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MLSA 2020 : Machine Learning and Data Mining for Sports Analytics @ ECML PKDD

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Link: https://dtai.cs.kuleuven.be/events/MLSA20/
 
When Sep 14, 2020 - Sep 18, 2020
Where Ghent
Submission Deadline Jun 9, 2020
Notification Due Jul 7, 2020
Final Version Due Jul 21, 2020
Categories    machine learning   data mining   sports analytics
 

Call For Papers

*Scope of the workshop*

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. Build- ing off our successful workshops on Sports Analytics at ECML/PKDD 2013, ECML/PKDD 2015 through ECML/PKDD 2019 we wish to continue to grow this interest by hosting a seventh edi- tion at ECML/PKDD 2020. 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.

*"What if" (prediction) challenge*

A number of professional sports leagues all over the world have interrupted their seasons due to the corona outbreak and will, in all likelihood, not resume operations. This means that the question of eventual champions will remain unresolved. We invite sports analytics researchers to try their hand at crafting the story of how the respective season ended. Note that the imperative term is *story*! It will not be enough to simply do prediction - interpret your model and *tell* us why certain match-ups (crucial matches, play-off series, finals) ended the way they did. Could a certain guard not miss from behind the arc? Did a goalie produce save after save?

Contributions will be judged both on the plausibility of the outcomes (if a middling team rips off an undefeated run, the story better be convincing), and on how "realistic" the story reads.

Options for the challenge include but are not limited to:
- the NBA (data at https://www.basketball-reference.com)
- the NHL (data at https://www.hockey-reference.com)

Participants are welcome to try their hand at other leagues but data might be harder to come by. (Don't predict the Premier league champion, please!) They can also use all additional data sources.

*Submissions*

The workshop solicits papers covering both predictive and descriptive Machine Learning, Data Mining, and related approaches to Sports Analytics settings, including, but not limited to, the list of topics above. Adopting a broad definition of sports, the workshop is also open to submissions on electronic sports (i.e., e-sports) that are related to any of these topics. The following types of papers can be submitted:

- Long papers will be 9 pages of content and an unlimited number of references in the Springer LNCS style and should report on novel, unpublished work that might not be quite mature enough for a conference or journal submission.
- Extended abstracts will be 2 pages in Springer LNCS style and summarize recent publications fitting the workshop.
- "What if" (prediction) challenge papers will be 6 pages of content, and an unlimited number of references, and up to two pages of supplementary material

Papers are to be submitted in pdf format at https://easychair.org/conferences/?conf=mlsa20

Each paper will be reviewed by at least two members of the Program Committee on the basis of technical quality, relevance, significance, and clarity. Submitting a paper to the workshop means that if the paper is accepted at least one author should present the paper at the workshop. The workshop will include invited talks, a mix of oral and poster presentations for all accepted papers, and a discussion regarding the goals, limits, and desirability of Sports Analytics.

*Key dates*

Paper submission: 09/06/2020
Author notification: 07/07/2020
ECML PKDD early bird registration deadline: 20/07/2020
Camera-ready due: 21/07/2020

*Contact*
Ulf Brefeld: brefeld@leuphana.de
Jesse Davis: jesse.davis@cs.kuleuven.be
Jan van Haaren: j.vanhaaren@scisports.com
Albrecht Zimmermann: albrecht.zimmermann@unicaen.fr

Workshop site: https://dtai.cs.kuleuven.be/events/MLSA20/

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