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Combining machine learning and human experts to predict match outcomes in football: A baseline model

Combining machine learning and human experts to predict match outcomes in football: A baseline model
Combining machine learning and human experts to predict match outcomes in football: A baseline model
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.
Beal, Ryan James
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Middleton, Stuart
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Norman, Timothy
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Ramchurn, Sarvapali
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Beal, Ryan James
d9874cb0-bd92-4a16-8576-78d769b41ff7
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3

Beal, Ryan James, Middleton, Stuart, Norman, Timothy and Ramchurn, Sarvapali (2021) Combining machine learning and human experts to predict match outcomes in football: A baseline model. The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence, , Virtual. 02 - 09 Feb 2021. 5 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

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.

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Accepted/In Press date: 1 December 2020
Published date: 2 February 2021
Venue - Dates: The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence, , Virtual, 2021-02-02 - 2021-02-09

Identifiers

Local EPrints ID: 445607
URI: http://eprints.soton.ac.uk/id/eprint/445607
PURE UUID: b92a1ec5-0330-4f90-9c7c-67910257eba6
ORCID for Stuart Middleton: ORCID iD orcid.org/0000-0001-8305-8176
ORCID for Timothy Norman: ORCID iD orcid.org/0000-0002-6387-4034
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 18 Dec 2020 17:30
Last modified: 18 Dec 2020 17:30

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Contributors

Author: Ryan James Beal
Author: Timothy Norman ORCID iD
Author: Sarvapali Ramchurn ORCID iD

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