Enhancing Cricket Analytics Through Machine Learning: A Case Study on Player/Team Ranking Algorithms

Abstract

This study explores the enhancement of cricket analytics through machine learning, focusing on the Rain Rule methodology and player/team ranking algorithms. Traditional cricket ranking systems, while widely used, often lack depth in contextual understanding and adaptability, limiting their accuracy across cricket's various formats (Test, ODI, T20). These conventional systems primarily rely on simple win-loss metrics, failing to account for match conditions, opponent strength, or format-specific
requirements. This study aims to address these limitations by developing a machine learning-based ranking framework that integrates performance metrics and contextual nuances to produce a more precise ranking system.
The research employs a case study approach, applying machine learning models such as regression analysis, decision trees, and neural networks to rank cricket players and teams. Key data sources include historical match data and performance records,
analyzed using quantitative and qualitative methods. Comparative analysis of traditional ranking systems versus the proposed machine learning-based model shows that the latter provides enhanced predictive accuracy and fairness in player evaluations.
Findings indicate that machine learning algorithms can adapt to the complexities of cricket, offering a ranking system that captures the intricacies of each format and improves decision-making for stakeholders, including team selectors, coaches, and fans.
Additionally, the machine learning framework's flexibility supports real-time adjustments, reflecting players' current forms more accurately than traditional methods. This study contributes to the growing field of sports analytics, showcasing machine
learning's potential to redefine cricket rankings and provide stakeholders with actionable insights for improving game strategy and player management.