Sports Data Mining for Cricket Match Prediction

Abstract

Millions of people around the globe are fond of cricket. With such a big fan base, teams are sure to be competitive. Teams usually try to get their players to play at their full potential. Like many other sports, cricket also has a large amount of data available per match. Data collected from these matches can be used to provide insights on why a specific match resulted in a certain outcome. Data related to team performance and individual player statistics can be retrieved from each match. Analysis of these data helps the team improve their player performance. However, sometimes factors like venue, toss winning, and ranking can also affect the result of a match. In this paper, we design and implement sports data mining approaches to predict match results. In particular, we focus on cricket. Evaluation on real-life T20 International World Cup data—which examine and analyze factors like venue, toss winning, team ranking, and matches won against a specific opponent to predict the winner of a given match—demonstrates the practicality and effectiveness of our sport data mining and prediction approaches.