AI-powered mechanisms as judges: Breaking ties in chess

TL;DR

This paper introduces an AI-based tiebreaking method for chess that assesses move quality against optimal engine suggestions, improving fairness and game integrity in high-level competitions.

Contribution

It presents a novel AI-driven tiebreak mechanism that evaluates move quality to objectively resolve ties in chess tournaments.

Findings

The method effectively distinguishes player skill levels based on move quality.

Application to historical grandmaster games demonstrates its robustness.

The approach enhances fairness without compromising game standards.

Abstract

Recently, Artificial Intelligence (AI) technology use has been rising in sports to reach decisions of various complexity. At a relatively low complexity level, for example, major tennis tournaments replaced human line judges with Hawk-Eye Live technology to reduce staff during the COVID-19 pandemic. AI is now ready to move beyond such mundane tasks, however. A case in point and a perfect application ground is chess. To reduce the growing incidence of ties, many elite tournaments have resorted to fast chess tiebreakers. However, these tiebreakers significantly reduce the quality of games. To address this issue, we propose a novel AI-driven method for an objective tiebreaking mechanism. This method evaluates the quality of players' moves by comparing them to the optimal moves suggested by powerful chess engines. If there is a tie, the player with the higher quality measure wins the tiebreak. This approach not only enhances the fairness and integrity of the competition but also maintains the game's high standards. To show the effectiveness of our method, we apply it to a dataset comprising approximately 25,000 grandmaster moves from World Chess Championship matches spanning from 1910 to 2018, using Stockfish 16, a leading chess AI, for analysis.