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SummaryThis paper develops a Twenty20 cricket simulator for matches between sides belonging to the International Cricket Council. As input, the simulator requires the probabilities of batting outcomes which are dependent on the batsman, the bowler, the number of overs consumed and the number of wickets lost. The determination of batting probabilities is based on an amalgam of standard classical estimation techniques and a hierarchical empirical Bayes approach where the probabilities of batting outcomes borrow information from related scenarios. Initially, the probabilities of batting outcomes are obtained for the first innings. In the second innings, the target score obtained from the first innings affects the aggressiveness of batting during the second innings. We use the target score to modify batting probabilities in the second innings simulation. This gives rise to the suggestion that teams may not be adjusting their second innings batting aggressiveness in an optimal way. The adequacy of the simulator is addressed through various goodness-of-fit diagnostics.

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This paper considers the determination of optimal team lineups in Twenty20 cricket where a lineup consists of three components: team selection, batting order and bowling order. Via match simulation, we estimate the expected runs scored minus the expected runs allowed for a given lineup. The lineup is then optimized over a vast combinatorial space via simulated annealing. We observe that the composition of an optimal Twenty20 lineup sometimes results in non-traditional roles for players. As a byproduct of the methodology, we obtain an 'all-star' lineup selected from international Twenty20 cricketers. ARTICLE HISTORY

Abstract. This paper introduces a new metric for player evaluation in Twenty20 cricket. The proposed metric of "expected run differential" measures the proposed additional runs that a player contributes to his team when compared to a standard player. Of course, the definition of a standard player depends on their role and therefore the metric is useful for comparing players that belong to the same positional cohort. We provide methodology to investigate both career performances and current form. Our metrics do not correlate highly with conventional measures such as batting average, strike rate, bowling average, economy rate and the Reliance ICC ratings. Consequently, our analyses of individual players based on results from international competitions provide some insights that differ from widely held beliefs. We supplement our analysis of player evaluation by investigating those players who may be overpaid or underpaid in the Indian Premier League.