Browsing by Author "Rajapaksha, R. A. R. S."

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Smart Cricket: Strategic batting place prediction and player ranking with ensemble learning
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Rajapaksha, R. A. R. S.; Kumarika, B. M. T.
Cricket is a global sport with three formats, of which T20 is the fastest format. A team’s batting order decides the outcome of the match as each player must play according to their position. Therefore, assigning roles to batsmen according to their position is crucial. Previous studies have focused on win prediction, team selection, player performance evaluation, and player classification using machine learning. However, there appears to be a lack of research on classifying batsmen according to their positions and ranking players within each category. This study aims to classify players based on their roles in the batting order, as top-order, middle-order, lower-order, or tail-ender batsmen in the T20 format, and rank players within each category using classification probabilities. Data were collected from www.cricinfo.com, encompassing 347 players from countries that participate in Test-format cricket. For this study, 15 batting features were used for model building. Additionally, the dataset was split into training and testing sets, with 75% of the data used for training and 25% for testing. The training set was used for model development and the testing set for performance evaluation. Initially, classification was done using machine learning models such as Naïve Bayes, Random Forest, Decision Tree, SVM, and KNN. Then, ensemble learning techniques including boosting, voting, and stacking on different combinations of base models were employed to improve model performance. The best performance was observed with a stacking model that had seven base models. This model had a training accuracy of 97.69% and a testing accuracy of 95.40%. It also had a precision of 95.16%, recall of 95.35%, and F1-score of 95.23%. The findings of this study show that ensemble models can classify batsmen accurately according to their positions and rank players within each category. This classification and ranking system is highly valuable for selectors, coaches, captains, and team management for team selection, forming batting lineups, and tasks like strategic player replacement. Also, this study is beneficial for players to understand and improve their performance. Furthermore, this classification is helpful in franchise leagues, particularly for player selection in auctions. Additionally, this study proves that using ensemble models with different techniques and multiple base models can significantly enhance predictive performance. Future work will focus on improving the predictions with scheduled and continued training.