Machine Learning in Fantasy Premier League

Βαλουξής, Σπυρίδων; Valouxis, Spiros

dc.contributor.author	Βαλουξής, Σπυρίδων	el
dc.contributor.author	Valouxis, Spiros	en
dc.date.accessioned	2023-09-06T09:22:43Z
dc.date.available	2023-09-06T09:22:43Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/58026
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.25723
dc.rights	Default License
dc.subject	Μηχανική Μάθηση	el
dc.subject	Aναμενόμενη Αξία	el
dc.subject	Βελτιστοποίηση	el
dc.subject	Επιστήμη Δεδομένων	el
dc.subject	Παιχνίδια Φάντασι	el
dc.subject	Fantasy Premier League	en
dc.subject	Machine Learning	en
dc.subject	Optimization	en
dc.subject	Expected Value	en
dc.subject	Data Science	en
dc.title	Machine Learning in Fantasy Premier League	en
dc.contributor.department	Division of Computer Science - Artificial Intelligence and Learning Systems Laboratory	el
heal.type	bachelorThesis
heal.classification	Data Science	en
heal.classification	Machine Learning	en
heal.classification	Optimization	en
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2023-06-21
heal.abstract	The world of fantasy sports has witnessed a surge in popularity, with Fantasy Premier League (FPL) standing out as a leader in the field. This diploma thesis focuses on the application of machine learning techniques in Fantasy Premier League (FPL) to enhance team performance and decision-making. The research objectives were threefold: to develop machine learning models for predicting Expected Value (EV), to generate optimal moves based on the predicted EV values, and to contribute to the field of FPL analytics by advancing the application of machine learning techniques. The growing popularity of FPL (over 11.4 million teams in the 2022/23 season) has increased the demand for data-driven strategies across the community to gain a competitive edge. While previous efforts by the FPL community have explored similar concepts, our study presents the first open-source, full-scale FPL model utilizing machine learning techniques. The results of our research demonstrate the superiority of our model compared to most of the other existing models. Through rigorous evaluation using various metrics (MAE, RMSE, R² score), our models consistently outperformed almost all competitors in predicting EV. Furthermore, a noteworthy achievement was the top-ranking performance of the team managed by our model in the GW24 league, surpassing over 25,000 competing teams by the end of the season. By successfully integrating machine learning and optimization algorithms, our project provides FPL managers with actionable recommendations for team selection, transfers, and strategic decision-making. This research significantly contributes to the field of FPL Analytics by offering an innovative solution that enhances team performance and provides valuable insights for managers seeking to improve their FPL results.	en
heal.advisorName	Κόλλιας, Στέφανος	el
heal.committeeMemberName	Κόλλιας, Στέφανος	el
heal.committeeMemberName	Στάμου, Γιώργος	el
heal.committeeMemberName	Βουλόδημος, Αθανάσιος	el
heal.committeeMemberName	Τζούβελη, Παρασκευή	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	119 σ.	el
heal.fullTextAvailability	false