Sports operating system

1. A method for evaluating player metrics comprising, by one or more computing devices of a sports operating system:

accessing, by the one or more computing devices of the sports operating system, user sensor data from one or more wearable sensors on one or more players and optical sensor data from one or more cameras, wherein the user sensor data comprises location data of the player and acceleration data, and wherein the optical sensor data comprises a plurality of frames portraying the one or more players and a plurality of scenes from an athletic event;

analyzing, by a machine-learning model of the sports operating system, the optical sensor data to identify the one or more players associated with the one or more wearable sensors and one or more actions during the athletic event;

synchronizing, by the one or more computing devices of the sports operating system, the optical sensor data of the identified one or more players with user sensor data of the respective one or more wearable sensors on the identified one or more players using the analysis of the optical sensor data to identify the one or more players associated with the one or more wearable sensors and one or more actions during the athletic event;

calculating, by the one or more computing devices of the sports operating system, one or more player metrics for the identified one or more players based on the synchronized user sensor data and the identified actions captured within the synchronized optical sensor data, wherein the one or more player metrics are based on a role associated with the identified one or more players;

normalizing, using one or more benchmark algorithms of the sports operating system, the one or more player metrics for the one or more players based on one or more weighted parameters and one or more other player metrics corresponding to the one or more players having the same role associated with the identified one or more players;

predicting, using one or more performance evaluation algorithms of the sports operating system, one or more future outcome and one or more performance levels for the one or more players based on their associated roles; and

providing, by the one or more computing devices of the sports operating system, a report to one or more users about the one or more normalized player metrics and the one or more future outcomes and the one or more performance levels for the one or more players.

2. The method of claim 1 , further comprising:

receiving feedback related to the one or more players or the one or more actions; and

updating the machine-learning model based on the feedback corresponding to the one or more players or the one or more actions.

3. The method of claim 1 , wherein the wearable sensors are configured as one or more of a wearable buckle, a waist band clip, a wearable boot, a boxing glove style sensor, or a body patch.

4. The method of claim 1 , wherein the user sensor data and the optical sensor data is accessed in real-time as the athletic event occurs, and wherein the wearable sensors comprise cellular antennas.

5. The method of claim 1 , further comprising:

determining whether the one or more users have permission from the first player to access the report, wherein the report is provided to the one or more users in response to determining the one or more users have permission from the one or more players to access the report.

6. The method of claim 1 , further comprising:

accessing one or more player goals for the one or more players; and

tracking the one or more player goals for the one or more players based on the one or more normalized player metrics, wherein the report indicates a progress on completing the one or more player goals for the one or more players.

7. The method of claim 1 , further comprising:

accessing one or more third-party data sources, wherein the calculation of the one or more player metrics is further based on the one or more third-party data sources.

8. The method of claim 1 , further comprising:

accessing data indicative of one or more prior athletic events, wherein the calculation of the one or more player metrics is further based on the data indicative of the one or more prior athletic events, and wherein the prediction of the one or more future outcomes is further based on the data indicative of the one or more prior athletic events.

9. The method of claim 1 , further comprising:

accessing data indicative of one or more behavioral actions of the identified one or more players, wherein the calculation of the one or more player metrics is further based on the data indicative of the one or more behavioral actions, and wherein the prediction of the one or more future outcomes is further based on the data indicative of the one or more behavioral actions of the identified one or more players.

10. The method of claim 9 , wherein the data indicative of the one or more behavioral actions comprises one or more of financial data, sleep data, or nutrition data.

11. The method of claim 1 , wherein the report to the one or more users further includes one or more training strategies for the identified one or more players, wherein the one or more training strategies are for one or more of a player development, a player valuation, or a health safety of the identified one or more players.

12. The method of claim 1 , wherein the athletic event is associated with a first sport of a plurality of sports, wherein the normalization of the one or more player metrics is based on the first sport of the plurality of sports.

13. The method of claim 1 , further comprising:

generating a valuation of the identified one or more players based on the one or more normalized player metrics, wherein the report comprises the valuation.

14. A sports operating system for evaluating player metrics comprising:

one or more processors; and

one or more computer-readable non-transitory storage media coupled to one or more of the processors and comprising instructions operable when executed by one or more of the processors to cause the system to:

access user sensor data from one or more wearable sensors on one or more players and optical sensor data from one or more cameras, wherein the user sensor data comprises location data of the player and acceleration data, and wherein the optical sensor data comprises a plurality of frames portraying the one or more players and a plurality of scenes from an athletic event;

analyze, by a machine-learning model of the sports operating system, the optical sensor data to identify the one or more players associated with the one or more wearable sensors and one or more actions during the athletic event;

synchronize the optical sensor data of the identified one or more players with user sensor data of the respective one or more wearable sensors on the identified one or more players using the analysis of the optical sensor data to identify the one or more players associated with the one or more wearable sensors and one or more actions during the athletic event;

calculate one or more player metrics for the identified one or more players based on the synchronized user sensor data and the identified actions captured within the synchronized optical sensor data, wherein the one or more player metrics are based on a role associated with the identified one or more players;

normalize, using one or more benchmark algorithms of the sports operating system, the one or more player metrics for the one or more players based on one or more weighted parameters and one or more other player metrics corresponding to the one or more players having the same role associated with the identified one or more players;

predict, using one or more performance evaluation algorithms of the sports operating system, one or more future outcome and one or more performance levels for the one or more players based on their associated roles; and

provide a report to one or more users about the one or more normalized player metrics and the one or more future outcomes and the one or more performance levels for the one or more players.

15. The sports operating system of claim 14 , wherein the instructions are further operable when executed by one or more of the processors to cause the sports operating system to:

receive feedback related to the one or more players or the one or more actions; and

update the machine-learning model based on the feedback corresponding to the one or more players or the one or more actions.

16. The sports operating system of claim 14 , wherein the wearable sensors are configured as one or more of a wearable buckle, a waist band clip, a wearable boot, a boxing glove style sensor, or a body patch.

17. The sports operating system of claim 14 , wherein the user sensor data and the optical sensor data is accessed in real-time as the athletic event occurs, and wherein the wearable sensors comprise cellular antennas.

18. The sports operating system of claim 14 , wherein the instructions are further operable when executed by one or more of the processors to cause the system to:

determine whether the one or more users have permission from the first player to access the report, wherein the report is provided to the one or more users in response to determining the one or more users have permission from the one or more players to access the report.

19. The sports operating system of claim 14 , wherein the instructions are further operable when executed by one or more of the processors to cause the system to:

access one or more player goals for the one or more players; and

track the one or more player goals for the one or more players based on the one or more normalized player metrics, wherein the report indicates a progress on completing the one or more player goals for the one or more players.

20. One or more computer-readable non-transitory storage media embodying software for evaluating player metrics that is operable when executed by one or more processors of a sports operating system to:

access user sensor data from one or more wearable sensors on one or more players and optical sensor data from one or more cameras, wherein the user sensor data comprises location data of the player and acceleration data, and wherein the optical sensor data comprises a plurality of frames portraying the one or more players and a plurality of scenes from an athletic event;

analyze, by a machine-learning model of the sports operating system, the optical sensor data to identify the one or more players associated with the one or more wearable sensors and one or more actions during the athletic event;

synchronize the optical sensor data of the identified one or more players with user sensor data of the respective one or more wearable sensors on the identified one or more players using the analysis of the optical sensor data to identify the one or more players associated with the one or more wearable sensors and one or more actions during the athletic event;

calculate one or more player metrics for the identified one or more players based on the synchronized user sensor data and the identified actions captured within the synchronized optical sensor data, wherein the one or more player metrics are based on a role associated with the identified one or more players;

normalize, using one or more benchmark algorithms of the sports operating system, the one or more player metrics for the one or more players based on one or more weighted parameters and one or more other player metrics corresponding to the one or more players having the same role associated with the identified one or more players;

predict, using one or more performance evaluation algorithms of the sports operating system, one or more future outcome and one or more performance levels for the one or more players based on their associated roles; and

provide a report to one or more users about the one or more normalized player metrics and the one or more future outcomes and the one or more performance levels for the one or more players.