Analytics

In this paper, we present a method which accurately estimates the likelihood of chances in soccer using strategic features from an entire season of player and ball tracking data taken from a professional league.

In this paper, we give an overview of the types of analysis currently performed mostly with hand-labeled event data and highlight the problems associated with the influx of spatiotemporal data.

In this paper, we present a method which can accurately determine the identity of a team from spatiotemporal player tracking data. We do this by utilizing a formation descriptor which is found by minimizing the entropy of role-specific occupancy maps.

In this paper, we propose an “augmented- Hidden Conditional Random Field” (a-HCRF) which incorporates the local observation within the HCRF which boosts its forecasting performance.

We propose a method of representing audience behavior through facial and body motions from a single video stream and use these motions to predict the rating for feature-length movies.

In this paper, we use ball and player tracking data from “Hawk-Eye” to discover unique player styles and predict within-point events.

In this paper, we use ball and player tracking data from STATS SportsVU from the 2012-2013 NBA season to analyze offensive and defensive formations of teams.