A system and method for tracking sports players to generate and apply receiver tracking metrics includes determining a catch/no-catch probability for a given pass route for a specific receiver using the player tracking data using a neural network model, determining a completion expected catch/no-catch estimation for a given pass route for a typical receiver using a classifier model and pass route data, calculating RTM sub-components of the receiver tracking metrics using the catch/no-catch probability and the completion expected catch/no-catch estimation, calculating corresponding weightings for each of the RTM sub-components, and calculating RTM scores by combining the RTM sub-components and weightings, the RTM scores including at least one of: open score, catch score, YAC score, and overall RTM score. In some embodiments, RTMs may be used to enhance or improve an end software application.
BACKGROUND
In sports where players must elude defenders to receive a pass and then advance toward a goal, such as in the sport of American Football, measurement of athlete performance using location sensing technology and time stamps associated therewith has the potential to enable advanced insights into an athlete's performance. Conventional box score statistics data, such as targets, catches, touchdowns, yards or yards per attempt, do not provide sufficient insight into receiver performance.
One technique currently used is called “Completion Percentage Over Expected” (CPOE), which estimates the chance (or probability) of a completion on a given pass, given the locations, directions and speeds of relevant players on the field to generate an expected completion benchmark. If a completion occurs, the passer (e.g., a football quarterback or other thrower) would be credited with all the probability between the prediction and 1. However, CPOE and associated metrics have a flaw when applied to pass-catchers, because the locations, directions, and speeds of the other players are impacted by the catcher's actions. The CPOE “benchmark value”, therefore, includes the catching/receiving ability of a given receiver, which inherently includes his ability to create space between himself and his defenders (“get open”) to receive the pass. Thus, the better the receiver is at getting open, the higher the benchmark the receiver sets for himself in the metric and the narrower the
1. A computer-based system for generating player tracking data for a plurality of players playing a sport and using the player tracking data to provide a receiver tracking metrics (RTM) score for at least one player of the plurality of players, comprising: a player tracking system configured to generate player tracking data indicative of at least the position of each of the plurality of players and a corresponding time; a processor configured to receive the player tracking data from the player tracking system and configured to determine 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 processor further configured to receive pass route data related to the given pass route and defensive coverage and configured to determine a completion expected catch/no-catch estimation for the given pass route for a typical receiver using a classifier model; the processor further configured to determine at least one RTM sub-component of the receiver tracking metrics for the at least one player, based on the catch/no-catch probability and the completion expected catch/no-catch estimation for the given pass route; the processor further configured to receive receiver production data and configured to determine weightings corresponding to each of the RTM sub-components based on the values of the RTM sub-components, the real-world receiver production data, and the type of receiver group for the at least one player; and the processor further configured to combine the RTM sub-components and the corresponding weightings to determine the RTM scores for the at least one player, the RTM scores comprising at least one of: open score, catch score, YAC score, and overall RTM score. ||
21. 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 the 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; determining a completion expected catch/no-catch estimation for a given pass route for a typical receiver using a classifier model; 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. ||
40. 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 the relative position and velocity of the 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; determining a completion expected catch/no-catch estimation for a given pass route for a typical receiver using a classifier model; calculating RTM sub-components of the receiver tracking metrics for the specific receiver to be analyzed; 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 including at least one of an open score, catch score, YAC score, and overall RTM score; and wherein the determining the at least one RTM sub-component comprises determining the difference between the catch/no-catch probability from the neural network model and the catch/no-catch estimation from the classifier model.