AI eye looking over chess board

Chess Tiebreakers Get an AI Upgrade: Fair Play or Computer Domination?

Rhea Morgan in Tech & Innovation December 2025 • 4 min read.

"AI-powered solutions could revolutionize chess tiebreakers, but will they enhance fairness or create a new set of challenges?"

Artificial Intelligence (AI) is rapidly transforming sports, from line calls in tennis to goal-line decisions in soccer. Now, chess is the next frontier. With the rise in drawn games, particularly in elite tournaments, the need for fair and effective tiebreakers has never been greater.

Traditionally, chess tournaments have used methods like rapid games or Armageddon scenarios to break ties. However, these methods often compromise the quality of play or introduce imbalances. The Armageddon game, for example, gives White more time but requires Black to win or draw—a format criticized for its asymmetry and potential for chaotic finishes.

Enter the AI revolution. Researchers propose an AI-driven tiebreaker that evaluates the quality of moves by comparing them against optimal moves suggested by powerful chess engines. This approach aims to provide a more objective measure of a player’s performance, ensuring fairness and maintaining the high standards of chess. Let's delve into how this innovative method works and its potential implications for the chess world.

How AI Tiebreakers Work: Pawn Loss and Engine Evaluations

The core of the AI tiebreaker lies in assessing move quality using chess engines like Stockfish 16. The method focuses on calculating what's termed "total pawn loss," a metric that reflects the difference between a player's actual move and the engine's best-recommended move at any given position.

Here’s a breakdown of the key concepts:

Pawn Loss: This is the difference between the engine's evaluation of the best possible move and the evaluation of the player's actual move. It's measured in centipawns (1/100th of a pawn).
Total Pawn Loss Value (TPLV): This is the sum of all pawn losses for a player throughout the game. A lower TPLV indicates higher-quality play, as it means the player consistently made moves closer to the engine's optimal suggestions.
Cumulative TPLV (C-TPLV): When assessing an entire tournament, the total pawn loss values across all games are added up. This provides an overall quality score for each player.

In the event of a tie, the player with the lower cumulative TPLV is declared the winner. This approach aims to reward consistent, high-quality play throughout the tournament, rather than relying on the outcome of faster, potentially less strategic tiebreaker games.

The Future of Chess Tiebreakers: A Fairer Game?

The introduction of AI into chess tiebreakers represents a significant step towards objective and fair gameplay. By focusing on the quality of moves rather than the speed of execution, this method has the potential to revolutionize how ties are resolved in chess tournaments. Whether it will truly level the playing field or introduce new strategic considerations remains to be seen. As AI continues to evolve, its role in shaping the future of chess and other sports will undoubtedly expand, sparking ongoing debates and innovations.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2210.08289,

Title: Ai-Powered Mechanisms As Judges: Breaking Ties In Chess

Subject: econ.th cs.ai

Authors: Nejat Anbarci, Mehmet S. Ismail

Published: 15-10-2022

Everything You Need To Know

How does the AI tiebreaker system determine the winner in chess, and what metrics are used?

The AI tiebreaker system determines the winner by evaluating move quality using chess engines like Stockfish 16. The primary metric used is the Total Pawn Loss Value (TPLV). TPLV measures the difference, in centipawns, between a player's move and the engine's best-recommended move at each position. The Cumulative TPLV (C-TPLV), which sums up the TPLV across all games in a tournament, is used to rank players. The player with the lower C-TPLV is declared the winner, indicating they consistently made moves closer to the engine's optimal suggestions.

What is 'Pawn Loss' in the context of the AI tiebreaker, and why is it important?

In the AI tiebreaker, 'Pawn Loss' is the difference, measured in centipawns (1/100th of a pawn), between the engine's evaluation of the best possible move and the evaluation of the player's actual move. Pawn Loss is critical because it quantifies the quality of a player's move relative to the optimal move suggested by the chess engine, such as Stockfish 16. A lower Pawn Loss indicates a move closer to the engine's recommendation, signifying better decision-making and higher-quality play.

How does the AI tiebreaker compare to traditional tiebreaking methods like Armageddon, and what are the advantages?

Traditional tiebreakers, such as rapid games or the Armageddon scenario, often compromise the quality of play or introduce imbalances. For instance, the Armageddon format gives White more time but requires Black to win or draw, which is criticized for its asymmetry. The AI tiebreaker, using the Cumulative TPLV, aims to provide a more objective measure of a player’s performance by evaluating move quality, maintaining the high standards of chess. It rewards consistent, high-quality play across an entire tournament rather than relying on the outcome of a single faster game.

Could you explain Cumulative TPLV (C-TPLV) and its role in determining the winner?

Cumulative TPLV (C-TPLV) is the sum of all Total Pawn Loss Values (TPLV) for a player across all games in a tournament. TPLV represents the difference, in centipawns, between a player's move and the engine's best-recommended move at each position. The C-TPLV provides an overall quality score for each player across the entire tournament. In the event of a tie, the player with the lower C-TPLV is declared the winner, indicating higher-quality play over the course of the tournament. This approach aims to reward consistent, high-quality play rather than relying on the outcome of faster tiebreaker games.

What are the potential implications of using AI tiebreakers for the future of chess, and what challenges might arise?

The introduction of AI tiebreakers, such as the one using Stockfish 16 and Cumulative TPLV, represents a significant step toward objective and fair gameplay. By focusing on the quality of moves rather than the speed of execution, it can revolutionize how ties are resolved in tournaments. This could lead to a greater emphasis on strategic thinking and precise move selection. However, challenges may arise, such as the potential for players to adapt their strategies to minimize pawn loss, perhaps leading to more cautious play. As AI continues to evolve, its role in shaping the future of chess and other sports will expand, sparking ongoing debates and innovations regarding fairness and strategic depth.