Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach

TL;DR

This paper explores machine learning techniques to predict T20 cricket match outcomes by utilizing player statistics, ratings, clustering, and a novel ELO-based rating system, comparing various algorithms for accuracy.

Contribution

It introduces a new ELO-based player rating method and compares multiple ML approaches for cricket match prediction using diverse feature engineering techniques.

Findings

ELO-based player ratings improve prediction accuracy.

Support vector machines outperform other models in this context.

Clustering players enhances feature representation.

Abstract

Twenty20 cricket, sometimes written Twenty-20, and often abbreviated to T20, is a short form of cricket. In a Twenty20 game the two teams of 11 players have a single innings each, which is restricted to a maximum of 20 overs. This version of cricket is especially unpredictable and is one of the reasons it has gained popularity over recent times. However, in this paper we try four different machine learning approaches for predicting the results of T20 Cricket Matches. Specifically we take in to account: previous performance statistics of the players involved in the competing teams, ratings of players obtained from reputed cricket statistics websites, clustering the players' with similar performance statistics and propose a novel method using an ELO based approach to rate players. We compare the performances of each of these feature engineering approaches by using different ML algorithms, including logistic regression, support vector machines, bayes network, decision tree, random forest.