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.