# Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model

arXiv: https://arxiv.org/abs/2012.04380 · HTML: https://mlsanity.com/paper/2012.04380/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2410.08474/) (2024)
- [SportQA: A Benchmark for Sports Understanding in Large Language Models](https://mlsanity.com/paper/2402.15862/) (2024)
- [Let's Play Across Cultures: A Large Multilingual, Multicultural Benchmark for Assessing Language Models' Understanding of Sports](https://mlsanity.com/paper/2510.01247/) (2025)
- [Machine Learning for Soccer Match Result Prediction](https://mlsanity.com/paper/2403.07669/) (2024)
- [WorldCupArena: Fine-Grained Evaluation of Language Models and Deep-Research Agents on Football Forecasting](https://mlsanity.com/paper/2607.18084/) (2026)
- [PGD: A Large-scale Professional Go Dataset for Data-driven Analytics](https://mlsanity.com/paper/2205.00254/) (2022)
- [Predicting Football Match Outcomes with eXplainable Machine Learning and the Kelly Index](https://mlsanity.com/paper/2211.15734/) (2022)
- [The ProfessionAl Go annotation datasEt (PAGE)](https://mlsanity.com/paper/2211.01559/) (2022)

## Abstract (arXiv)

In this paper, we present a new application-focused benchmark dataset and results from a set of baseline Natural Language Processing and Machine Learning models for prediction of match outcomes for games of football (soccer). By doing so we give a baseline for the prediction accuracy that can be achieved exploiting both statistical match data and contextual articles from human sports journalists. Our dataset is focuses on a representative time-period over 6 seasons of the English Premier League, and includes newspaper match previews from The Guardian. The models presented in this paper achieve an accuracy of 63.18% showing a 6.9% boost on the traditional statistical methods.