Methods for detecting events in sports using a convolutional neural network

A system for identifying a defensive alignment and an offensive alignment in a set-piece, 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 streams of tracking data associated with one or more matches;
• identifying one or more set-piece plays contained in the one or more streams of the tracking data, wherein the one or more set-piece plays include a portion of the one or more matches where play is started following a stoppage or re-started following the stoppage;
• identifying a defensive alignment of a first team by:
• extracting, via a trained convolutional neural network, one or more features corresponding to a type of defensive alignment implemented by the first team by passing each of the set-piece plays through the trained convolutional neural network, wherein the one or more features include one or more local edge patterns, one or more interactions, or one or more player locations;
• identifying an offensive alignment of a second team by:
• scanning each of the one or more set-piece plays, via the trained convolutional neural network, to identify one or more features indicative of a type of offensive alignment implemented by the second team;
• determining a success outcome for each of the one or more set-piece plays;
• generating a first graphical representation of the defensive alignment or the offensive alignment and a corresponding effectiveness, wherein the effectiveness is based on the success outcome for each of the one or more set-piece plays, and wherein the effectiveness indicates whether the defensive alignment was successful or whether the offensive alignment was successful;
• and
• generating a second graphical representation that includes a cluster of an average team behavior for the first team or the second team, wherein the average team behavior is determined based on the extracting and the scanning,
• wherein the convolutional neural network is trained by:
• retrieving a set of match data from a data store,
• identifying, in each match, one or more set-piece plays,
• extracting player motion before and after each set-piece play, and
• learning one or more features unique to each offensive and defensive alignment found in the set of match data.

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 streams of tracking data associated with one or more matches;
• identifying, by the computing system, one or more set-piece plays contained in the one or more streams of the tracking data, wherein the one or more set-piece plays include a portion of the one or more matches where play is started following a stoppage or re-started following the stoppage;
• identifying, by the computing system, a defensive alignment of a first team by:
• extracting, by the computing system via a trained convolutional neural network, one or more features corresponding to a type of defensive alignment implemented by the first team by passing each of the set-piece plays through the trained convolutional neural network, wherein the one or more features include one or more local edge patterns, one or more interactions, or one or more player locations;
• identifying, by the computing system, an offensive alignment of a second team by:
• scanning each of the one or more set-piece plays, by the computing system via the trained convolutional neural network, to identify one or more features indicative of a type of offensive alignment implemented by the second team;
• determining, by the computing system, a success outcome for each of the one or more set-piece plays;
• and
• generating, by the computing system, a first graphical representation of the defensive alignment or the offensive alignment and a corresponding effectiveness, wherein the effectiveness is based on the success outcome for each of the one or more set-piece plays, and wherein the effectiveness indicates whether the defensive alignment was successful or whether the offensive alignment was successful;
• and
• generating, by the computing system, a second graphical representation that includes a cluster of an average team behavior for the first team or the second team, wherein the average team behavior is determined based on the extracting and the scanning,
• wherein the convolutional neural network is trained by:
• retrieving a set of match data from a data store,
• identifying, in each match, one or more set-piece plays,
• extracting player motion before and after each set-piece play, and
• learning one or more features unique to each offensive and defensive alignment found in the set of match data.