Abstract: A computer implemented method for tracking one or more individuals during a sporting event, the method including: receiving, as an input, broadcast tracking data of a sporting event and labeled event data of the sporting event; performing multi-object tracking of one or more agents of the received broadcast tracking data to determine one or more vectors; inputting the labeled event data and one or more vectors into a diffusion model; and determining, using the diffusion model, one or more trajectory sequences for the one or more agents; and determining, an output, based on the one or more trajectory sequences for the one or more agents.

Abstract: Systems and methods for generating trajectories for one or more players during an event include receiving broadcast footage of a sporting event, determining tracking data of one or more players in the sporting event from the broadcast footage, the tracking data including one or more vectors, receiving event data of the sporting event, and inputting the one or more vectors and event data into a multimodal model including an event encoder and a tracking decoder. A linear layer of the multimodal model may be applied to the vectors and event data to tokenize the event data and vectors. A tensor representing a sequence of the event data and tracking data may be determined. Perturbed tracking data of the sporting event and the tensor may be input into a diffusion model. The diffusion model may generate one or more trajectories for the one or more players in the sporting event.

Abstract: Disclosed techniques relate to using machine learning for sports applications. In an example, a method for generating sports tracking data using multimodal generative models may include receiving one or more inputs by a user. The input may be related to a description. The method may further include extracting metadata items relating to the description. The method may further include mapping the metadata items to at least one or more event streams. The method may further include receiving content items relating to the event streams. The event streams contain content items that are outputted by a multimodal sports learning language model (LLM). The method may further include transmitting the content items to a user device for display.

Abstract: The present embodiments relate to tracking player movements from a video broadcast and determining a defensive influence score from the tracked movements of the player. The present embodiments can implement one or more models to generate a defensive influence score that quantifies a defensive intensity of a player during the course of a game. The defensive influence score can include a frame-by-frame machine learning prediction that can be used to estimate the defensive pressure a player is having on another player during the course of the game. Additionally, the present embodiments can capture and estimate fitness metrics, such as sprints and efforts around detected plays such as pick-and-rolls and off-ball screens, which can be good proxies for player effort. Further, event detection outputs (both offensive and defensive metrics), can be used as features to estimate fitness metrics for the player (e.g., player load, sprints, jogs, etc.).

Abstract: A method for tracking one or more individuals during a sporting event, the method including: receiving, as an input, a geospatial data of a sporting event; receiving, as an input, labeled event data of the sporting event; performing multi-object tracking of one or more agents of the geospatial data to determine one or more vectors; inputting the labeled event data and one or more vectors into a diffusion model; and determining, using the diffusion model, one or more trajectory sequences for the one or more agents.

Abstract: A method for tracking one or more individuals during a sporting event, the method including: receiving, as an input, a geospatial data of a sporting event; receiving, as an input, labeled event data of the sporting event; performing multi-object tracking of one or more agents of the geospatial data to determine one or more vectors; inputting the labeled event data and one or more vectors into a diffusion model; and determining, using the diffusion model, one or more trajectory sequences for the one or more agents.