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Elo rating, widely used for skill assessment across diverse domains ranging from competitive games to large language models, is often understood as an incremental update algorithm for estimating a stationary Bradley-Terry (BT) model.…

机器学习 · 计算机科学 2025-02-18 Shange Tang , Yuanhao Wang , Chi Jin

The Elo rating system is widely adopted to evaluate the skills of (chess) game and sports players. Recently it has been also integrated into machine learning algorithms in evaluating the performance of computerised AI agents. However, an…

机器学习 · 计算机科学 2022-01-21 Xue Yan , Yali Du , Binxin Ru , Jun Wang , Haifeng Zhang , Xu Chen

The Elo rating system is a highly successful ranking algorithm for games of skill where, by construction, one team wins and the other loses. A primary limitation of the original Elo algorithm is its inability to predict information beyond a…

统计方法学 · 统计学 2018-02-05 J. Scott Moreland , Matthew C. Superdock

The Elo rating system is a popular and widely adopted method for measuring the relative skill levels of players or teams in various sports and competitions. It assigns players numerical ratings and dynamically updates them based on game…

概率论 · 数学 2026-01-28 Roberto Cortez , Hagop Tossounian

This study aims to provide a data-driven approach for empirically tuning and validating rating systems, focusing on the Elo system. Well-known rating frameworks, such as Elo, Glicko, TrueSkill systems, rely on parameters that are usually…

应用统计 · 统计学 2025-12-23 Shirsa Maitra , Tathagata Banerjee , Anushka De , Diganta Mukherjee , Tridib Mukherjee

This work is concerned with the rating of players/teams in face-to-face games with three possible outcomes: loss, win, and draw. This is one of the fundamental problems in sport analytics, where the very simple and popular, non-trivial…

统计理论 · 数学 2019-10-15 Leszek Szczecinski , Aymen Djebbi

In Natural Language Processing (NLP), the Elo rating system, originally designed for ranking players in dynamic games such as chess, is increasingly being used to evaluate Large Language Models (LLMs) through "A vs B" paired comparisons.…

计算与语言 · 计算机科学 2023-11-30 Meriem Boubdir , Edward Kim , Beyza Ermis , Sara Hooker , Marzieh Fadaee

Rating systems play a crucial role in evaluating player skill across competitive environments. The Elo rating system, originally designed for deterministic and information-complete games such as chess, has been widely adopted and modified…

计算机科学与博弈论 · 计算机科学 2025-12-23 Avirup Chakraborty , Shirsa Maitra , Tathagata Banerjee , Diganta Mukherjee , Tridib Mukherjee

The Elo algorithm, renowned for its simplicity, is widely used for rating in sports tournaments and other applications. However, despite its widespread use, a detailed understanding of the convergence characteristics of the Elo algorithm is…

机器学习 · 计算机科学 2023-11-28 Daniel Gomes de Pinho Zanco , Leszek Szczecinski , Eduardo Vinicius Kuhn , Rui Seara

The Elo rating system, which was originally proposed by Arpad Elo for chess, has become one of the most important rating systems in sports, economics and gaming nowadays. Its original formulation is based on two-player zero-sum games, but…

最优化与控制 · 数学 2022-04-12 Düring Bertram , Fischer Michael , Wolfram Marie-Therese

Rating systems play an important role in competitive sports and games. They provide a measure of player skill, which incentivizes competitive performances and enables balanced match-ups. In this paper, we present a novel Bayesian rating…

信息检索 · 计算机科学 2021-01-05 Aram Ebtekar , Paul Liu

ELO rating system is proposed by Arpad Elo, a Hungarian-American physics professor. Originally, it was proposed for the ranking system of chess players, but it was soon adapted to many other zero-sum sports fields like football, baseball,…

人机交互 · 计算机科学 2023-10-24 Yuhan Song

This article discusses in detail the rating system that won the kaggle competition "Chess Ratings: Elo vs the rest of the world". The competition provided a historical dataset of outcomes for chess games, and aimed to discover whether novel…

机器学习 · 计算机科学 2015-03-17 Yannis Sismanis

Prediction and modelling of competitive sports outcomes has received much recent attention, especially from the Bayesian statistics and machine learning communities. In the real world setting of outcome prediction, the seminal \'{E}l\H{o}…

机器学习 · 统计学 2017-01-30 Franz J. Király , Zhaozhi Qian

One of the main goals of online competitive games is increasing player engagement by ensuring fair matches. These games use rating systems for creating balanced match-ups. Rating systems leverage statistical estimation to rate players'…

人工智能 · 计算机科学 2021-06-23 Arman Dehpanah , Muheeb Faizan Ghori , Jonathan Gemmell , Bamshad Mobasher

Assessing the skill level of players to predict the outcome and to rank the players in a longer series of games is of critical importance for tournament play. Besides weaknesses, like an observed continuous inflation, through a steadily…

人工智能 · 计算机科学 2021-04-13 Stefan Edelkamp

In competitive games, strength ratings like Elo are widely used to quantify player skill and support matchmaking by accounting for skill disparities better than simple win rate statistics. However, scalar ratings cannot handle complex…

机器学习 · 计算机科学 2025-02-07 Chiu-Chou Lin , I-Chen Wu

Elo rating systems measure the approximate skill of each competitor in a game or sport. A competitor's rating increases when they win and decreases when they lose. Increasing one's rating can be difficult work; one must hone their skills…

组合数学 · 数学 2024-04-16 Rikhav Shah

We introduce a novel system of matching and scoring players in tournaments, called Multi-Tier Tournaments, illustrated by chess and based on the following rules: 1. Players are divided into skill-based tiers, based on their Elo ratings. 2.…

理论经济学 · 经济学 2024-07-22 Steven J. Brams , Mehmet S. Ismail

The Elo system for rating chess players, also used in other games and sports, was adopted by the World Chess Federation over four decades ago. Although not without controversy, it is accepted as generally reliable and provides a method for…

物理与社会 · 物理学 2011-03-31 Trevor Fenner , Mark Levene , George Loizou
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