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Pitching Statistics, Talent and Luck, and the Best Strikeout Seasons of All-Time

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  • Albert James

    (Bowling Green University)

Abstract

Many count statistics are used to evaluate pitchers such as the number of wins and losses, the number of strikeouts, the number of walks, and the number of runs allowed. For a given measure such as strikeouts, this paper focuses on the estimation of pitchers' probabilities of striking out a batter. The variation in the season strikeout rates among a group of pitchers is due to differences in the pitchers' probabilities and also due to chance binomial variation. Among all the various rates, we find that a strikeout rate is one of the most accurate estimates of the corresponding probability of a pitcher performing the associated task. We examine the distribution of strikeout, walk and runs-prevented "true" rates of pitchers across the years. By use of our model, we are able to judge the magnitude of a great strikeout season. A z-score statistic is used to rank the greatest strikeout seasons of baseball history and this ranking is contrasted with other traditional ways of ranking pitchers.

Suggested Citation

  • Albert James, 2006. "Pitching Statistics, Talent and Luck, and the Best Strikeout Seasons of All-Time," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(1), pages 1-32, January.
    • Handle: RePEc:bpj:jqsprt:v:2:y:2006:i:1:n:2
    DOI: 10.2202/1559-0410.1014
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    Citations

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    Cited by:

    1. Null Brad, 2009. "Modeling Baseball Player Ability with a Nested Dirichlet Distribution," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(2), pages 1-38, May.
    2. Phillips Andrew J. K., 2014. "Uncovering Formula One driver performances from 1950 to 2013 by adjusting for team and competition effects," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 1-18, June.
    3. Albert Jim, 2016. "Improved component predictions of batting and pitching measures," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(2), pages 73-85, June.
    4. Albert Jim, 2010. "Using the Count to Measure Pitching Performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(4), pages 1-30, October.
    5. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.
    6. Łukasz Szczepański & Ian McHale, 2016. "Beyond completion rate: evaluating the passing ability of footballers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 513-533, February.
    7. Piette James & Braunstein Alexander & McShane Blakeley B & Jensen Shane T., 2010. "A Point-Mass Mixture Random Effects Model for Pitching Metrics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(3), pages 1-17, July.
    8. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.
    9. Baumer Ben S. & Piette James & Null Brad, 2012. "Parsing the Relationship between Baserunning and Batting Abilities within Lineups," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(2), pages 1-19, June.
    10. Sumit Sarkar & Sooraj Kamath, 2023. "Does luck play a role in the determination of the rank positions in football leagues? A study of Europe’s ‘big five’," Annals of Operations Research, Springer, vol. 325(1), pages 245-260, June.
    11. Albert Jim, 2013. "Looking at spacings to assess streakiness," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(2), pages 151-163, June.
    12. Vock David Michael & Vock Laura Frances Boehm, 2018. "Estimating the effect of plate discipline using a causal inference framework: an application of the G-computation algorithm," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 37-56, June.

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