Context-adjusted Player Evaluation for Twenty20 Cricket

Abstract

I develop a reproducible framework for evaluating individual batting and bowling performances in Twenty20 (T20) cricket on one interpretable scale of runs above expectation, built from two ball-level primitives. The first, Runs Above Expected (RAE), is the residual between the runs scored on a delivery and a contextual expectation of what an average performer would produce in the same situation. That expectation is a multiplicative log-linear model of the cohort scoring rate, whose per-cell estimator is shown to be a conditional Poisson maximum likelihood multiplier. It is fitted by iterated backfitting and stabilised by empirical Bayes shrinkage, so that thinly sampled contexts are pooled towards the population. A single opposition symmetry places run-scoring and run-prevention on the same footing. The second primitive, \emph{Dismissal Adjusted Runs} (DAR), prices a dismissal in that currency as the runs it forgoes, read off a batting side value function solved by dynamic programming through a Bellman expectation recursion, under observed play. Because dismissal is the expected end of every innings, the realised wicket cost (realDAR) is centered against its expectation (xDAR) under a league dismissal hazard rate. The centered quantity is a run-weighted mean zero martingale residual of the dismissal process, so a player is charged only for departing from average behaviour. The two primitives sum to a symmetric Impact, which makes batting and bowling comparable in centre as well as in unit. Estimated on over 2.7 million legal deliveries of men's T20 cricket, the framework recovers known contextual structure, agrees with the conventional rates it refines while correcting their context-blindness, and yields face-valid player, innings and season leaderboards for the Indian Premier League.