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Probability estimation models play an important role in various fields, suchas weather forecasting, recommendation systems, and sports analysis. Amongseveral models estimating probabilities, it is difficult to evaluate whichmodel gives reliable probabilities since the ground-truth probabilities are notavailable. The win probability estimation model for esports, which calculatesthe win probability under a certain game state, is also one of the fields beingactively studied in probability estimation. However, most of the previous worksevaluated their models using accuracy, a metric that only can measure theperformance of discrimination. In this work, we firstly investigate the Brierscore and the Expected Calibration Error (ECE) as a replacement of accuracyused as a performance evaluation metric for win probability estimation modelsin esports field. Based on the analysis, we propose a novel metric calledBalance score which is a simple yet effective metric in terms of six goodproperties that probability estimation metric should have. Under the generalcondition, we also found that the Balance score can be an effectiveapproximation of the true expected calibration error which has been imperfectlyapproximated by ECE using the binning technique. Extensive evaluations usingsimulation studies and real game snapshot data demonstrate the promisingpotential to adopt the proposed metric not only for the win probabilityestimation model for esports but also for evaluating general probabilityestimation models.

Rethinking Evaluation Metric for Probability Estimation Models Using Esports Data