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Bayesian binary segmentation procedure for detecting streakiness in sports
Author(s) -
Yang Tae Young
Publication year - 2004
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2004.00484.x
Subject(s) - bayesian probability , computer science , bayes factor , competitor analysis , binary number , segmentation , constant (computer programming) , bayes' theorem , basketball , artificial intelligence , mathematics , marketing , business , arithmetic , programming language , archaeology , history
Summary. When an individual player or team enjoys periods of good form, and when these occur, is a widely observed phenomenon typically called ‘streakiness’. It is interesting to assess which team is a streaky team, or who is a streaky player in sports. Such competitors might have a large number of successes during some periods and few or no successes during other periods. Thus, their success rate is not constant over time. We provide a Bayesian binary segmentation procedure for locating changepoints and the associated success rates simultaneously for these competitors. The procedure is based on a series of nested hypothesis tests each using the Bayes factor or the Bayesian information criterion. At each stage, we only need to compare a model with one changepoint with a model based on a constant success rate. Thus, the method circumvents the computational complexity that we would normally face in problems with an unknown number of changepoints. We apply the procedure to data corresponding to sports teams and players from basketball, golf and baseball.