<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

A Critical Comparison of Machine Learning Classifiers to Predict Match Outcomes in the NFL

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this paper, we critically evaluate the performance of nine machine learning classification techniques when applied to the match outcome prediction problem...

A Critical Comparison of Machine Learning Classifiers to Predict Match Outcomes in the NFL

Online veröffentlicht: 31. Dez. 2020

Seitenbereich: 36 - 50

DOI: https://doi.org/10.2478/ijcss-2020-0009

Schlüsselwörter
MACHINE LEARNING, SUPERVISED LEARNING, FOOTBAL, NFL

© 2020 Ryan Beal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Critical Comparison of Machine Learning Classifiers to Predict Match Outcomes in the NFL

Online veröffentlicht: 31. Dez. 2020

Seitenbereich: 36 - 50

DOI: https://doi.org/10.2478/ijcss-2020-0009

SchlüsselwörterMACHINE LEARNING, SUPERVISED LEARNING, FOOTBAL, NFL

© 2020 Ryan Beal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
MACHINE LEARNING, SUPERVISED LEARNING, FOOTBAL, NFL