# US 12,594,462 B2 · System and method for tracking sports players to generate and apply receiver tracking metrics

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- Number: US 12,594,462 B2
- Document ID: US12594462B2
- Status: Granted
- Kind: B2
- Application number: 18/499,082
- Filed: 2023-10-31
- Published: 2025-03-27
- Granted: 2026-04-07
- Earliest claimed: 2023-09-27
- Assignees: Disney Enterprises, Inc.
- Applicants: Disney Enterprises, Inc.
- Inventors: Brian J. Burke
- Web page: https://patentgrep.com/patent/US12594462B2

## CPC

- A63B 24/0062: {Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance}
- G06V 40/23: {Recognition of whole body movements, e.g. for sport training}
- A63B 71/06: Indicating or scoring devices for games or players {, or for other sports activities}
- G06N 3/02: Neural networks
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/00: Machine learning
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06N 20/20: Ensemble learning
- A63B: APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT (apparatus for passive exercising, massage A61H)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

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.

## Claims

### Claim 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 a 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 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;
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 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;
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 values of the RTM sub-components, real-world receiver production data, and 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.

### Claim 2 (depends on 1)

The system of claim 1 wherein the neural network model comprises a convolutional neural network.

### Claim 3 (depends on 1)

The system of claim 1 wherein the classifier model comprises a random forest classifier.

### Claim 4 (depends on 1)

The system of claim 1 wherein the processor is configured to combine corresponding ones of the RTM sub-components associated with the corresponding one of the RTM scores after applying the associated weighting factor to determine at least one of the RTM scores.

### Claim 5 (depends on 1)

The system of claim 1 wherein the RTM sub-components associated with the open score comprises at least one of openness at release, openness at arrival, openness vs man-only defense at release, openness vs man-only defense at arrival, and double team adjustment.

### Claim 6 (depends on 5)

The system of claim 5 wherein the processor is configured to determine the difference between an expected number of defenders and an actual number of defenders guarding a receiver to generate the double team adjustment.

### Claim 7 (depends on 1)

The system of claim 1 wherein the RTM sub-components associated with the catch score comprises at least one of catch over predicted and catch over expected and the RTM sub-components associated with the YAC score comprises YAC over predicted.

### Claim 8 (depends on 1)

The system of claim 1 wherein the RTM sub-components associated with the Overall RTM score comprises at least one of openness at release, openness at arrival, openness vs man-only defense at release, openness vs man-only defense at arrival, double team adjustment, catch over predicted, catch over expected and YAC over predicted.

### Claim 9 (depends on 1)

The system of claim 1 wherein the pass route data, comprises at least one of: route type, coverage type, depth, time of release, distance from sideline, and situational variables.

### Claim 10 (depends on 1)

The system of claim 1 wherein the player tracking data comprises relative positions and velocities of the sports-players relative to the at least one player.

### Claim 11 (depends on 1)

The system of claim 1 wherein the neural network model uses the player tracking data for all routes run whether targeted or not.

### Claim 12 (depends on 1)

The system of claim 1 wherein the RTM score is used to provide a recommendation for an end software application.

### Claim 13 (depends on 1)

The system of claim 1 wherein the RTM score is used to provide an RTM graphic indicative of the RTM score overlayed on a broadcast display of a sporting event.

### Claim 14 (depends on 1)

The system of claim 1 wherein the RTM score is used in an end software application comprising at least one of: sports player drafting app, electronic sports games, gambling apps, and fantasy apps.

### Claim 15 (depends on 1)

The system of claim 1 wherein the classifier model comprises at least one of: Random Forest Classifier, gradient-boosted trees, logistic regression, support vector machines, and K-nearest neighbors.

### Claim 16 (depends on 1)

The system of claim 1 wherein the classifier model is trained using targeted routes only.

### Claim 17 (depends on 1)

The system of claim 1 wherein the weights have different values based on a type of receiver group.

### Claim 18 (depends on 1)

The system of claim 1 wherein the processor is configured to determine 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 to generate the at least one RTM sub-component.

### Claim 19 (depends on 1)

The system of claim 1 wherein the player tracking system comprises at least one of a signal-based system and an image-based system.

### Claim 20

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.

### Claim 21 (depends on 20)

The method of claim 20 wherein the neural network model comprises a convolutional neural network.

### Claim 22 (depends on 20)

The method of claim 20 wherein the classifier model comprises a random forest classifier.

### Claim 23 (depends on 20)

The method of claim 20 wherein the calculating RTM scores comprises calculating at least one of the RTM scores by combining corresponding ones of the RTM sub-components associated with one of the RTM scores after applying the associated weighting factor.

### Claim 24 (depends on 20)

The method of claim 20 wherein the RTM sub-components associated with the open score comprises at least one of openness at release, openness at arrival, openness vs man-only defense at release, openness vs man-only defense at arrival, and double team adjustment.

### Claim 25 (depends on 24)

The method of claim 24 wherein the double team adjustment comprises determining the difference between an expected number of defenders and an actual number of defenders guarding a receiver.

### Claim 26 (depends on 20)

The method of claim 20 wherein the RTM sub-components associated with the catch score comprises at least one of catch over predicted and catch over expected, the RTM sub-components associated with the YAC score comprises YAC over predicted, and the RTM sub-components associated with the Overall RTM score comprises at least one of openness at release, openness at arrival, openness vs man-only defense at release, openness vs man-only defense at arrival, double team adjustment, catch over predicted, catch over expected and YAC over predicted.

### Claim 27 (depends on 20)

The method of claim 20 wherein the classifier model uses pass route data, comprising at least one of: route type, coverage type, depth, time of release, distance from sideline, and situational variables.

### Claim 28 (depends on 20)

The method of claim 20 wherein the player tracking data comprises relative positions and velocities of the sports-players relative to each other.

### Claim 29 (depends on 20)

The method of claim 20 wherein the neural network model uses the player tracking data for all routes run whether targeted or not.

### Claim 30 (depends on 20)

The method of claim 20 wherein the RTM score is used to provide a recommendation for an end software application.

### Claim 31 (depends on 20)

The method of claim 20 wherein the RTM score is used to provide an RTM graphic indicative of the RTM score overlayed on a broadcast display of a sporting event.

### Claim 32 (depends on 20)

The method of claim 20 wherein the RTM score is used in an end software application comprising at least one of: sports player drafting app, electronic sports games, gambling apps, and fantasy apps.

### Claim 33 (depends on 20)

The method of claim 20 wherein the classifier model comprises at least one of: Random Forest Classifier, gradient-boosted trees, logistic regression, support vector machines, and K-nearest neighbors.

### Claim 34 (depends on 20)

The method of claim 20 wherein the classifier model is trained using targeted routes only.

### Claim 35 (depends on 20)

The method of claim 20 wherein the weights have different values based on a type of receiver group.

### Claim 36 (depends on 20)

The method of claim 20 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 completion expected catch/no-catch estimation from the classifier model.

### Claim 37 (depends on 20)

The method of claim 20 wherein the player tracking system comprises at least one of a signal-based system and an image-based system.

### Claim 38

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.