System and Method for Improved Structural Discovery and Representation Learning of Multi-Agent Data
A computing system retrieves player tracking data for a plurality of players across a plurality of events. The player tracking data includes coordinates of player positions during each event. The computing system initializes the player tracking data based on an average position of each player in the plurality of events. The computing system learns an optimal formation of player positions based on the player tracking data using a Gaussian mixture model. The computing system aligns the optimal formation of player positions to a global template by identifying a distance between each distribution in the optimal formation and each distribution in the global template to generate a learned formation template. The computing system assigns a role to each player in the learned template.
1 . A method, comprising:
retrieving, by a computing system, player tracking data for a plurality of players across a plurality of events, the player tracking data comprising coordinates of player positions during each event;
initializing, by the computing system, the player tracking data based on an average position of each player in the plurality of events;
learning, by the computing system, an optimal formation of player positions based on the player tracking data using a Gaussian mixture model;
aligning, by the computing system, the optimal formation of player positions to a global template by identifying a distance between each distribution in the optimal formation and each distribution in the global template to generate a learned formation template; and
assigning, by the computing system, a role to each player in the learned formation template.
2 . The method of claim 1 , further comprising:
generating, by the computing system, aligned data comprising a per-frame ordered role assignment of players; and
clustering, by the computing system, the aligned data to identify new formations.
3 . The method of claim 2 , wherein the clustering comprises a flat or hierarchical clustering algorithm.
4 . The method of claim 1 , further comprising:
filtering, by the computing system, the player tracking data to identify event frames corresponding to frames of tracking data in which an even occurs.
5 . The method of claim 1 , further comprising:
normalizing, by the computing system, the player tracking data so that all players in the player tracking data are attacking from left to right.
6 . The method of claim 1 , wherein learning, by the computing system, the optimal formation of player positions based on the player tracking data using the Gaussian mixture model comprises:
parametrizing a distribution of player positions as a mixture of K Gaussians to identify the optimal formation.
7 . The method of claim 1 , wherein learning, by the computing system, the optimal formation of player positions based on the player tracking data using the Gaussian mixture model comprises:
monitoring eigenvalues throughout the learning to determine if an eigenvalue ratio is outside a range of acceptable values; and
upon determining that the eigenvalue ratio is outside the range of acceptable values, resetting the Gaussian mixture model before continuing the learning.
8 . A non-transitory computer readable medium comprising instructions which, when executed by a computing system, cause the computing system to perform operations comprising:
retrieving, by a computing system, player tracking data for a plurality of players across a plurality of events, the player tracking data comprising coordinates of player positions during each event;
initializing, by the computing system, the player tracking data based on an average position of each player in the plurality of events;
learning, by the computing system, an optimal formation of player positions based on the player tracking data using a Gaussian mixture model;
aligning, by the computing system, the optimal formation of player positions to a global template by identifying a distance between each distribution in the optimal formation and each distribution in the global template to generate a learned formation template; and
assigning, by the computing system, a role to each player in the learned formation template.
9 . The non-transitory computer readable medium of claim 8 , further comprising:
generating, by the computing system, aligned data comprising a per-frame ordered role assignment of players; and
clustering, by the computing system, the aligned data to identify new formations.
10 . The non-transitory computer readable medium of claim 9 , wherein the clustering comprises a flat or hierarchical clustering algorithm.
11 . The non-transitory computer readable medium of claim 8 , further comprising:
filtering, by the computing system, the player tracking data to identify event frames corresponding to frames of tracking data in which an even occurs.
12 . The non-transitory computer readable medium of claim 8 , further comprising:
normalizing, by the computing system, the player tracking data so that all players in the player tracking data are attacking from left to right.
13 . The non-transitory computer readable medium of claim 8 , wherein learning, by the computing system, the optimal formation of player positions based on the player tracking data using the Gaussian mixture model comprises:
parametrizing a distribution of player positions as a mixture of K Gaussians to identify the optimal formation.
14 . The non-transitory computer readable medium of claim 8 , wherein learning, by the computing system, the optimal formation of player positions based on the player tracking data using the Gaussian mixture model comprises:
monitoring eigenvalues throughout the learning to determine if an eigenvalue ratio is outside a range of acceptable values; and
upon determining that the eigenvalue ratio is outside the range of acceptable values, resetting the Gaussian mixture model before continuing the learning.
15 . A system comprising:
a processor; and
a memory having programming instructions stored thereon, which, when executed by the processor, performs operations comprising:
receiving a request from a client device to identify a team's formation and role assignment across a selected subset of games, wherein the request defines a context within each game of the subset of games;
retrieving player tracking data for the selected subset of games, the player tracking data comprising coordinates of player positions during each game;
filtering the player tracking data to identify frames corresponding to the defined context;
learning an optimal formation of player positions based on the player tracking data and the defined context using a Gaussian mixture model to generate a learned formation template;
assigning a role to each player in the learned formation template; and
generating a graphical representation of a structured representation of a team's formation across the subset of games for the defined context.
16 . The system of claim 15 , wherein the operations further comprise:
generating aligned data comprising a per-frame ordered role assignment of players; and
clustering the aligned data to identify new formations.
17 . The system of claim 16 , wherein the clustering comprises a flat or hierarchical clustering algorithm.
18 . The system of claim 15 , wherein the defined context corresponds to an in-game situation.
19 . The system of claim 15 , wherein learning the optimal formation of player positions based on the player tracking data using the Gaussian mixture model comprises:
parametrizing a distribution of player positions as a mixture of K Gaussians to identify the optimal formation.
20 . The system of claim 15 , wherein learning the optimal formation of player positions based on the player tracking data using the Gaussian mixture model comprises:
monitoring eigenvalues throughout the learning to determine if an eigenvalue ratio is outside a range of acceptable values; and
upon determining that the eigenvalue ratio is outside the range of acceptable values, resetting the Gaussian mixture model before continuing the learning.