Bridging Technology and Probability: A Bayesian Model for Enhanced Cricket Officiating

Authors: Arjun Agaram Mangad

DOI: https://doi.org/10.5281/zenodo.15054595

Short DOI: https://doi.org/g8837w

Country: USA

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Abstract: Integrating technology in sports officiating has led to significant advancements in decision-making accuracy, particularly with the implementation of Hawk-Eye technology in cricket. Hawk-Eye employs a deterministic physics-based model to predict ball trajectories and aid umpires in decisions such as leg before-wicket (LBW) calls. However, limitations include fixed error margins, reliance on predefined extrapolations, and the absence of real-time probabilistic updates. This paper proposes the incorporation of Bayesian probability models into Hawk-Eye's framework to enhance decision-making accuracy. Unlike current deterministic models, Bayesian inference would allow real-time updates to decision probabilities based on evolving match conditions. Before each match, sample deliveries are bowled under controlled conditions to collect baseline statistics on ball bounce, deviation, and pace. These data initialize Bayesian priors tailored to the pitch. During the game, every delivery's actual tracked trajectory (from bowler to batsman) refines the real-time probability estimates. The model continuously adapts to evolving factors such as ball wear, pitch degradation, and atmospheric changes, focusing on immediate conditions rather than historical bowler trends. Based on theoretical discussions in the paper, there is an indication that this integrated approach can increase the accuracy and consistency of LBW predictions as the system learns and adjusts live. We discuss the implications for umpiring accuracy and technology-assisted decisions and outline future research directions for implementing and validating this Bayesian Hawk-Eye model in live cricket matches.

Keywords: Cricket, Hawk-Eye, LBW, Bayesian theorem, Monty Hall Paradox, Models, DRS, Umpires

Paper Id: 232257

Published On: 2020-10-04

Published In: Volume 8, Issue 5, September-October 2020

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