OBJECTIVES OF WORKSHOP:
Virtually every aspect of sports analytics is now entering the “Big Data” phase, and the interest in effectively mining, modeling, and learning from such data has also been correspondingly growing. Relevant data sources include detailed play-by-play game logs, tracking data, physiological sensor data to monitor the health of players, social media and text-based content, and video recordings of games.
The objective of this workshop is to bring together researchers and analysts from academia and industry who work in sports analytics, data mining and machine learning. We hope to enable meaningful discussions about state-of-the-art in sports analytics research, and how it might be improved upon.
We seek poster submissions (which can be both preliminary research as well as recently published work) on topics including but not limited to:
- Spatiotemporal modeling
- Video, text and social media analysis
- Feature selection and dimensionality reduction
- Feature learning and latent factor models
- Computational rationality
- Real-time predictive modeling
- Interactive analysis & visualization tools
- Sensor technology and reliability
- Labeling and annotation of events/activities/tactics
- Real-time/deployed analytical systems
- Knowledge discovery of player/team/league behaviors
- Game theory