CricSquad: A System to Recommend Ideal Players to a Particular Match and Predict the Outcome of the Match

• Published in: 2023 3rd International Conference on Advanced Research in Computing (ICARC) pp. 42 - 47
• Main Authors: Lekamge, E. L., Wickramasinghe, K. R., Gamage, S. E., Thennakoon, T. M. K. L., Haddela, Prasanna S., Senaratne, Sandamini
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23-02-2023
• Subjects: Association Rule mining; Classification Algorithm; Classification algorithms; Data mining; Engineering profession; Machine Learning; Machine learning algorithms; Neural Network; Prediction algorithms; Predictive models; Random forests; Regression
• DOI: 10.1109/ICARC57651.2023.10145677

Selection of the cricket squad plays a very important role in the outcome of the match. This work is about selecting ideal players for a cricket match and predicting the outcome of the match according to the selected cricket team. A cricket squad consist of around 15 to 16 players, with different expertise in batting, bowling, fielding. To select players for the squad, points were calculated using a statistical approach considering player's overall career data. And then for the further use of selecting players for the squad next match performance of each and every player were predicted using Machine Learning techniques. Association rule mining was used to find frequent winning player combinations with day/night, home/away, batting first/second, against different opponent combinations. Finally calculate points for each player in both teams, then predict the outcome of the match with classification algorithms by considering the calculated total points of each team and other factors such as toss outcome, batting inning, day night conditions and venue. As for the results, XG boost regressor has produced the highest R2 score of 0.92 for batsman runs prediction model while random forest regressor has produced the highest R2 score of 0.66 for bowler wickets prediction model. The Gradient Boost Classifier predicted the Outcome of a match with the highest accuracy of 0.92 while the K Nearest Neighbor achieved the lowest accuracy of 0.82 score.