An augmented ratingsystem for Test cricket: adapting the Glicko rating system

Abstract The International Cricket Council’s (ICC) rating system for Test cricket updates a team’s rating from match and series results alone. It makes no allowance for the precision of a rating, nor for contextual factors such as home advantage and the outcome of the toss. This study develops an enhanced rating framework by adapting the Glicko rating system so that these influences enter a probabilistically grounded expected score. The rating scale is recalibrated for the dynamics of Test cricket, and the home-ground and toss effects enter as data-driven covariates whose statistical significance and relative weights are estimated directly from match data: playing at home is worth an advantage of about 13 rating points and winning the toss about 8, and the two effects combine additively, with no statistically significant interaction. Applied to the two completed World Test Championship cycles (2021–23 and 2023–25), the model correctly predicts 77.6 % of decisive matches in the first cycle. Benchmarked against the standard Elo and the unmodified Glicko systems, it matches the better of their predictive accuracies and produces considerably better calibrated win probabilities, with a lower Brier score and log loss in both cycles. A resampling test over 1,000 permutations of each cycle’s match sequence shows every team’s final rating to be invariant to the order in which the fixtures are played. We present this as robustness to match ordering and distinguish it from fairness with respect to the fixture list, which the unbalanced Test calendar does not allow us to assess. The resulting ordering of teams agrees very closely with the ICC’s (Spearman rank correlation 0.979 and 0.983 at the end of the two cycles), so the improvement we demonstrate is not a different ranking but what accompanies it: a calibrated win probability for every match, an explicit rating deviation on every rating and contextual adjustments whose magnitude is estimated, none of which the current ICC scheme supplies.

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Publication Details

Journal
Journal of Quantitative Analysis in Sports
Published
2026-10-07
DOI
https://doi.org/10.1515/jqas-2026-0020
Primary Topic
Sports Analytics and Performance
Type
article
Field-Weighted Citation Impact
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article

An augmented ratingsystem for Test cricket: adapting the Glicko rating system

Diganta Mukherjee, Rhitankar Bandyopadhyay
Journal of Quantitative Analysis in Sports
Sports Analytics and Performance
article

An augmented ratingsystem for Test cricket: adapting the Glicko rating system

Diganta Mukherjee, Rhitankar Bandyopadhyay
article en

Abstract

Abstract The International Cricket Council’s (ICC) rating system for Test cricket updates a team’s rating from match and series results alone. It makes no allowance for the precision of a rating, nor for contextual factors such as home advantage and the outcome of the toss. This study develops an enhanced rating framework by adapting the Glicko rating system so that these influences enter a probabilistically grounded expected score. The rating scale is recalibrated for the dynamics of Test cricket, and the home-ground and toss effects enter as data-driven covariates whose statistical significance and relative weights are estimated directly from match data: playing at home is worth an advantage of about 13 rating points and winning the toss about 8, and the two effects combine additively, with no statistically significant interaction. Applied to the two completed World Test Championship cycles (2021–23 and 2023–25), the model correctly predicts 77.6 % of decisive matches in the first cycle. Benchmarked against the standard Elo and the unmodified Glicko systems, it matches the better of their predictive accuracies and produces considerably better calibrated win probabilities, with a lower Brier score and log loss in both cycles. A resampling test over 1,000 permutations of each cycle’s match sequence shows every team’s final rating to be invariant to the order in which the fixtures are played. We present this as robustness to match ordering and distinguish it from fairness with respect to the fixture list, which the unbalanced Test calendar does not allow us to assess. The resulting ordering of teams agrees very closely with the ICC’s (Spearman rank correlation 0.979 and 0.983 at the end of the two cycles), so the improvement we demonstrate is not a different ranking but what accompanies it: a calibrated win probability for every match, an explicit rating deviation on every rating and contextual adjustments whose magnitude is estimated, none of which the current ICC scheme supplies.

Journal of Quantitative Analysis in Sports
University of Florida (US), Indian Statistical Institute (IN)
Openalex Percentile: Top 88%
Sports Analytics and Performance
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