Implementation of business analytics tools in a padel club : Quinta de Monserrate

• Autor(a) principal: Moreira, Bernardo Nuno Alves de Vaz
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.14/36856
• topic: Business analytics; Sport; Cluster analysis; Quinta de Monserrate; Análise de cluster; Desporto; Padel; Domínio/Área Científica::Ciências Sociais::Economia e Gestão

The purpose of this Master’s Final Assessment (MFA) is to describe the implementation of Business Intelligence and Analytics (BI&A) in the sports industry. In a nut shell, BI&A is a technology-driven process focused on data management solutions, which aims to collect and analyse information to improve the decision-making. Although many studies were conducted across the sports industry, there is still alack of investigation on padel. This sport is rising across the globe and is well-known for its strong social component and easiness to play. For instance, since 2011, the number of padel courts in Portugal rose as much as 700%. In Portugal, and similar to other southern European countries, the industry is competitive, and clubs are growing exponentially without any concrete business plans, raising the question, “would they improve any competitive advantage and value added if implementing BI&A?” This project aims to develop a successful application of analytic tools to improve the decision-making of Quinta de Monserrate (QM), one of the padel clubs pioneers in Portugal, across all their value chain. From an operational perspective, padel classes are intended to deliver a more complex view of the athletes base don structure data. It was intended to improve the learning process of each individual by reaching new insights about the presences and characteristics of the game. For this purpose, two different systems were developed, i.e. one regarding the attendance in classes and the other the evaluation of the athletes. Both retrieve structure data that was then used to create visualization tools and compute deep analysis. This analysis is expected to find sub groups with in the training’s groups already assemble, according to new insights obtained with the data collected. As a result of this MFA, the coaches have two reliable systems to control attendance and the evaluation of the athletes that impact the decision-making process and the time wasted in the organization of the systems.