Sports Outcome Prediction

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Overview

Sports outcome prediction estimates who will win, by how much, or how an athlete will perform, supporting scouting, strategy, betting, and competition analysis. Common approaches combine rankings such as Elo, historical and live match statistics, bookmaker odds, and machine-learning models; newer work adds graph features, sensor and video data, and language-model agents that search information or simulate match states. Evaluation spans pre-match and in-game forecasts, exact scores, tournament brackets, and performance or injury-related outcomes, using metrics such as accuracy, Brier score, and log loss against market and simple baselines. A central challenge is that gains over betting markets are often small, and results depend on calibration, changing information, leakage controls, and benchmark design; richer context does not always improve forecasts.

Latest papers

CardsList

19 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

  1. JockeyBench: Evaluating LLM Agents on Horse-Racing Decision-Making

    Oct 14, 2026Zhongwei Xie, Xingsu Li, Jingqiao Yu +8LLM Agent EvaluationForecasting Benchmarks

    The Hong Kong University of Science and Technology · Tsy Capital Limited · Independent researcher +1

  2. Throwing4: Phase-Aware Pose Modeling for Throw-Distance Prediction in Elite Athletics

    Oct 12, 2026Bikash Kumar Badatya, Kartike Tiwari, Jyotirmoy Amin +1Sports Outcome PredictionSports Analytics

    Department of Electrical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, India. · Department of Electronics and Communication Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India. · Department of Mathematical Sciences, IISER Berhampur, Berhampur, India.

  3. Information Specialization and Constrained Synthesis in Multi-Agent LLM Forecasting: A Prospective Live-Study of the 2026 FIFA World Cup

    Sep 11, 2026Julian Varghese, Lucas Bickmann, Sarah SandmannProbabilistic ForecastingMulti-Agent Collaboration

    Institute of Medical Informatics, Otto-von-Guericke University Magdeburg, Germany.

  4. Forecasting the Winner of a Live Tennis Match

    Sep 7, 2026Charles Xie, Aneesh MuppidiProbabilistic ForecastingSports Outcome Prediction

    Natick High School 15 West Street, Natick, Massachusetts 01760, USA · University of Oxford Oxford, United Kingdom

  5. A Statistical and Machine Learning Framework for Quantifying Offensive Impact in Professional Box Lacrosse

    Sep 6, 2026Robert JimersonSports Outcome Prediction

    Independent Researcher

  6. Multimodal Injury Risk and Performance Prediction in Tennis Using Weighted Ensemble Learning

    Aug 21, 2026Weihao Qu, Dongyang Wang, Ling Zheng +3Multimodal Sensor FusionSports Outcome Prediction

    Department of Computer Science and Software Engineering Monmouth University, West Long Branch, NJ 07764, USA

  7. From Score Matrices to Football-Aware Match-State Simulation: An Auditable LLM Harness for Exact-Score Reranking

    Aug 5, 2026Shaopeng LiangProbabilistic ForecastingLLM Reranking

    Independent Researcher

  8. WorldCup Arena: Prospective, Leakage-Free Evaluation of Frontier LLMs on a Live Tournament

    Aug 4, 2026Zhenran Wang, Zhonghan Bian, Jinsong Li +1LLM EvaluationForecasting Benchmarks

  9. AI World Cup 2026: Benchmarking Large Language Models for End-to-End Football Tournament Prediction

    Aug 4, 2026Jonaid Shianifar, Iias FaiudLLM EvaluationForecasting Benchmarks

    AI World Cup Project

  10. LLM-SoccerArena: Benchmarking LLMs on Real-World Predictions in Sports

    Jul 27, 2026Jonas Schröder, Jonas Schweisthal, Oliver Müller +2LLM EvaluationForecasting Benchmarks

    MCML & LMU Munich Munich, Germany · Paderborn University Paderborn, Germany · University of Cologne Cologne, Germany

  11. Topological Data Analysis and Graph-Theoretic Approaches for Tennis Match Prediction

    Jul 26, 2026Jake Schwaderer, Alexander Bastien, Omid Khormali +3Sports AnalyticsSports Outcome Prediction

    Department of Mathematics University of Evansville Evansville, Indiana 47722, USA.

  12. WorldCupArena: Fine-Grained Evaluation of Language Models and Deep-Research Agents on Football Forecasting

    Jul 20, 2026Zhaokai Wang, Tianlin Gui, Jiayuan Rao +3LLM EvaluationForecasting Benchmarks

    Shanghai Jiao Tong University · Shanghai, China · Nanjing University +4

  13. FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents: Four Models, a Bookmaker, and 104 Matches

    Jul 20, 2026Jiacheng Ding, Cong Guo, Jason XuLLM Agent EvaluationForecasting Benchmarks

    University of Memphis Memphis, Tennessee, USA · QuantaInsight Seattle, Washington, USA

  14. Verifiable Rewards for Calibrated Probabilistic Forecasting

    Jun 30, 2026Sadanand Singh, Allam Reddy, Manan ChopraProbability CalibrationRL for Language Model Reasoning

    Cascade Research

  15. Forecast Sports Outcomes under Efficient Market Hypothesis: Theoretical and Experimental Analysis of Odds-Only and Generalised Linear Models

    Apr 19, 2026Kaito Goto, Naoya Takeishi, Takehisa YairiProbabilistic ForecastingGeneralized Linear Models

    Research Center for Advanced Science and Technology, The University of Tokyo, Japan

  16. New insights into Elo algorithm for practitioners and statisticians

    Apr 4, 2026Leszek SzczecinskiMaximum Marginal Likelihood EstimationSports Outcome Prediction

  17. Modeling Matches as Language: A Generative Transformer Approach for Counterfactual Player Valuation in Football

    Mar 16, 2026Miru Hong, Minho Lee, Geonhee Jo +4Autoregressive TransformerCounterfactual Prediction

    University of Seoul, Seoul, Republic of Korea · Saarland University, Saarbrücken, Germany · KAIST, Daejeon, Republic of Korea +2

  18. NCAA Bracket Prediction Using Machine Learning and Combinatorial Fusion Analysis

    Mar 11, 2026Yuanhong Wu, Isaiah Smith, Tushar Marwah +3Sports Outcome PredictionSports Analytics

    Department of Computer and Information Science, Fordham University, New York, NY, 10023, USA · Gabelli School of Busines, Fordham University, New York, NY, 10023, USA · Department of Computer and Information Science, Fordham Universiyt, New York, NY, 10023, USA

  19. What If They Took the Shot? A Hierarchical Bayesian Framework for Counterfactual Expected Goals

    Nov 28, 2025Mikayil Mahmudlu, Oktay Karakuş, Hasan Arkadaş +1Counterfactual PredictionSports Outcome Prediction

    Cardiff University, School of Mathematics, Abacws, Senghennydd Road, Cardiff, CF24 4AG, UK. · Cardiff University, School of Computer Science and Informatics, Abacws, Senghennydd Road, Cardiff, CF24 4AG, UK. · Dead Ball Analytics Limited, Barry, CF63 2QQ, UK.