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.

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.

In the sport of cricket, a player’s batting ability is traditionally measured using the batting average. However, the batting average fails to measure both short‐term changes in ability that occur during an innings and long‐term changes in ability that occur between innings due to factors such as age and experience in various match conditions. We derive and fit a Bayesian parametric model that employs a Gaussian process to measure and predict how the batting abilities of cricket players vary and fluctuate over the course of entire playing careers. The results allow us to better quantify and predict the batting ability of a player, compared with both traditional cricket statistics, such as the batting average, and more complex models, such as the official International Cricket Council ratings.

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