Intelligent recommendation system to enhance youth football athletes development

• Autor(a) principal: Fraga, Pedro Filipe Monteiro
• Data de Publicação: 2024
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: http://hdl.handle.net/10400.22/26753
• topic: Football recommendation system; Weighted hybrid filtering; K-means clustering; Weighted score calculation; Ranked recommendation

With the constant evolution of sports, technological advancements are constantly being made with the objective of maximizing the performance of teams and players. Looking specifically at football, which is the most played sport across the world, teams are constantly looking to develop the best players, and that process starts when they are young. However, only the top football clubs in the world have the funding necessary to create the best conditions to improve the best players. Consequently, for most of the football academies, it is hard to maximize the potential of their young players and enhance their growth. Therefore, this thesis pretends to solve this issue by proposing a football recommendation system for young athletes improvement, where the main objectives were to help the coaching staff by recommending the most relevant skills for a player to improve, using different approaches to achieve these recommendations, such as the input of experts, test them in a real life football academy environment, with the aid of recommendation system evaluation metrics, and discuss the results obtained. In the State of the Art chapter, a systematic literature review with PRISMA methodology was used, to assess the existing recommendation systems in football and their algorithms, as well as the aspects typically included in player modeling. The system was tested in a U14 and a U17 men’s football team, and the results obtained are very good indicators of the effectiveness of the system, showcasing 93.1% of accuracy in the recommendations while maintaining recall, precision and F1-measure values above 80%. The results obtained, combined with an interview with a coach that tested the system, show evidence that the system enhances the growth of the players, by aiding the coaching staff at suggesting the most relevant skills to a player. It also indicates that the system can be implemented in football academies, to complement the coaching staff. There are some limitations to the system, notably not having an interface built, the goalkeeper position not being addressed by the system and the experimentation tests having a relatively small sample size. Future work includes improving the performance of the algorithms, testing on a bigger set of players, adding the goalkeeper position, reanalyzing the skills used to modulate a player and the addition of an interface, which will aid the utilization of the system and enable the customization of the parameters used in the models.