A Bayesian stochastic model for batting performance evaluation in one-day cricket

Koulis Theodoro (), Muthukumarana Saman and Briercliffe Creagh Dyson
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Koulis Theodoro: Department of Statistics, University of Manitoba, Winnipeg Manitoba, Canada
Muthukumarana Saman: Department of Statistics, University of Manitoba, Winnipeg Manitoba, Canada
Briercliffe Creagh Dyson: Department of Statistics, University of Manitoba, Winnipeg Manitoba, Canada

Journal of Quantitative Analysis in Sports, 2014, vol. 10, issue 1, 1-13

Abstract: We consider the modeling of individual batting performance in one-day international (ODI) cricket by using a batsman-specific hidden Markov model (HMM). The batsman-specific number of hidden states allows us to account for the heterogeneous dynamics found in batting performance. Parallel sampling is used to choose the optimal number of hidden states. Using the batsman-specific HMM, we then introduce measures of performance to assess individual players via reliability analysis. By classifying states as either up or down, we compute the availability, reliability, failure rate and mean time to failure for each batsman. By choosing an appropriate classification of states, an overall prediction of batting performance of a batsman can be made. The classification of states can also be modified according to the type of game under consideration. One advantage of this batsman-specific HMM is that it does not require the consideration of unforeseen factors. This is important since cricket has gone through several rule changes in recent years that have further induced unforeseen dynamic factors to the game. We showcase the approach using data from 20 different batsmen having different underlying dynamics and representing different countries.

Keywords: Gibbs sampling; Hidden Markov model; parallel sampling; reliability analysis (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/jqas-2013-0057

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