Publicação
Towards a pervasive intelligent system on football scouting - a data mining study case
| Resumo: | Football, which is a popular world-wide sport, has become one of the most practiced sports but also, with more study cases. Scouting and game analysis that is currently made has offered the possibility to improve the competition and increase the performance levels within a team. Taking this into account it emerged the term Scouting. The objective of this study is to streamline the Scouting process in Football, through Data Mining (DM) techniques and following the Cross Industry Standard Process for Data Mining (CRIPS-DM) methodology. The goal of DM was to develop and evaluate predictive models capable of forecasting a score of a football player’s performance. Based on this target, 2808 classification models and 936 regression models were developed and evaluated. For the classification, the maximum accuracy percentage was centered at 94% for the Forward player position, while for the regression the minimum error value was 0.07 for the Forward position. The results obtained allow to streamline the Scouting process in Football thus enhancing the sporting advantage. |
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| Autores principais: | Vilela, Tiago |
| Outros Autores: | Portela, Filipe; Santos, Manuel |
| Assunto: | Data mining Football Knowledge discovery in databases Scouting |
| Ano: | 2018 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Football, which is a popular world-wide sport, has become one of the most practiced sports but also, with more study cases. Scouting and game analysis that is currently made has offered the possibility to improve the competition and increase the performance levels within a team. Taking this into account it emerged the term Scouting. The objective of this study is to streamline the Scouting process in Football, through Data Mining (DM) techniques and following the Cross Industry Standard Process for Data Mining (CRIPS-DM) methodology. The goal of DM was to develop and evaluate predictive models capable of forecasting a score of a football player’s performance. Based on this target, 2808 classification models and 936 regression models were developed and evaluated. For the classification, the maximum accuracy percentage was centered at 94% for the Forward player position, while for the regression the minimum error value was 0.07 for the Forward position. The results obtained allow to streamline the Scouting process in Football thus enhancing the sporting advantage. |
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