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Activity Number: 539
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Sports
Abstract - #309994
Title: A Bayesian Stochastic Model for Batting Performance Evaluation in One-Day Cricket
Author(s): Theodoro Koulis*+ and Saman Muthukumarana
Companies: University of Manitoba and University of Manitoba
Keywords: Markov chain ; Bayesian ; Performance evaluation ; Forecasting ; Model selection
Abstract:

We consider a Bayesian ergodic Markov chain to model individual batting performance in one-day cricket. A cut-point vector is used to create a discrete state space for batting performance. By choosing an appropriate cut-point vector, the next performance state only depends on the current state, so that an overall prediction of batting performance of a batsman can be made. One advantage of this Markov model 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.


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