=== Call for Submissions === When: August 24th Where: New York City, NY Website: http://large-scale-sports-analytics.org/ Description: 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 Submission Details: Poster submissions should be extended abstracts no more than 4 pages in length (in KDD format, do not need to be anonymous). Extended abstracts should be submitted by June 17th 11:59 PM PDT, and can be submitted electronically via: https://cmt.research.microsoft.com/LSSA2014 Important Dates: Submission - 17th June 2014 11:59 PM PDT Notification - 8th July 2014 Workshop - 24th August 2014 Organizers: Yisong Yue (Disney Research) <yisong.yue@disneyresearch.com> Patrick Lucey (Disney Research) <patrick.lucey@disneyresearch.com> Peter Carr (Disney Research) <peter.carr@disneyresearch.com> Jenna Wiens (MIT) <jwiens@mit.edu>