MLSA 2023 : Machine Learning and Data Mining for Sports Analytics

*Scope of the workshop*

Sports Analytics has become an increasingly important field in recent years, with the use of advanced analytical techniques and data analysis transforming the way sports teams and organizations operate. From player evaluation and performance prediction to match strategy and tactics, the impact of data-driven decision-making in sports cannot be ignored. Moreover, as the amount of available data continues to grow and the methods used to analyze it become more sophisticated, the potential benefits of Sports Analytics are also increasing. In particular, the use of machine learning and data mining techniques has opened up new opportunities for Sports Analytics researchers and practitioners.
The workshop will cover a range of topics related to Sports Analytics, including:

- Match analysis and strategy
- Player recruitment, valuation, and team spending
- Talent identification and development
- Training and performance optimization
- Injury prevention and management
- Performance prediction and evaluation
- Match outcome and league ranking prediction
- Tournament design and scheduling optimization
- Betting odds modeling and analysis

We look forward to receiving high-quality submissions from researchers and practitioners in both the sports and machine learning/data mining communities. The workshop will provide a valuable opportunity to showcase the latest advances in Sports Analytics research, foster collaborations between researchers from different backgrounds and inspire further research in this exciting and rapidly evolving field.

*Submissions*

The workshop welcomes papers covering both predictive and descriptive Machine Learning, Data Mining, and related approaches to Sports Analytics settings. The scope of the workshop includes, but is not limited to, the list of topics mentioned above. We adopt a broad definition of sports and are open to submissions on electronic sports (i.e., e-sports) that relate to any of these topics. There are two types of papers that 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will be 9 pages of content and an unlimited number of references in the Springer LNCS style and should report on novel, unpublished work that might not be quite mature enough for a conference or journal submission.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 workshops.

Papers are to be submitted in pdf format via the CMT system: https://cmt3.research.microsoft.com/ECMLPKDDworkshop2023. When submitting, authors need to choose the track "10th Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA)".

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: 12/06/2022
Author notification: 12/07/2022
Camera-ready due: 26/07/2022

*Contact*
Ulf Brefeld: [email protected]
Jesse Davis: [email protected]
Jan van Haaren: [email protected]
Albrecht Zimmermann: [email protected]

Workshop site: https://dtai.cs.kuleuven.be/events/MLSA23/