Introduction
Sports Analytics has been a steadily growing area in the last decade, especially in the context of US professional sports leagues but also in connection with European football leagues. The recent implementation of strict financial fair-play regulations in European football will definitely render sports analytics even more important in the coming years. In addition, there is of course the always popular sports betting. Developed approaches have been used for decision support in all aspects of professional sports:
- Player acquisition and team spending
- Training regimens and focus
- Match strategy
- Injury prediction and prevention
- Predicting match outcomes
- Betting odds calculation
- Text analysis of match reports
- Descriptive modeling
The majority of techniques used in the field so far are statistical and while there has been some interest in the Machine Learning and Data Mining community, it has been somewhat muted so far. We intend to change this by hosting a workshop on Sports Analytics at ECML/PKDD 2013. Not only do we believe that the setting is interesting and challenging, and can potentially be a source of new data, but also that this offers a great opportunity to bring people from outside of the ML community into contact with typical ECML/PKDD contributors, and to highlight what the community has done and can do in the field.
To facilitate this, we have assembled a diverse program committee that includes statisticians as well as practitioners in sports-related matters in addition to Machine Learning and Data Mining folk.