In recent years, statistical analysis has revolution-ized decision-making in the NBA and the growth of Fantasy Basketball platforms like DraftKings and FanDuel. This paper presents a novel approach to draft a fantasy team, utilizing a machine learning optimization model powered by Gurobi solver. Our model efficiently constructs optimal fantasy teams, navigating constraints such as salary caps and player positions. By leveraging player statistics and injury data through metic- ulous data processing and advanced optimization techniques, our methodology maximizes fantasy points while ensuring active player participation. Our study showcases the effectiveness of integrating machine learning optimization models in fantasy basketball team selection, offering valuable insights and practical solutions for enthusiasts seeking a technological competitive edge in fantasy sports.
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Veluru et al. (2024) studied this question.
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