Abstract: A system is described for analyzing plays of a sporting event based on body-pose information in conjunction with real-world positional tracking data. The system permits segmentation of sporting event plays into discrete time periods and the labeling of appropriate body-pose characteristics for each period. The system allows for comparison and fine-grained analysis of plays with respect to either a series of body-pose vertices that exist in continuous space (i.e., skeleton) or map directly to attributes, using the positional tracking data and play information to account for contextual differences. To enable analysis for the former, the system performs a projection to 3D space, followed by a spatiotemporal alignment step. Through the system, the importance of particular body-pose motions or specific attributes to the success of particular sporting event plays is quantified.

Abstract: A system and method for predicting multi-agent locations is disclosed herein. A computing system retrieves tracking data from a data store. The computing system generates a predictive model using a conditional variational autoencoder. The conditional variational autoencoder learns one or more paths a subset of agents of the plurality of agents are likely to take. The computing system receives tracking data from a tracking system positioned remotely in a venue hosting a candidate sporting event. The computing system identifies one or more candidate agents for which to predict locations. The computing system infers, via the predictive model, one or more locations of the one or more candidate agents. The computing system generates a graphical representation of the one or more locations of the one or more candidate agents.

Abstract: A system and method for predicting multi-agent locations is disclosed herein. A computing system retrieves tracking data from a data store. The computing system generates a predictive model using a conditional variational autoencoder. The conditional variational autoencoder learns one or more paths a subset of agents of the plurality of agents are likely to take. The computing system receives tracking data from a tracking system positioned remotely in a venue hosting a candidate sporting event. The computing system identifies one or more candidate agents for which to predict locations. The computing system infers, via the predictive model, one or more locations of the one or more candidate agents. The computing system generates a graphical representation of the one or more locations of the one or more candidate agents.

Abstract: A system is described for analyzing plays of a sporting event based on body-pose information in conjunction with real-world positional tracking data. The system permits segmentation of sporting event plays into discrete time periods and the labeling of appropriate body-pose characteristics for each period. The system allows for comparison and fine-grained analysis of plays with respect to either a series of body-pose vertices that exist in continuous space (i.e., skeleton) or map directly to attributes, using the positional tracking data and play information to account for contextual differences. To enable analysis for the former, the system performs a projection to 3D space, followed by a spatiotemporal alignment step. Through the system, the importance of particular body-pose motions or specific attributes to the success of particular sporting event plays is quantified.

Abstract: A system is described for analyzing plays of a sporting event based on body-pose information in conjunction with real-world positional tracking data. The system permits segmentation of sporting event plays into discrete time periods and the labeling of appropriate body-pose characteristics for each period. The system allows for comparison and fine-grained analysis of plays with respect to either a series of body-pose vertices that exist in continuous space (i.e., skeleton) or map directly to attributes, using the positional tracking data and play information to account for contextual differences. To enable analysis for the former, the system performs a projection to 3D space, followed by a spatiotemporal alignment step. Through the system, the importance of particular body-pose motions or specific attributes to the success of particular sporting event plays is quantified.

Abstract: A system and method for predicting multi-agent locations is disclosed herein. A computing system retrieves tracking data from a data store. The computing system generates a predictive model using a conditional variational autoencoder. The conditional variational autoencoder learns one or more paths a subset of agents of the plurality of agents are likely to take. The computing system receives tracking data from a tracking system positioned remotely in a venue hosting a candidate sporting event. The computing system identifies one or more candidate agents for which to predict locations. The computing system infers, via the predictive model, one or more locations of the one or more candidate agents. The computing system generates a graphical representation of the one or more locations of the one or more candidate agents.

Abstract: A system is described for analyzing plays of a sporting event based on body-pose information in conjunction with real-world positional tracking data. The system permits segmentation of sporting event plays into discrete time periods and the labeling of appropriate body-pose characteristics for each period. The system allows for comparison and fine-grained analysis of plays with respect to either a series of body-pose vertices that exist in continuous space (i.e., skeleton) or map directly to attributes, using the positional tracking data and play information to account for contextual differences. To enable analysis for the former, the system performs a projection to 3D space, followed by a spatiotemporal alignment step. Through the system, the importance of particular body-pose motions or specific attributes to the success of particular sporting event plays is quantified.