Cricket Match Outcome Forecasting Using Historical Data: A Machine Learning Study on India’s Test Matches

Abstract

This study presents a data-driven investigation into the prediction of match outcomes for India’s Test cricket matches spanning a historical timeline from 1932 to 2022. Leveraging a dataset comprising 561 match instances with 13 curated features including match venue, toss result, opposition team, and team form this research applies three supervised machine learning algorithms: Decision Tree, Logistic Regression, and Random Forest. The objective is to assess each model’s effectiveness in forecasting binary outcomes: win or loss.
Unlike prior studies that emphasize limited datasets or contemporary formats such as T20 or ODI, this work explores the long-format Test cricket context, offering a comprehensive view of performance dynamics over nine decades. Feature engineering strategies were employed to enrich contextual variables, while cross-validation techniques ensured model robustness.
The Random Forest model demonstrated superior performance in terms of accuracy and generalization, suggesting its suitability for capturing the nonlinear complexities of Test match outcomes. This research contributes to the domain of sports analytics by offering a replicable framework for long-term performance forecasting, and highlights the evolving nature of team dynamics, strategy, and opposition patterns over time.
The findings not only support the potential of machine learning in historical sports data analysis but also serve as a foundation for strategic planning and predictive modeling in cricket and similar domains.