MACHINE LEARNING TECHNIQUES FOR PREDICTION OF ONE-ON-ONE PASS RUSH AND PROTECTION

1 . A method for using machine learning to predict a success of a matchup in a sporting event, the method comprising:

accessing tracking data from a data store;

identifying, from the tracking data, one or more matchups wherein each matchup comprises an identification of a first player, an identification of a second player, and a success of a corresponding outcome;

filtering the identified matchups to create a subset of matchups;

providing the subset of matchups to a trained machine learning model;

receiving, from the machine learning model, a prediction of success of the matchup;

comparing the prediction of success with a measured outcome; and

adjusting a ranking of the first player, a ranking of the second player, and/or the machine learning model based on the prediction.

2 . The method of claim 1 , wherein filtering the identified matchups is based on one or more criteria associated with the identified matchups.

3 . The method of claim 2 , wherein the one or more criteria is associated with a client device.

4 . The method of claim 2 , wherein the trained machine learning model discards tracking data not associated with the identified matchups.

5 . The method of claim 1 , wherein the ranking of the first player includes a first Elo rating and the second ranking of the second player includes a second Elo rating.

6 . The method of claim 5 , wherein a win quality rating is generating based on a difference between the first Elo rating of the first player and the second Elo rating of the second player.

7 . The method of claim 1 , wherein the adjusted ranking of the first player is used in generation of a prediction of performance on a destination team.

8 . A non-transitory computer readable medium having a sequence of instructions, which, when executed by a processor, causes a computing system to perform operations comprising:

accessing, by the computing system, tracking data from a data store;

identifying, via the computing system from the tracking data, one or more matchups wherein each matchup comprises an identification of a first player, an identification of a second player, and a success of a corresponding outcome;

filtering, by the computing system, the identified matchups to create a subset of matchups;

providing, by the computing system, the subset of matchups to a trained machine learning model;

receiving, by the computing system from the machine learning model, a prediction of success of the matchup;

comparing, by the computing system, the prediction of success with a measured outcome; and

adjusting, by the computing system, a ranking of the first player, a ranking of the second player, and/or the machine learning model based on the prediction.

9 . The non-transitory computer readable medium of claim 8 , wherein filtering the identified matchups is based on one or more criteria associated with the identified matchups.

10 . The non-transitory computer readable medium of claim 9 , wherein the one or more criteria is associated with a client device.

11 . The non-transitory computer readable medium of claim 9 , wherein the trained machine learning model discards tracking data not associated with the identified matchups.

12 . The non-transitory computer readable medium of claim 8 , wherein the ranking of the first player includes a first Elo rating and the second ranking of the second player includes a second Elo rating.

13 . The non-transitory computer readable medium of claim 12 , wherein a win quality rating is generating based on a difference between the first Elo rating of the first player and the second Elo rating of the second player.

14 . The non-transitory computer readable medium of claim 8 , wherein the adjusted ranking of the first player is used in generation of a prediction of performance on a destination team.

15 . A computing system comprising:

a processor implemented in hardware; and

a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations comprising:

accessing tracking data from a data store;

identifying, from the tracking data, one or more matchups wherein each matchup comprises an identification of a first player, an identification of a second player, and a success of a corresponding outcome;

filtering the identified matchups to create a subset of matchups;

providing the subset of matchups to a trained machine learning model;

receiving, from the machine learning model, a prediction of success of the matchup;

comparing the prediction of success with a measured outcome; and

adjusting a ranking of the first player, a ranking of the second player, and/or the machine learning model based on the prediction.

16 . The system of claim 15 , wherein filtering the identified matchups is based on one or more criteria associated with the identified matchups.

17 . The system of claim 16 , wherein the one or more criteria is associated with a client device.

18 . The system of claim 16 , wherein the trained machine learning model discards tracking data not associated with the identified matchups.

19 . The system of claim 15 , wherein the ranking of the first player includes a first Elo rating and the second ranking of the second player includes a second Elo rating.

20 . The system of claim 19 , wherein a win quality rating is generating based on a difference between the first Elo rating of the first player and the second Elo rating of the second player.