  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.
