MLSA 2015 - Machine Learning and Data Mining for Sports Analytics (MLSA 15)

Sports Analytics has been a steadily growing and rapidly evolving area over 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. The developed approaches are being used for decision support in all aspects of professional sports:
Match strategy, tactics, and analysis
Player acquisition, player valuation, and team spending
Training regimens and focus
Injury prediction and prevention
Performance management and prediction
Match outcome prediction
Tournament design and scheduling
Betting odds calculation
Traditionally, the definition of sports has also included certain non-physical activities, such as chess ? in other words, games. Especially in the last decade, so-called e-sports, based on a number of computer games, have become very relevant commercially. Professional teams have been formed for games such as Starcraft 2, Defense of the Ancients (DOTA) 2, or League of Legends, and tournaments offer large amounts of prize money and are important broadcast events. Given that topics such as strategy analysis and match forecasting apply in equal measure to these new sports (and other topics might apply as well but are not very well explored so far), and data collection is in fact somewhat easier than for off-line sports. We have therefore chosen to broaden the scope of the workshop this year and solicit e-sports submissions as well.
The majority of techniques used in the field so far are statistical. While there has been some interest in the Machine Learning and Data Mining community, it has been somewhat muted so far. Building off our successful workshop on Sports Analytics at ECML/PKDD 2013, which was attended by about 50 people, we intend to change this by hosting a second edition at ECML/PKDD 2015. We think that the setting is interesting and challenging, and can potentially be a source of new data. Furthermore, we believe that this offers a great opportunity to bring people from outside of the Machine Learning community into contact with typical ECML/PKDD contributors as well as to highlight what the community has done and can do in the field of Sports Analytics.
To facilitate this, we have assembled a diverse program committee that includes statisticians, practitioners in sports-related matters, and Machine Learning and Data Mining researchers.