Predicting Chess Player Rating Based on a Single Game.

Traditionally, the relative strength of a chess player within a competitive pool is identified by a rating number. In order to reach a fair rating that best represents their level of play, chess players are required to play numerous games against various opponents within that pool. However, intuitively, experienced chess players are capable of extracting a rough estimate of a player’s strength by looking at the moves they made in a single game. How accurately could a machine learning model based on a large dataset of chess games predict player ratings from a single game, and what would these predictions depend on? This paper presents an attempt to identify, encode and model chess gameplay features in order to predict a player’s rating from a single game played. If successful, such a model could be employed to attach a fair initial rating to a new player within a pool before any games are played. We use an extensive dataset of chess games downloaded from a popular online chess platform, from which we extract a set of 30 features which are used to model and ultimately predict players’ ratings. Our findings show that we are capable of predicting the rating bracket of a player with 79.3% accuracy when considering the extreme ends of the dataset (lowest vs. highest rated players), while the accuracy consistently drops as we increase the respective bracket width. We discovered that the most important features of our predictive models are both theory-and engine-related; most importantly, the features that we have extracted lead to explainable, quantifiable predictions of chess player strength.