Batikas, MichailBerardo, Maria Inês Alves da Graça2024-11-282024-11-282024-01-102024-01-10http://hdl.handle.net/10362/175950This research assesses the complexity of the rare phenomenon of the hot hand in the NBA, analysing an extensive dataset and employing analytical analysis. Perceptions versus statistical validity are examined, starting by defining heat and assessing its impact on gameplay. Leveraging NBA shot records, the study aims to identify patterns and ultimately build a predictive model tailored to dataset characteristics. Through meticulous analysis and model development, this research contributes to understanding hot hand occurrences and their potential implications for data-driven game strategies and performance optimization. Aligning with previous studies on rare events, predicting hot shot occurrences underscores the inherent challenge in forecasting such phenomenon.engPredictionAlgorithmSports analyticsMachine learningBasketballNbaHot handmaster thesis203679903