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AI-powered mechanisms as judges: Breaking ties in chess

Nejat Anbarci, Mehmet S. Ismail

TL;DR

The paper tackles the persistent draw problem in elite chess by introducing an objective AI-based tiebreaker that measures move quality against engine-optimal moves. It defines Total Pawn Loss ($TPLV$) and its tournament-level aggregation ($C{-}TPLV$) to rank players in tied conditions, using Stockfish 16 to analyze roughly 25,000 moves from World Championship data (1910–2018). Empirically, the approach is shown on 286 games across nine matches, with historical tie rates rising and a depth-related anomaly addressed by deeper analysis; in practice, the method can convert ties into decisive outcomes using a 3-2-1 like scoring tied to $TPLV$ differences. The proposed framework offers a fair, transparent, and potentially sport-enhancing alternative to existing rapid/blitz Armageddon tiebreaks, while acknowledging limitations related to engine choice, depth, and strategic incentives, and suggesting pathways for broader application to other zero-sum games.

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.

AI-powered mechanisms as judges: Breaking ties in chess

TL;DR

The paper tackles the persistent draw problem in elite chess by introducing an objective AI-based tiebreaker that measures move quality against engine-optimal moves. It defines Total Pawn Loss () and its tournament-level aggregation () to rank players in tied conditions, using Stockfish 16 to analyze roughly 25,000 moves from World Championship data (1910–2018). Empirically, the approach is shown on 286 games across nine matches, with historical tie rates rising and a depth-related anomaly addressed by deeper analysis; in practice, the method can convert ties into decisive outcomes using a 3-2-1 like scoring tied to differences. The proposed framework offers a fair, transparent, and potentially sport-enhancing alternative to existing rapid/blitz Armageddon tiebreaks, while acknowledging limitations related to engine choice, depth, and strategic incentives, and suggesting pathways for broader application to other zero-sum games.

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.
Paper Structure (18 sections, 8 equations, 6 figures, 5 tables)

This paper contains 18 sections, 8 equations, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. The empirical summary and the computation method
  3. An AI-based tiebreaking mechanism
  4. US Women's Championship
  5. World Chess Championship 2018: Carlsen vs. Caruana
  6. The definition, data collection, and computation
  7. Definition
  8. Further practicable tiebreaking rules
  9. Data collection and computation
  10. Limitations
  11. Sensitivity to the software (AI system) and the hardware
  12. Sensitivity to the aggregation of TPLVs and the threshold selection
  13. Different playing styles
  14. Computer-like play
  15. Playing strength
  16. ...and 3 more sections

Figures (6)

  • Figure 1: Percentage of draws in World Chess Championship matches 1886--2021.
  • Figure 2: TPLVs of Irina Krush and Jennifer Yu in the 2022 US Women Chess Championship games. Lower TPLV implies better play.
  • Figure 3: TPLVs per round/game in World Chess Championship matches
  • Figure 4: Outcomes of world championship matches with 14 or fewer classical games: a hollow dot represents a draw, a solid dot represents a win for the first player, and a cross represents a loss for the first player
  • Figure 5: Outcomes of world championship matches with 24 classical games: a hollow dot represents a draw, a solid dot represents a win for the first player, and a cross represents a loss for the first player
  • ...and 1 more figures

Theorems & Definitions (2)

  • Definition 1: Pawn loss
  • Definition 2: Total pawn loss value