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Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. We test our solutions using real-world data-sets from across four seasons (2014-2017). We highlight the advantage that can be gained from using our machine-based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game-weeks over a season.<\/jats:p>","DOI":"10.2478\/ijcss-2020-0008","type":"journal-article","created":{"date-parts":[[2021,1,24]],"date-time":"2021-01-24T06:42:03Z","timestamp":1611470523000},"page":"21-35","source":"Crossref","is-referenced-by-count":11,"title":["Optimising Daily Fantasy Sports Teams with Artificial Intelligence"],"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":"2026042806550789551_j_ijcss-2020-0008_ref_001_w2aab3b7b3b1b6b1ab1ab1Aa","doi-asserted-by":"crossref","unstructured":"Beal, R., Norman, T. 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