Pitch Sequence Complexity and Long-Term Pitcher Performance

Sports

Joel R. Bock¹ 

[1] Strategic Innovation Group, Booz Allen Hamilton, 901 15th Street NW, Washington, DC 20005, USA; E-Mail

关键词: machine learning;    sequence complexity;    sports analytics;    performance prediction;   

DOI  :  10.3390/sports3010040

来源: mdpi

【 摘 要 】

Winning one or two games during a Major League Baseball (MLB) season is often the difference between a team advancing to post-season play, or “waiting until next year”. Technology advances have made it feasible to augment historical data with in-game contextual data to provide managers immediate insights regarding an opponent’s next move, thereby providing a competitive edge. We developed statistical models of pitcher behavior using pitch sequences thrown during three recent MLB seasons (2011–2013). The purpose of these models was to predict the next pitch type, for each pitcher, based on data available at the immediate moment, in each at-bat. Independent models were developed for each player’s most frequent four pitches. The overall predictability of next pitch type is ). With further development, such models may reduce risk faced by management in evaluation of potential trades, or to scouts assessing unproven emerging talent. Pitchers themselves might benefit from awareness of their individual statistical tendencies, and adapt their behavior on the mound accordingly. To our knowledge, the predictive model relating pitch-wise complexity and long-term performance appears to be novel.

【 授权许可】

CC BY   
© 2015 by the author; licensee MDPI, Basel, Switzerland.