseball Statistics: Just for Fun!

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  1. Baseball Statistics: Just for Fun!

  2. Issues, Theory, and Data Home Run hitters: more strikeouts and four balls, and less steals? Hypothesis Korea Baseball Organization and US Major League Home Pages Data collection y1=#strikeouts,y2=#steals,y3=#4Bs, x=#HRs. Regress y on constant, x. Model Hypothesis Testing Test the statistical significance of regression slopes using t-tests.

  3. 2. Data Collection KBO http://www.koreabaseball.or.kr US Major League Baseball http://www.majorleaguebaseball.com

  4. 3. Model I (#strike outs) = 1 + 1(#HRs) + 

  5. 3. Model II (#steals made) = 2 + 2(#HRs) +  (#steals attempted) = 3 + 3(#HRs) + 

  6. 3. Model III (# four balls) = 4 + 4(#HRs) + 

  7. 4. Hypothesis Testing • t-test on  Significant 4= ?? 1= ?? 1 = 0.84 t-value = 2.89 4 = 0.51 t-value = 2.50 Insignificant 2, 3= ?? 2 = -0.12 3 = -0.18 t-value = -0.94 t-value =-1.14

  8. 4. Hypothesis Testing (1) HR hitters get more strike outs! (2) HR hitter does not well steal a base because of his big body. Insignificant (3) HR hitters pull out more four balls!

  9. Wait a minute! To prevent “spurious correlation” between #HRs and #strike-outs, #steals, #4Balls, we need to control for the number of appearance at the batter box.Right!

  10. Multiple Regression–control for “#at bats”- • Without “control for # at bats,” a hitter with more appearances would record a higher number in each category than others, generating “spurious correlation between any pair of variables among #HRs, #strike-outs, #steals, and #four balls. • Two ways of control for # at batter box • Use a subsample of hitters who appeared more than 100. • Use “# at bats” as a control variable in multiple regression.

  11. Model I (extended) (#strike outs) = 1 + 1(#HRs) + 2(#at bats)

  12. Results using sub-sample 1 = 0.84 (2.89) 1 = 0.89 (2.88) 2= -0.03 (-0.49) 1 = 2.40 (11.64) 1 = 0.63 (3.11) 2= 0.14 (12.53) using entire sample

  13. Interpretation sub-sample 1 = 0.84 (2.89) 1 = 0.89 (2.88) 2= -0.03 (-0.49) When using a sub-sample which is already rather homogeneous in terms of number at bats, it doesn’t make much diference whether you control for # at bats or not. However, when using the entire sample which comprises of hitters vastly differing in terms of number at bats, control for # at bats does matter. In this entire sample, you would get distorted results if you do not control for # at bats. 1 = 2.40 (11.64) 1 = 0.63 (3.11) 2= 0.14 (12.53) entire sample

  14. Model II (extended) (#4Balls) = 1 + 1(#HRs) + 2(#at bats)

  15. Results sub-sample 1 = 0.51 (2.50) 1 = 0.34 (1.71) 2= 0.12 (2.77) 1 = 1.32 (11.01) 1 = 0.33 (2.73) 2= 0.07 (11.51) entire sample

  16. The End Was it fun?

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