Increased Prediction Accuracy in the Game of Cricket using Machine Learning

TL;DR

This paper applies machine learning classifiers to predict cricket players' performance, specifically runs scored and wickets taken, aiming to improve team selection accuracy.

Contribution

It introduces a machine learning approach to predict cricket players' performance metrics as classification tasks, comparing multiple classifiers to identify the most accurate model.

Findings

Random Forest achieved highest accuracy among tested classifiers.

Prediction models effectively classify performance into ranges.

Machine learning enhances decision-making in cricket team selection.

Abstract

Player selection is one the most important tasks for any sport and cricket is no exception. The performance of the players depends on various factors such as the opposition team, the venue, his current form etc. The team management, the coach and the captain select 11 players for each match from a squad of 15 to 20 players. They analyze different characteristics and the statistics of the players to select the best playing 11 for each match. Each batsman contributes by scoring maximum runs possible and each bowler contributes by taking maximum wickets and conceding minimum runs. This paper attempts to predict the performance of players as how many runs will each batsman score and how many wickets will each bowler take for both the teams. Both the problems are targeted as classification problems where number of runs and number of wickets are classified in different ranges. We used na\"ive bayes, random forest, multiclass SVM and decision tree classifiers to generate the prediction models for both the problems. Random Forest classifier was found to be the most accurate for both the problems.