  Ranking sportsmen whose careers took place in different eras is often a
contentious issue and the topic of much debate. In this paper we focus on
cricket and examine what conclusions may be drawn about the ranking of Test
batsmen using data on batting scores from the first Test in 1877 onwards. The
overlapping nature of playing careers is exploited to form a bridge from past
to present so that all players can be compared simultaneously, rather than just
relative to their contemporaries. The natural variation in runs scored by a
batsman is modelled by an additive log-linear model with year, age and
cricket-specific components used to extract the innate ability of an individual
cricketer. Incomplete innings are handled via censoring and a zero-inflated
component is incorporated into the model to allow for an excess of frailty at
the start of an innings. The innings-by-innings variation of runs scored by
each batsman leads to uncertainty in their ranking position. A Bayesian
approach is used to fit the model and realisations from the posterior
distribution are obtained by deploying a Markov Chain Monte Carlo algorithm.
Posterior summaries of innate player ability are then used to assess
uncertainty in ranking position and this is contrasted with rankings determined
via the posterior mean runs scored. Posterior predictive checks show that the
model provides a reasonably accurate description of runs scored.
