A Method for Estimating Online Chess Game Player Ratings with Decision Tree

Chess is a popular game among many people worldwide and is frequently played online. Although players are ranked based on existing rating systems, automation is essential for coordinating matches in tournaments with thousands of participants. In this study, we analyzed the game records of highly skilled chess players and determined appropriate ratings for players without ratings by using a decision tree model. It was determined that high-rated and low-rated players could be identified with accuracy levels above 80%, even for players who were not included at the time of model training.