System and method for tracking sports players to generate and apply receiver tracking metrics

A computer-based method for using player tracking data of a plurality of players playing a sport to provide receiver tracking metrics (RTM) scores for at least one player of the plurality of players, comprising:

• receiving the player tracking data from a player tracking system indicative of at least a position of each of the plurality of players and a corresponding time;
• determining a catch/no-catch probability for a given pass route for the at least one player using the player tracking data and a neural network model, the neural network comprising a convolutional neural network (CNN), which receives the player tracking data as 3D tensor data comprising number of input variables, number of defensive players, and number of offensive players;
• determining a completion expected catch/no-catch estimation for the given pass route for a typical receiver using a classifier model, the classifier model being trained using tabular pass route data for actual targeted routes for given players, and during run time, the classifier model uses tabular pass route data for targeted and untargeted routes;
• calculating RTM sub-components of the receiver tracking metrics for the at least one player;
• calculating corresponding weightings for each of the RTM sub-components;
• and
• calculating RTM scores for the at least one player by combining the RTM sub-components and weightings, the RTM scores comprising at least one of:
• open score, catch score, YAC score, and overall RTM score.

A computer-based method for providing receiver tracking metrics (RTM) scores for at least one player of a plurality of players playing a sport using player tracking data of the plurality of players, comprising:

• receiving the player tracking data from a player tracking system indicative of relative position and velocity of the plurality of players relative to the at least one player;
• determining a catch/no-catch probability for a given pass route for the at least one player using the player tracking data and a neural network model, the neural network comprising a convolutional neural network (CNN), which receives the player tracking data as 3D tensor data comprising number of input variables, number of defensive players, and number of offensive players;
• determining a completion expected catch/no-catch estimation for the given pass route for a typical receiver using a classifier model, the classifier model being trained using tabular pass route data for actual targeted routes for given players, and during run time, the classifier model uses tabular pass route data for targeted and untargeted routes;
• calculating RTM sub-components of the receiver tracking metrics for the at least one player;
• obtaining corresponding weightings for each of the RTM sub-components;
• determining RTM scores for the at least one player by performing a weighted sum of the RTM sub-components and weightings to determine the RTM scores for the at least one player, the RTM scores comprising at least one of an open score, catch score, YAC score, and overall RTM score;
• and
• wherein the calculating the RTM sub-components comprises determining the difference between the catch/no-catch probability from the neural network model and the completion expected catch/no-catch estimation from the classifier model.