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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 Na\u00efve 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.<\/jats:p>","DOI":"10.2478\/ijcss-2020-0009","type":"journal-article","created":{"date-parts":[[2021,1,24]],"date-time":"2021-01-24T06:42:04Z","timestamp":1611470524000},"page":"36-50","source":"Crossref","is-referenced-by-count":11,"title":["A Critical Comparison of Machine Learning Classifiers to Predict Match Outcomes in the NFL"],"prefix":"10.2478","volume":"19","author":[{"given":"Ryan","family":"Beal","sequence":"first","affiliation":[{"name":"University of Southampton , University Rd, Southampton, SO17 1BJ"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy J.","family":"Norman","sequence":"additional","affiliation":[{"name":"University of Southampton , University Rd, Southampton, SO17 1BJ"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarvapali D.","family":"Ramchurn","sequence":"additional","affiliation":[{"name":"University of Southampton , University Rd, Southampton, SO17 1BJ"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2020,12,31]]},"reference":[{"key":"2026042808574224460_j_ijcss-2020-0009_ref_001_w2aab3b7b5b1b6b1ab1ab1Aa","doi-asserted-by":"crossref","unstructured":"Beal, R., Norman, T., & Ramchurn, S. 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