Statistical models for match prediction and decision making in sport

In this study, we investigate models for the prediction of match outcome. Thesemodels are then used to aid decision-making. In particular, we consider battingstrategy in test cricket. This model provides decision support for a team that is aimingto set a target at declaration. We also develop a measure of the importance of a matchin a tournament. Such a measure may be of use in tournament design.Decision-making on the timing of a declaration in test cricket is considered usingmatch outcome probabilities given the state of a game. Logistic regression is used tomodel the effect of covariates, target set and overs remaining, on match outcomeprobabilities. This approach is then extended to establish batting strategy byconsidering run rate and the distribution of runs scored during a partnership. Adecision tool for batting strategy towards a target aimed for is established.The importance of a particular match in a tournament is measured given theoutcomes of all other matches. This method is illustrated for the English Premiership.Match importance is calculated with respect to winning the Championship, relegationfrom Premiership, qualifying for the UEFA Champions League and prize money.Match outcome probabilities for the match of interest are estimated using an ordinallogistic regression model. Covariates that represent the short and long termperformance of the competing teams are used in this prediction model.This thesis makes the following contributions regarding the application of statisticalmethods in sport. A new quantitative approach that considers the optimum declaration"time" in test cricket is developed. We consider this modelling of fundamentalplaying strategy to be novel. We find that a zero-inflated negative binomialdistribution is a good model for the distribution of runs scored in test cricket. Thematch importance measure that we describe extends an existing definition. The matchoutcome model we use for calculating match importance considers novel covariatesrelated to the recent results of teams.

Level dc:type.qualificationlevel

Doctoral (Level 8)

Year dc:date.issued

2008

Author dc:creator

• Shi, X

Language dc:language

en

Identifier

oai:salford-repository.worktribe.com:1336961

OAI identifier oai:identifier

oai:salford-repository.worktribe.com:1336961