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Rhitankar Bandyopadhyay

Publications and source records attributed to Rhitankar Bandyopadhyay.

4 recordsLinked to original sources

An Augmented Rating System for Test cricket: adapting the Glicko rating system

The International Cricket Council's (ICC) Test cricket ratings use match and series results alone, stating no allowance about the precision of a rating, or about home advantage and toss. We fold both into the Glicko rating system, which gives a probabilistic expected score, and recalibrate its scale for Test cricket. The two enter as covariates whose significance and weights are estimated from match data. Home advantage is worth approximately 13 rating points and the toss roughly 8. They add, with no significant interaction. Over the two completed World Test Championship cycles (2021-23 and 2023-25) the model predicts 77.6% of decisive matches correctly in the first, and matches whichever of standard Elo and unmodified Glicko rating system is more accurate, at a lower Brier score and log loss in both. Permuting each cycle's match order 1000 times leaves every final rating unchanged. The model shows robustness to ordering, not fairness towards the fixture list, which the unbalanced calendar prevents judging. Our ordering follows the ICC's (Spearman 0.979 and 0.983). We add not a different ranking but a calibrated win probability per match, a deviation on every rating, and home and toss adjustments of estimated size, none of which the ICC supplies.

stat.AP

Context-adjusted Player Evaluation for Twenty20 Cricket

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.

stat.AP

Asymptotic Minimax Estimation under Global-Local Priors

Global-local priors, often also referred to as shrinkage priors, have proved to be a very effective tool for the analysis of high dimensional data under sparsity. Asymptotic theoretical properties of such priors, studied under various scenarios, are now available in the literature. However, to our knowledge, theoretical guarantees of such priors provided so far, involve the assumption of known sample variance. The present paper relaxes this assumption, and carries out the analysis with a prior assigned to the error variance as well. In the process, some new tail bounds for shrinkage factors are developed, and these results are then utilized in providing asymptotic minimax rates for the posterior means of the parameters of interest.

math.ST

Applications of higher order Markov models and Pressure Index to strategize controlled run chases in Twenty20 cricket

In limited overs cricket, the team batting first posts a target score for the team batting second to achieve in order to win the match. The team batting second is constrained by decreasing resources in terms of number of balls left and number of wickets in hand in the process of reaching the target as the second innings progresses. The Pressure Index, a measure created by researchers in the past, serves as a tool for quantifying the level of pressure that a team batting second encounters in limited overs cricket. Through a ball-by-ball analysis of the second innings, it reveals how effectively the team batting second in a limited-over game proceeds towards their target. This research employs higher order Markov chains to examine the strategies employed by successful teams during run chases in Twenty20 matches. By studying the trends in successful run chases spanning over 16 years and utilizing a significant dataset of 6537 Twenty20 matches, specific strategies are identified. Consequently, an efficient approach to successful run chases in Twenty20 cricket is formulated, effectively limiting the Pressure Index to [0.5, 3.5] or even further down under 0.5 as early as possible. The innovative methodology adopted in this research offers valuable insights for cricket teams looking to enhance their performance in run chases.

stat.AP