An Augmented Rating System for Test cricket: adapting Glicko's model

The International Cricket Council's (ICC) ranking system for Test cricket does not adequately account for key contextual factors such as home advantage, the toss outcome and scheduling imbalances, leading to inconsistencies in team evaluation.

This study develops an enhanced rating framework by adapting Glicko's model to incorporate these influences within a probabilistically grounded expected score formulation.

The rating scale is re-calibrated 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 found to be 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% decisive matches in the first cycle and yields a team ordering closely aligned with the ICC (Spearman rank correlation 0.967).

Benchmarked against the standard Elo and the unmodified Glicko systems, it matches their predictive accuracy while producing markedly better calibrated win probabilities, attaining a lower Brier score and log loss in both cycles.

A resampling based test of significance over 1000 permutations of match sequence confirms that the ratings are stable and governed by match outcomes rather than by fixture scheduling.

Overall, the framework offers a fairer, more interpretable and statistically consistent approach to ranking Test teams.