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A critical comparison of machine learning classifiers to predict match outcomes in the NFL

A critical comparison of machine learning classifiers to predict match outcomes in the NFL
A critical comparison of machine learning classifiers to predict match outcomes in the NFL
In this paper, we critically evaluate the performance of nine machine learning classification techniques when applied to the match outcome prediction problem presented by American Football. Specifically, we implement and test nine techniques using real-world datasets of 1280 games over 5 seasons from the National Football League (NFL). We test the nine different classifier techniques using a total of 42 features for each team and we find that the best performing algorithms are able to improve one previous published works. The algoriothms achieve an accuracy of between 44.64% for a Guassian Process classifier to 67.53% with a Naïve Bayes classifer. We also test each classifier on a year by year basis and compare our results to those of the bookmakers and other leading academic papers.
1684-4769
Beal, Ryan James
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Norman, Timothy
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Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Beal, Ryan James
d9874cb0-bd92-4a16-8576-78d769b41ff7
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3

Beal, Ryan James, Norman, Timothy and Ramchurn, Sarvapali (2020) A critical comparison of machine learning classifiers to predict match outcomes in the NFL. International Journal of Computer Science in Sport, 19 (2). (doi:10.2478/ijcss-2020-0009 |).

Record type: Article

Abstract

In this paper, we critically evaluate the performance of nine machine learning classification techniques when applied to the match outcome prediction problem presented by American Football. Specifically, we implement and test nine techniques using real-world datasets of 1280 games over 5 seasons from the National Football League (NFL). We test the nine different classifier techniques using a total of 42 features for each team and we find that the best performing algorithms are able to improve one previous published works. The algoriothms achieve an accuracy of between 44.64% for a Guassian Process classifier to 67.53% with a Naïve Bayes classifer. We also test each classifier on a year by year basis and compare our results to those of the bookmakers and other leading academic papers.

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Accepted/In Press date: 1 November 2020
Published date: 30 December 2020

Identifiers

Local EPrints ID: 446078
URI: http://eprints.soton.ac.uk/id/eprint/446078
ISSN: 1684-4769
PURE UUID: f5de6f18-87b5-4ee0-8d26-e02572a0ea4e
ORCID for Timothy Norman: ORCID iD orcid.org/0000-0002-6387-4034
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

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Date deposited: 20 Jan 2021 17:30
Last modified: 18 Feb 2021 17:26

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Contributors

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

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