2022-07-05T10:24:01Z

2022-07-05

Default License

Sports

masterThesis

This dissertation was written as a part of the MSc in Data Science at the International Hellenic University. This dissertation is related to the NBA League and its players; it focuses on Daily performance prediction in terms of Fantasy Points for each player and Lineup Optimization for betting purposes in Fantasy Tournaments. The primary purpose of this dissertation is to explore, develop and evaluate ML predictive models, each one focused separately on each player for Daily Player's Performance Prediction in terms of Fantasy Points. In adittion, tries to develop and evaluate a Lineup Optimizer focused on total Fantasy Points for a range of Dates. In this project tried to experiment with Pycaret library. Therefore, we develop four finalized models for each selected player. We used two primary datasets, with advanced statistics and only basic statistics. Also, the models are developed with historical data from Season 2010-11 to Season 2020-21, and in historical data from last seasons (2018-19, 2019-20, 2020-21) while in cases that the player does not participate in at least 100 games, additional season's data is included. Furthermore, in the next stage of this project, using the predictions, we developed a Lineup Optimizer with restrictions applied, focused on maximizing the sum of NBA Fantasy Points of our selected players. Results show that we can accurately predict the performance of each selected player in terms of Fantasy Points and build a well-performing Lineup for selected game dates.

Sports Analytics

2022-06-23

free

School of Science and Technology, MSc in Data Science

This dissertation was written as a part of the MSc in Data Science at the International Hellenic University. This dissertation is related to the NBA League and its players; it focuses on Daily performance prediction in terms of Fantasy Points for each player and Lineup Optimization for betting purposes in Fantasy Tournaments. The primary purpose of this dissertation is to explore, develop and evaluate ML predictive models, each one focused separately on each player for Daily Player's Performance Prediction in terms of Fantasy Points. In adittion, tries to develop and evaluate a Lineup Optimizer focused on total Fantasy Points for a range of Dates. In this project tried to experiment with Pycaret library. Therefore, we develop four finalized models for each selected player. We used two primary datasets, with advanced statistics and only basic statistics. Also, the models are developed with historical data from Season 2010-11 to Season 2020-21, and in historical data from last seasons (2018-19, 2019-20, 2020-21) while in cases that the player does not participate in at least 100 games, additional season's data is included. Furthermore, in the next stage of this project, using the predictions, we developed a Lineup Optimizer with restrictions applied, focused on maximizing the sum of NBA Fantasy Points of our selected players. Results show that we can accurately predict the performance of each selected player in terms of Fantasy Points and build a well-performing Lineup for selected game dates.

Bozanis, Panayiotis

IHU