Modelling career trajectories of cricket players using Gaussian processes

Abstract

In the sport of cricket, variations in a player’s batting ability can usually be measured on one of two scales. Short-term changes in ability that are observed during a single innings, and long-term changes that are witnessed between matches, over entire playing careers. To measure long-term variations, we derive a Bayesian parametric model that uses a Gaussian process to measure and predict how the batting abilities of international cricketers fluctuate between innings. The model is fitted using nested sampling given its high dimensionality and for ease of model comparison. Generally speaking, the results support an anecdotal description of a typical sporting career. Young players tend to begin their careers with some raw ability, which improves over time as a result of coaching, experience and other external circumstances. Eventually, players reach the peak of their career, after which ability tends to decline. The model provides more accurate quantifications of current and future player batting abilities than traditional cricketing statistics, such as the batting average. The results allow us to identify which players are improving or deteriorating in terms of batting ability, which has practical implications in terms of player comparison, talent identification and team selection policy.