System and method for content and style predictions in sports
A system and method for generating a play prediction for a team is disclosed herein. A computing system retrieves trajectory data for a plurality of plays from a data store. The computing system generates a predictive model using a variational autoencoder and a neural network by generating one or more input data sets, learning, by the variational autoencoder, to generate a plurality of variants for each play of the plurality of plays, and learning, by the neural network, a team style corresponding to each play of the plurality of plays. The computing system receives trajectory data corresponding to a target play. The predictive model generates a likelihood of a target team executing the target play by determining a number of target variants that correspond to a target team identity of the target team.
1. A method of generating a play prediction for a team, comprising:
retrieving, by a computing system comprising a processor and a memory, trajectory data for a plurality of plays from a data store;
generating, by the computing system, a predictive model using a variational autoencoder and a neural network, by:
generating one or more input data sets, each input data set comprising tracking information for a play of the plurality of plays;
learning, by the variational autoencoder, to generate a plurality of variants for each play of the plurality of plays, wherein each variant comprises trajectory information corresponding thereto; and
learning, by the neural network, a team style corresponding to each play of the plurality of plays;
receiving, by the computing system, trajectory data for a target play;
generating, via the predictive model, a likelihood of a target team executing the target play by determining a number of target variants that correspond to a target team identity of the target team;
generating, by the computing system, a graphical representation of each target variant, wherein the graphical representation comprises a trajectory of each play on a field and a team associated therewith; and
causing output, by the computing system, of the graphical representation via a display.
2. The method of claim 1 , wherein generating, via the predictive model, the likelihood of the target team executing the target play, comprises:
generating a plurality of target variants for the target play using the variational autoencoder.
3. The method of claim 2 , further comprising:
associating each target variant of the plurality of target variants with a team identity.
4. The method of claim 1 , wherein generating, via the predictive model, the likelihood of the target team executing the target play, comprises:
comparing the number of the target variants that correspond to the target team identity of the target team in relation to a number of other variants corresponding to other team identities.
5. The method of claim 1 , wherein the input data set further comprises possession information, playing style information, and team identity information.
6. The method of claim 1 , wherein the target play comprises a content and a style corresponding thereto, and each variant of the target play comprises the content and a variant style corresponding thereto.
7. A system for generating a play prediction for a team, comprising:
a processor; and
a memory having programming instructions stored thereon, which, when executed by the processor, cause the system to perform one or more operations, comprising:
retrieving trajectory data for a plurality of plays from a data store;
generating a predictive model using a variational autoencoder and a neural network, by:
generating one or more input data sets, each input data set comprising tracking information for a play of the plurality of plays;
learning, by the variational autoencoder, to generate a plurality of variants for each play of the plurality of plays, wherein each variant comprises trajectory information corresponding thereto; and
learning, by the neural network, a team style corresponding to each play of the plurality of plays;
receiving trajectory data corresponding to a target play;
generating, via the predictive model, a likelihood of a target team executing the target play by determining a number of target variants that correspond to a target team identity of the target team;
generating a graphical representation of each target variant, wherein the graphical representation comprises a trajectory of each play on a field and a team associated therewith; and
causing output of the graphical representation via a display.
8. The system of claim 7 , wherein generating, via the predictive model, the likelihood of the target team executing the target play, comprises:
generating a plurality of target variants for the target play using the variational autoencoder.
9. The system of claim 8 , further comprising:
associating each target variant of the plurality of target variants with a team identity.
10. The system of claim 7 , wherein generating, via the predictive model, the likelihood of the target team executing the target play, comprises:
comparing the number of the target variants that correspond to the target team identity of the target team in relation to a number of other variants corresponding to other team identities.
11. The system of claim 7 , wherein the input data set further comprises possession information, playing style information, and team identity information.
12. The system of claim 7 , wherein the target play comprises a content and a style corresponding thereto, and each variant of the target play comprises the content and a variant style corresponding thereto.
13. A non-transitory computer readable medium including one or more sequences of instructions that, when executed by one or more processors, cause a computing system comprising the one or more processors to perform operations comprising:
retrieving trajectory data for a plurality of plays from a data store;
generating a predictive model using a variational autoencoder and a neural network, by:
generating one or more input data sets, each input data set comprising tracking information for a play of the plurality of plays;
learning, by the variational autoencoder, to generate a plurality of variants for each play of the plurality of plays, wherein each variant comprises trajectory information corresponding thereto; and
learning, by the neural network, a team style corresponding to each play of the plurality of plays;
receiving by the computing system, trajectory data corresponding to a target play;
generating, via the predictive model, a likelihood of a target team executing the target play by determining a number of target variants that correspond to a target team identity of the target team;
generating a graphical representation of each target variant, wherein the graphical representation comprises a trajectory of each play on a field and a team associated therewith; and
causing output of the graphical representation via a display.
14. The non-transitory computer readable medium of claim 13 , wherein generating, via the predictive model, the likelihood of the target team executing the target play, comprises:
generating a plurality of target variants for the target play using the variational autoencoder.
15. The non-transitory computer readable medium of claim 13 , further comprising:
associating each target variant of the plurality of target variants with a team identity.
16. The non-transitory computer readable medium of claim 13 , wherein generating, via the predictive model, the likelihood of the target team executing the target play, comprises:
comparing the number of the target variants that correspond to the target team identity of the target team in relation to a number of other variants corresponding to other team identities.
17. The non-transitory computer readable medium of claim 13 , wherein the input data set further comprises possession information, playing style information, and team identity information.