Machine Learning and Data Mining for Sports Analytics 2018

Країна: Ірладнія

Місто: Dublin

Тези до: 02.07.2018

Дати: 10.09.18 — 10.09.18

Область наук: Технічні;

Е-мейл Оргкомітету: https://easychair.org/conferences/?conf=mlsa18

Організатори: ECML/PKDD

 

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 strict financial fair-play regulations in European football will definitely increase the importance of Sports Analytics in the coming years. In addition, there is of course the popularity of sports betting. The developed approaches are being used for decision support in all aspects of professional sports, including:
- Analyzing positional data (tracking data)
- 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 and league table 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, 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 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 chosen to broaden the scope of the workshop and solicit e-sports submissions as well.
The majority of techniques used in the field so far are statistical. However, there has been growing interest in the Machine Learning and Data Mining community about this topic. Building off our successful workshops on Sports Analytics at ECML/PKDD 2013, ECML/PKDD 2015, ECML/PKDD 2017, and ECML/PKDD 2017 we wish to continue to grow this interest by hosting a fourth edition at ECML/PKDD 2018. 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.

Веб-сторінка конференції: https://dtai.cs.kuleuven.be/events/MLSA18/

Конференції по темі - із близькими дедлайнами: