Sports Analytics with Statistical Learning
| Témavezető: | Csáji Balázs Csanád |
| SZTAKI és ELTE TTK Valószínűségelméleti és Statisztika Tanszék | |
| email: | csaji@sztaki.hu |
Projekt leírás
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.
Előfeltételek
- Strong proficiency in English: ability to read, understand, and communicate effectively in scientific contexts.
- Solid foundation in statistics: a deep understanding of statistical concepts, methods, and applications.
- Expertise in Python programming: proficiency in using Python for data analysis, machine learning, and statistical modeling.
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.