Sports analytics utilizes statistical inference and machine learning techniques to analyze data collected from sporting events. By examining multi-sensor data, such as the ones generated during soccer matches, analysts can gain insights into player movements, team tactics, spatial dynamics, and overall game performance. Potential applications include predicting player movements using time series analysis based on historical actions.
Hivatkozások
- Trevor Hastie, Robert Tibshirani, Jerome Friedman: "The Elements of Statistical Learning: Data Mining, Inference, and Prediction", Springer, 2009.
- Javier Fernández, Luke Born: "Wide Open Spaces: A Statistical Technique for Measuring Space Creation in Professional Soccer", MIT Sloan Sports Analytics Conference, 2018.
- Luca Pappalardo, Paolo Cintia, Alessio Rossi, Emanuele Massucco, Paolo Ferragina, Dino Pedreschi, and Fosca Giannotti: "A Public Data Set of Spatio-Temporal Match Events in Soccer Competitions", Scientific Data, Nature Research, Vol. 6, 2019.