Statistical models for match prediction and decision making in sport

Shi, X

Abstract

In this study, we investigate models for the prediction of match outcome. These
models are then used to aid decision-making. In particular, we consider batting
strategy in test cricket. This model provides decision support for a team that is aiming
to set a target at declaration. We also develop a measure of the importance of a match
in 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 using
match outcome probabilities given the state of a game. Logistic regression is used to
model the effect of covariates, target set and overs remaining, on match outcome
probabilities. This approach is then extended to establish batting strategy by
considering run rate and the distribution of runs scored during a partnership. A
decision tool for batting strategy towards a target aimed for is established.
The importance of a particular match in a tournament is measured given the
outcomes of all other matches. This method is illustrated for the English Premiership.
Match importance is calculated with respect to winning the Championship, relegation
from Premiership, qualifying for the UEFA Champions League and prize money.
Match outcome probabilities for the match of interest are estimated using an ordinal
logistic regression model. Covariates that represent the short and long term
performance of the competing teams are used in this prediction model.
This thesis makes the following contributions regarding the application of statistical
methods in sport. A new quantitative approach that considers the optimum declaration
"time" in test cricket is developed. We consider this modelling of fundamental
playing strategy to be novel. We find that a zero-inflated negative binomial
distribution is a good model for the distribution of runs scored in test cricket. The
match importance measure that we describe extends an existing definition. The match
outcome model we use for calculating match importance considers novel covariates
related to the recent results of teams.

Citation

Shi, X. Statistical models for match prediction and decision making in sport. (Thesis). University of Salford

• Thesis Type: Thesis
• Deposit Date: Aug 19, 2021
• Award Date: Jan 1, 2008