US-20260127879-A1 - SYSTEMS AND METHODS FOR IMPLEMENTING SPORTS TRACKING DATA

US20260127879A1US 20260127879 A1US20260127879 A1US 20260127879A1US-20260127879-A1

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.

Inventors

Harry HUGHES
Michael John Horton
Felix WEI
Patrick Joseph LUCEY

Assignees

STATS LLC

Dates

Publication Date: 20260507
Application Date: 20250829

Claims (20)

1 . A computer implemented method for tracking one or more individuals during a sporting event, the method comprising: 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 broadcast tracking data to determine one or more vectors; inputting the labeled event data and one or more vectors into a diffusion model; 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.
2 . The method of claim 1 , further including: determining, a sequence of past events from the sporting event, the sequences corresponding to one or more plays in the sporting event.
3 . The method of claim 1 , further including: determining, one or more alternative trajectory sequences for the one or more agents, the one or more alternative trajectory being trajectories of highest predicted success for the one or more agents.
4 . The method of claim 1 , further including: generating, with a second machine learning model, a textual description of the broadcast tracking data and the labeled event data.
5 . The method of claim 1 , wherein the broadcast tracking data and/or the labeled event data includes incomplete data of the sporting event.
6 . The method of claim 1 , wherein the sporting event is soccer, football, or hockey.
7 . A system for tracking one or more individuals during a sporting event, the system comprising: a non-transitory computer readable medium configured to store processor-readable instructions; and a processor operatively connected to the non-transitory computer readable medium, and configured to execute the instructions to perform operations comprising: 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; 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.
8 . The system of claim 7 , further including: determining, a sequence of past events from the sporting event, the sequences corresponding to one or more plays in the sporting event.
9 . The system of claim 7 , further including: determining, one or more alternative trajectory sequences for the one or more agents, the one or more alternative trajectory being trajectories of highest predicted success for the one or more agents.
10 . The system of claim 7 , further including: generating, with a second machine learning model, a textual description of the broadcast tracking data and the labeled event data.
11 . The system of claim 7 , wherein the broadcast tracking data and/or the labeled event data includes incomplete data of the sporting event.
12 . The system of claim 7 , wherein the sporting event is soccer, football, or hockey.
13 . A non-transitory computer readable medium configured to store processor-readable instructions, wherein when executed by a processor, the instructions perform operations comprising: 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; 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.
14 . The non-transitory computer readable medium of claim 13 , further including: determining, a sequence of past events from the sporting event, the sequences corresponding to one or more plays in the sporting event.
15 . The non-transitory computer readable medium of claim 13 , further including: determining, one or more alternative trajectory sequences for the one or more agents, the one or more alternative trajectory being trajectories of highest predicted success for the one or more agents.
16 . The non-transitory computer readable medium of claim 15 , wherein the one or more alternative trajectories, being a respective trajectory with a highest percentage chance of a particular play in the sporting event ending with a goal.
17 . The non-transitory computer readable medium of claim 13 , further including: generating, with a second machine learning model, a textual description of the broadcast tracking data and the labeled event data.
18 . The non-transitory computer readable medium of claim 13 , wherein the broadcast tracking data and/or the labeled event data includes incomplete data of the sporting event.
19 . The non-transitory computer readable medium of claim 13 , wherein the sporting event is soccer, football, or hockey.
20 . The non-transitory computer readable medium of claim 13 , further including: determining one or more fitness outputs for the one or more agents, the one or more fitness outputs each indicating how far a player has run throughout the sporting event.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Patent Application No. 63/696,918, filed on Sep. 20, 2024, the entirety of which is incorporated herein by reference. TECHNICAL FIELD Various aspects of the present disclosure relate generally to machine learning for sports applications, in particular various aspects relate to machine learning techniques for systems and methods for downstream analysis of sports tracking data. BACKGROUND With the rising popularity of sports, there is an increased desire for data relating to sports events, such as, for example, accurate granular predictions of what will occur during a sporting event. This desire extends beyond traditional statistics such as scores and win-loss records, encompassing more granular data including predictions, player analysis, simulations, animations, etc. For example, predicting how the number of passes or shots that a particular soccer player (e.g., Lionel Messi) will have in the given game (e.g., World Cup final), both prior to and during the World Cup final, can be of particular interest to members of the media, broadcast (whether on the primary feed, or a second screen experience), sportsbook, and fantasy/gamification applications. Existing solutions are unable to accurately make such predictions. In particular, existing solutions may be unable to accurately make predictions to the trajectory one or more players in a game. Furthermore, existing solutions may be unable to collect such data without invasive or expensive monitoring systems, such as GPS trackers, heart rate monitors, etc. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art, or suggestions of the prior art, by inclusion in this section. SUMMARY OF THE DISCLOSURE In some aspects, the techniques described herein relate to a 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; 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. In some aspects, the techniques described herein relate to a method, further including: determining, a sequence of past events from the sporting event, the sequences corresponding to one or more plays in the sporting event. In some aspects, the techniques described herein relate to a method, further including: determining, one or more alternative trajectory sequences for the one or more agents, the one or more alternative trajectory being trajectories of highest predicted success for the one or more agents. In some aspects, the techniques described herein relate to a method, further including: generating, with a second machine learning model, a textual description of the broadcast tracking data and the labeled event data. In some aspects, the techniques described herein relate to a method, wherein the broadcast tracking data and/or the labeled event data includes incomplete data of the sporting event. In some aspects, the techniques described herein relate to a method, wherein the sporting event is soccer, football, or hockey. In some aspects, the techniques described herein relate to a system for tracking one or more individuals during a sporting event, the system including: a non-transitory computer readable medium configured to store processor-readable instructions; and a processor operatively connected to the non-transitory computer readable medium, and configured to execute the instructions to perform operations 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; 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. In some aspects, the techniques described herein relate to a system, further including: determining, a sequence of past events from the sporting event, the sequences corresponding to one or more plays in the sporting event. In some aspects, the techniques described herein relate to a system, further including: determining, one or more alternative trajectory sequences for the one or more agents, the one or more alternative trajectory being trajectorie