posted by organizer: JanVanHaaren || 3865 views || tracked by 8 users: [display]

MLSA 2016 : Machine Learning and Data Mining for Sports Analytics (MLSA 16) @ ECML/PKDD 2016

FacebookTwitterLinkedInGoogle

Link: https://dtai.cs.kuleuven.be/events/MLSA16/
 
When Sep 19, 2016 - Sep 19, 2016
Where Riva del Garda
Submission Deadline Jul 4, 2016
Notification Due Jul 25, 2016
Final Version Due Aug 8, 2016
Categories    machine learning   data mining   sports analytics
 

Call For Papers

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 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 topics above. Adopting a broad definition of sports, the workshop is also open to submissions on electronic sports related to any of these topics. The following types of papers can be submitted:

(1) Long papers are maximum 8 pages in Springer LNCS style and report on novel and unpublished work that might not be mature enough for a conference or journal submission.

(2) Prediction challenge papers are maximum 6 pages in Springer LNCS style and describe the approach that participants in the EURO 2016 prediction challenge have taken.

(3) Extended abstracts are maximum 2 pages in Springer LNCS style and summarize recent publications in the scope of the workshop.

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.

Related Resources

ADAH 2017   Advanced Data Analytics in Health
SDM 2018   SIAM International Conference on Data Mining
ICPR 2018   24th International Conference on Pattern Recognition
MLDM 2018   14th International Conference on Machine Learning and Data Mining MLDM 2018
ETHE Blearning 2017   Blended learning in higher education: research findings
PAKDD 2018   The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining
COLT 2018   Computational Learning Theory
ICDM 2018   18th Industrial Conference on Data Mining ICDM 2018
DSA 2018   The Frontiers in Intelligent Data and Signal Analysis DSA 2018
ICLR 2018   6th International Conference on Learning Representations