IP Library › Granted Patent US 12,374,110
Granted Patent B2
US 12,374,110 · App. 18/425,094 · Granted Jul 29, 2025

System and method for predicting formation in sports

Inventors: Jennifer Hobbs (Chicago, IL); Sujoy Ganguly (Chicago, IL); Patrick Joseph Lucey (Chicago, IL)
Assignee: STATS LLC
G06V20/42G06N3/08G06N5/04
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Quick Facts
Patent No.
US 12,374,110
App. No.
18/425,094
Granted
Jul 29, 2025
Kind
B2
Abstract

A system and method of predicting a team's formation on a playing surface are disclosed herein. A computing system retrieves one or more sets of event data for a plurality of events. Each set of event data corresponds to a segment of the event. A deep neural network, such as a mixture density network, learns to predict an optimal permutation of players in each segment of the event based on the one or more sets of event data. The deep neural network learns a distribution of players for each segment based on the corresponding event data and optimal permutation of players. The computing system generates a fully trained prediction model based on the learning. The computing system receives target event data corresponding to a target event. The computing system generates, via the trained prediction model, an expected position of each player based on the target event data.

Claims (48)

1. A computer-implemented method for training a mixture density network to predict a team formation, the computer-implemented method comprising:

receiving, by one or more processors, one or more sets of event data from a data store;

parameterizing, by the one or more processors, a neural network based on the one or more sets of event data;

training, by the one or more processors, the neural network to predict an optimal formation of a plurality of players of a team and to generate a semantic label corresponding to the optimal formation;

training, by the one or more processors, the neural network to predict a distribution of the plurality of players of the team based on the one or more sets of event data and the optimal formation of the plurality of players; and

outputting, by the one or more processors, the trained neural network configured to predict a formation of the team and generate the semantic label.

2. The computer-implemented method of claim 1 , wherein each of the one or more sets of event data include information associated with possession information of a sporting match.

3. The computer-implemented method of claim 1 , wherein training the mixture density network to predict the optimal formation of the plurality of players comprises:

generating, by the one or more processors, a set of possible permutations by soft-assigning each player to a role in each permutation of the set of possible permutations.

4. The computer-implemented method of claim 3 , wherein the optimal formation is selected from the set of possible permutations.

5. The computer-implemented method of claim 1 , the computer-implemented method further comprising:

generating, by the one or more processors, a semantic label associated with the optimal formation.

6. The computer-implemented method of claim 5 , the computer-implemented method further comprising:

outputting, by the one or more processors, the semantic label associated with the optimal formation.

7. The computer-implemented method of claim 1 , the training the neural network to predict the distribution of the plurality of players based on the one or more sets of event data further comprising:

predicting, by the one or more processors, at least one underlying distribution corresponding to a model of one or more player positions.

8. A system for training a mixture density network to predict a team formation, comprising:

a processor; and

a memory having programming instructions stored thereon, which, when executed by the processor, performs one or more operations, comprising:

receiving one or more sets of event data from a data store;

parameterizing a neural network based on the one or more sets of event data;

training, the neural network to predict an optimal formation of a plurality of players of a team and to generate a semantic label corresponding to the optimal formation;

training the neural network to predict a distribution of the plurality of players of the team based on the one or more sets of event data and the optimal formation of the plurality of players; and

outputting the trained neural network configured to predict a formation of the team and generate the semantic label.

9. The system of claim 8 , wherein each of the one or more sets of event data include information associated with possession information of a sporting match.

10. The system of claim 8 , wherein training the neural network to predict the optimal formation of the plurality of players comprises:

generating a set of possible permutations by soft-assigning each player to a role in each permutation of the set of possible permutations.

11. The system of claim 10 , wherein the optimal formation is selected from the set of possible permutations.

12. The system of claim 8 , the operations further comprising:

generating a semantic label associated with the optimal formation.

13. The system of claim 12 , the operations further comprising:

outputting the semantic label associated with the optimal formation.

14. The system of claim 8 , the training the neural network to predict the distribution of the plurality of players based on the one or more sets of event data further comprising:

predicting at least one underlying distribution corresponding to a model of one or more player positions.

15. A non-transitory computer readable medium including one or more sequences of instructions that, when executed by one or more processors, causes a computing system to perform operations, comprising:

receiving, by the computing system, one or more sets of event data from a data store;

parameterizing, by the computing system, a neural network based on the one or more sets of event data;

training, by the computing system, the neural network to predict an optimal formation of a plurality of players of a team and to generate a semantic label corresponding to the optimal formation;

training, by the computing system, the neural network to predict a distribution of the plurality of players of the team based on the one or more sets of event data and the optimal formation of the plurality of players; and

outputting, by the computing system, the trained neural network configured to predict a formation of the team and generate the semantic label.

16. The non-transitory computer readable medium of claim 15 , wherein each of the one or more sets of event data include information associated with possession information of a sporting match.

17. The non-transitory computer readable medium of claim 15 , wherein training the neural network to predict the optimal formation of the plurality of players comprises:

generating, by the computing system, a set of possible permutations by soft-assigning each player to a role in each permutation of the set of possible permutations.

18. The non-transitory computer readable medium of claim 17 , wherein the optimal formation is selected from the set of possible permutations.

19. The non-transitory computer readable medium of claim 15 , the operations further comprising:

generating, by the one or more processors, a semantic label associated with the optimal formation.

20. The non-transitory computer readable medium of claim 19 , the operations further comprising:

outputting, by the computing system, the semantic label associated with the optimal formation.

Assignments (2)
SECURITY INTEREST Recorded Apr 14, 2026
From: STATS LLC
To: MORGAN STANLEY SENIOR FUNDING, INC., AS COLLATERAL AGENT
Reel/Frame 075390/0491 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 30, 2024
From: HOBBS, JENNIFER; GANGULY, SUJOY; LUCEY, PATRICK JOSEPH
To: STATS LLC
Reel/Frame 066291/0695 →
Continuity (3)
Continuation 17303361 · May 27, 2021
Provisional Application 63035137 · Jun 5, 2020
Related Publication 20240185604A1 · Jun 6, 2024
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