Browsing by Author "Alves, João Miguel Lima"
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- Modelo de aprendizagem automática para prever jogos de basquetebolPublication . Alves, João Miguel Lima; Barbosa, Ramiro de SousaArtificial intelligence has emerged as an essential tool in many areas. Sport, due to its widespread appeal, has also embraced this trend and recognized its significance in driving its development. The processing and analytical capacity developed in recent years in artificial intelligence offers a new dimension for understanding and predicting complex events. Predictive analysis, driven by machine learning algorithms, makes it possible to identify patterns and trends that would not be evident using traditional methods. In the field of sports, machine learning is significantly altering the analysis of athletes’ and teams’ performance. In the realm of basketball, these techniques afford comprehensive insights into both individual and collective development, facilitating a more precise evaluation of the strategies and tactics employed. The meticulous analysis of every facet of a team’s daily routine has the potential to elevate the sport to a heightened level of competition. This thesis investigates the use of machine learning to predict the outcome of National Basketball Association and Women’s National Basketball Association games, using historical data and collective performance metrics. Through the utilization of advanced algorithms, this application seeks to analyze patterns that are crucial for detecting future results. The objective is to demonstrate the capability of these technologies to predict outcomes that may not be attainable solely through human analysis. The research findings underscore the potential of machine learning to surpass traditional statistical methods in predicting sports outcomes. hrough the integration of comprehensive data and advanced modeling techniques, it is possible to demonstrate the capacity to generate more accurate and pertinent predictions. This approach not only enriches sports analysis but also holds considerable practical value, supporting strategic decision-making in the realm of basketball.