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# Where Park Factors Fail: Case Study of an Extreme Park Effect

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Park factors describe how parks affect baseball outcomes on average. But what happens at the extremes? Originally presented at Saberseminar in 2015.

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### Where Park Factors Fail: Case Study of an Extreme Park Effect

1. 1. Where Park Factors Fail CASE STUDY OF AN EXTREME PARK EFFECT
2. 2. How do parks really affect outcomes? Park factors are great! ◦ Calculated as averages, and should be applied to averages ◦ E.g. when calculating wRC+ you adjust baseline up or down based on home park, not the player But what happens at the extremes? ◦ Do parks really affect all players equally? Probably not. Difficult to analyze ◦ Measuring a park effect requires large samples ◦ Most parks are fairly neutral ◦ Extreme parks tend to get altered SABERSEMINAR 2015 @BLABABOUTBALL
3. 3. If only there were an old park with an extreme effect… SABERSEMINAR 2015 @BLABABOUTBALL
4. 4. (Mostly) unchanged since 1940 At least one extreme park effect ◦ 30%+ impact on doubles, >25% for both LHH and RHH Fenway fits the bill on both fronts SABERSEMINAR 2015 @BLABABOUTBALL
5. 5. Found a park… how do we examine it? Split hitters into extreme groups and see if they’re affected differently To do that: ◦ Calculate 2B/PA deltas for visitors to Fenway vs. other road games, 1956-2014 ◦ Group hitters into quartiles each year by projected 2B/PA rate (using Marcel) ◦ Find weighted averages by quartile and hand And the results say… SABERSEMINAR 2015 @BLABABOUTBALL
6. 6. For RHH, Fenway doesn’t discriminate ∆=0.008 ∆=0.004 ∆=0.009 ∆=0.008 0 0.01 0.02 0.03 0.04 0.05 0.06 1st Quartile 2nd Quartile 3rd Quartile 4th Quartile Actual RHH 2B/PA rates, by projected rate quartiles Ex-Fenway 2B/PA rate Fenway 2B/PA delta SABERSEMINAR 2015 @BLABABOUTBALL
7. 7. What about for LHH? ∆=0.007 ∆=0.008 ∆=0.009 ∆=0.015 1.19x 1.20x 1.21x 1.34x 0 0.01 0.02 0.03 0.04 0.05 0.06 1st Quartile 2nd Quartile 3rd Quartile 4th Quartile Actual LHH 2B/PA rates, by projected rate quartiles Ex-Fenway 2B/PA rate Fenway 2B/PA delta SABERSEMINAR 2015 @BLABABOUTBALL
8. 8. Do Fenway’s changes matter? Yes… 90 100 110 120 130 140 150 160 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Average 2B/PA Fenway Park Effect, by Batter Hand L (5 yr avg) R (5 yr avg) SABERSEMINAR 2015 @BLABABOUTBALL Roofsuites EMCClub MonsterSeats BudweiserRFRoof CocaColaPartyDeck
9. 9. … But not for RHH/LHH weirdness -0.010 -0.005 0.000 0.005 0.010 0.015 0.020 0.025 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Q4 Delta - Q1 Delta, by Batter Hand L (10 yr avg) R (10 yr avg) SABERSEMINAR 2015 @BLABABOUTBALL Roofsuites EMCClub MonsterSeats BudweiserRFRoof CocaColaPartyDeck
10. 10. So what is the explanation? It’s all about asymmetry: SABERSEMINAR 2015 @BLABABOUTBALL Data source: Gameday 2007 hit data, converted to feet using method from Peter Jensen: http://www.hardballtimes.com/using-gameday-to-build-a-fielding-metric-part-1/ 75th percentile distance, by batter quartile 25th percentile distance, by batter quartile
11. 11. Conclusions Shocking revelation! Some lefties can exploit Fenway’s dimensions more than others. More interesting: at the extremes, park effects can impact different types of players differently But… there may not be another park effect like this one, now or ever SABERSEMINAR 2015 @BLABABOUTBALL
12. 12. Questions? SABERSEMINAR 2015 @BLABABOUTBALL
13. 13. Appendix: Pitchers are not the difference Avg 2B/PA allowed vs. batter hand Avg wOBA allowed vs. batter hand Batter group P faced ex-Fenway P faced at Fenway P faced ex-Fenway P faced at Fenway LHH 4th quartile 0.042 0.046 0.334 0.331 LHH 1st quartile 0.041 0.046 0.335 0.332 Difference 0.001 0.001 -0.001 -0.001 Batter group P faced ex-Fenway P faced at Fenway P faced ex-Fenway P faced at Fenway RHH 4th quartile 0.041 0.043 0.323 0.329 RHH 1st quartile 0.041 0.043 0.324 0.330 Difference -0.001 0.001 -0.001 -0.001 SABERSEMINAR 2015 @BLABABOUTBALL