Bayesian survival analysis of batsmen in Test cricket

TL;DR

This paper introduces a Bayesian survival analysis approach to model and predict the batting abilities of Test cricket players, capturing how players improve during innings and informing team strategy.

Contribution

It develops a hierarchical Bayesian model to quantify initial and equilibrium batting abilities and applies it to New Zealand opening batsmen for practical insights.

Findings

Most players start with 25-50% of their potential ability

The model predicts future batting abilities of debutants

Identifies top-performing opening batsmen

Abstract

Cricketing knowledge tells us batting is more difficult early in a player's innings but becomes easier as a player familiarizes themselves with the conditions. In this paper, we develop a Bayesian survival analysis method to predict the Test Match batting abilities for international cricketers. The model is applied in two stages, firstly to individual players, allowing us to quantify players' initial and equilibrium batting abilities, and the rate of transition between the two. This is followed by implementing the model using a hierarchical structure, providing us with more general inference concerning a selected group of opening batsmen from New Zealand. The results indicate most players begin their innings playing with between only a quarter and half of their potential batting ability. Using the hierarchical structure we are able to make predictions for the batting abilities of the next opening batsman to debut for New Zealand. Additionally, we compare and identify players who excel in the role of opening the batting, which has practical implications in terms of batting order and team selection policy.