MLSA 2016 - 2016 Workshop on Machine Learning and Data Mining for Sports Analytics

Sports Analytics has been a steadily growing and rapidly evolving area over the last decade both in US professional sports leagues and in European football leagues. The recent implementation of financial fair-play regulations in European football will definitely increase the importance of Sports Analytics in the coming years. In addition, the popularity of sports betting is also ever-increasing. Sports Analytics approaches are used in all aspects of professional sports, including:
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 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, and League of Legends. Moreover, tournaments offer large sums 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 data collection is easier than for off-line sports, the workshop is open to 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 workshops on Sports Analytics in 2013 and 2015, we intend to change this by hosting a third edition at ECML/PKDD 2016. 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.