A Machine Learning Approach to Forecasting Outcomes in Limited Overs Cricket - Nagajayant Nagamani (Recent Trends in Sports, 2025)
This study explores the application of machine learning techniques to forecasting outcomes in limited overs cricket matches, with a particular focus on One Day Internationals (ODIs). The research investigates how classification algorithms can be effectively utilized to analyze both contextual and dynamic factors that influence match results, including venue details, toss decisions, team strength, and historical performance records. By employing a structured methodology encompassing feature selection, data preprocessing, model training, and comparative performance evaluation, the study aims to demonstrate the feasibility and accuracy of predictive analytics in cricket. Two supervised learning approaches, Decision Trees and Multilayer Perceptron (MLP) neural networks, were implemented and tested on a curated dataset of over 3,900 ODI matches spanning nearly five decades. The models were assessed using established performance metrics such as Accuracy, Precision, Recall, F1 Score, and Average Precision, ensuring a comprehensive evaluation of predictive capability. The findings highlight that machine learning models can capture the nonlinear interactions among game-related variables and substantially improve forecasting accuracy compared to traditional statistical methods. Additionally, this work introduces a practical implementation through a custom-built application, CricAI, which enables users to input match-specific details and receive outcome predictions in real time. Beyond predictive performance, the study provides valuable insights into the key determinants of ODI outcomes, underscoring the importance of domain-specific feature engineering. By bridging theory with practical application, this research contributes to the growing field of sports analytics and demonstrates the potential of machine learning to enhance strategic planning, fan engagement, and decision-making in competitive cricket. Looking ahead, the framework can be extended to incorporate real-time, in-game data streams to enable dynamic outcome forecasting, paving the way for more adaptive and interactive sports analytics systems.
Keywords: Machine Learning in Sports; Predictive Modeling in Cricket; Sports Data Analytics; Sports Outcome Prediction; Supervised Learning Algorithms