ARTIFICIAL INTELLIGENCE ASSISTED LIVE SPORTS DATA QUALITY ASSURANCE

1 . A computer-implemented method comprising:

receiving, by a computing system, event data corresponding to a game, the event data including one or more events occurring within the game;

utilizing, by the computing system, an artificial intelligence anomaly engine to analyze the event data and flag a potential error in the event data;

generating, by the computing system, a ticket corresponding to the potential error flagged in the event data;

assigning, by the computing system, the ticket to a first quality assurance agent to resolve;

receiving, by the computing system, an indication that the ticket has been reviewed by the first quality assurance agent; and

providing, by the computing system, reviewed event data to an end user based on receiving the indication, wherein the reviewed event data is a version of the event data.

2 . The computer-implemented method of claim 1 , wherein assigning, by the computing system, the ticket to the first quality assurance agent to resolve, further comprises:

identifying, by the computing system, one or more attributes of the ticket, wherein the one or more attributes indicate information associated with the potential error;

generating, by a first artificial intelligence model of the computing system, a first grade for the ticket based on the one or more attributes of the ticket;

generating, by a second artificial intelligence model of the computing system, a second grade for the ticket based on the one or more attributes of the ticket; and

determining, by the computing system, that the first grade associated with the first quality assurance agent exceeds the second grade associated with a second quality assurance agent.

3 . The computer-implemented method of claim 2 , the computer-implemented method further comprising:

determining, by the computing system, that the ticket corresponds to a game that does not have any other tickets pending.

4 . The computer-implemented method of claim 2 , the computer-implemented method further comprising:

generating, by the computing system, the first artificial intelligence model by:

identifying, by the computing system, a plurality of historical tickets resolved by the first quality assurance agent;

for each historical ticket of the plurality of historical tickets, identifying, by the computing system, one or more historical attributes of the ticket and a historical grade assigned to the historical ticket by the first quality assurance agent; and

learning, by the first artificial intelligence model, to grade one or more future tickets by correlating the one or more historical attributes of the plurality of historical tickets with historical grades assigned to the plurality of historical tickets.

5 . The computer-implemented method of claim 4 , the computer-implemented method further comprising:

retraining, by the computing system, the first artificial intelligence model by supplementing the plurality of historical tickets graded by the first quality assurance agent with a plurality of new tickets that were graded by the first artificial intelligence model.

6 . The computer-implemented method of claim 2 , the computer-implemented method further comprising:

generating, by the computing system, the second artificial intelligence model by:

identifying a second plurality of historical tickets resolved by the second quality assurance agent;

for each second historical ticket of the second plurality of historical tickets, identifying second historical attributes of the second historical ticket and a second historical grade assigned to the second historical ticket by the second quality assurance agent; and

learning, by the second artificial intelligence model, to grade one or more future tickets by correlating the second historical attributes of the second plurality of historical tickets with second historical grades assigned to the second plurality of historical tickets.

7 . The computer-implemented method of claim 1 , wherein providing, by the computing system, the reviewed event data to the end user based on receiving the indication, wherein the reviewed event data is the version of the event data comprises:

providing, by the computing system, the reviewed event data to one or more downstream prediction models to generate one or more artificial intelligence insights associated with the game.

8 . The computer-implemented method of claim 1 , the computer-implemented method further comprising:

generating, by the computing system, a dashboard for the first quality assurance agent, wherein the dashboard indicates a number of pending tickets, a number of completed tickets, and one or more metrics associated with the completed tickets.

9 . A non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by one or more processors, causes a computing system to perform operations comprising:

receiving, by the computing system, event data corresponding to a game, the event data including one or more events occurring within the game;

utilizing, by the computing system, an artificial intelligence anomaly engine to analyze the event data and flag a potential error in the event data;

generating, by the computing system, a ticket corresponding to the potential error flagged in the event data;

assigning, by the computing system, the ticket to a first quality assurance agent to resolve;

receiving, by the computing system, an indication that the ticket has been reviewed by the first quality assurance agent; and

providing, by the computing system, reviewed event data to an end user based on receiving the indication, wherein the reviewed event data is a version of the event data.

10 . The non-transitory computer readable medium of claim 9 , wherein assigning, by the computing system, the ticket to the first quality assurance agent to resolve, comprises:

identifying, by the computing system, one or more attributes of the ticket, wherein the one or more attributes indicate information associated with the potential error;

generating, by a first artificial intelligence model of the computing system, a first grade for the ticket based on the one or more attributes of the ticket;

generating, by a second artificial intelligence model of the computing system, a second grade for the ticket based on the one or more attributes of the ticket; and

determining, by the computing system, that the first grade associated with the first quality assurance agent exceeds the second grade associated with a second quality assurance agent.

11 . The non-transitory computer readable medium of claim 10 , further comprising:

determining, by the computing system, that the ticket corresponds to a game that does not have any other tickets pending.

12 . The non-transitory computer readable medium of claim 10 , further comprising:

generating, by the computing system, the first artificial intelligence model by:

identifying, by the computing system, a plurality of historical tickets resolved by the first quality assurance agent;

for each historical ticket of the plurality of historical tickets, identifying, by the computing system, one or more historical attributes of the ticket and a historical grade assigned to the historical ticket by the first quality assurance agent; and

learning, by the first artificial intelligence model, to grade one or more future tickets by correlating the one or more historical attributes of the plurality of historical tickets with historical grades assigned to the plurality of historical tickets.

13 . The non-transitory computer readable medium of claim 12 , further comprising:

retraining, by the computing system, the first artificial intelligence model by supplementing the plurality of historical tickets graded by the first quality assurance agent with a plurality of new tickets that were graded by the first artificial intelligence model.

14 . The non-transitory computer readable medium of claim 10 , further comprising:

generating, by the computing system, the second artificial intelligence model by:

identifying a second plurality of historical tickets resolved by the second quality assurance agent;

for each second historical ticket of the second plurality of historical tickets, identifying second historical attributes of the second historical ticket and a second historical grade assigned to the second historical ticket by the second quality assurance agent; and

learning, by the second artificial intelligence model, to grade one or more future tickets by correlating the second historical attributes of the second plurality of historical tickets with second historical grades assigned to the second plurality of historical tickets.

15 . The non-transitory computer readable medium of claim 10 , wherein providing, by the computing system, the reviewed event data to the end user based on receiving the indication, wherein the reviewed event data is the version of the event data comprises:

providing, by the computing system, the reviewed event data to one or more downstream prediction models to generate one or more artificial intelligence insights associated with the game.

16 . The non-transitory computer readable medium of claim 10 , further comprising:

generating, by the computing system, a dashboard for the first quality assurance agent, wherein the dashboard indicates a number of pending tickets, a number of completed tickets, and one or more metrics associated with the completed tickets.

17 . A computer system, comprising:

a processor; and

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

receiving event data corresponding to a game, the event data including one or more events occurring within the game;

utilizing an artificial intelligence anomaly engine to analyze the event data and flag a potential error in the event data;

generating a ticket corresponding to the potential error flagged in the event data;

assigning the ticket to a first quality assurance agent to resolve;

receiving an indication that the ticket has been reviewed by the first quality assurance agent; and

providing reviewed event data to an end user based on receiving the indication, wherein the reviewed event data is a version of the event data.

18 . The computer system of claim 17 , wherein assigning the ticket to the first quality assurance agent to resolve, further comprises:

identifying one or more attributes of the ticket, wherein the one or more attributes indicate information associated with the potential error;

generating, by a first artificial intelligence model, a first grade for the ticket based on the one or more attributes of the ticket;

generating, by a second artificial intelligence model, a second grade for the ticket based on the one or more attributes of the ticket; and

determining that the first grade associated with the first quality assurance agent exceeds the second grade associated with a second quality assurance agent.

19 . The computer system of claim 18 , wherein the operations further comprise:

determining that the ticket corresponds to a game that does not have any other tickets pending.

20 . The computer system of claim 17 , wherein the operations further comprise:

generating a dashboard for the first quality assurance agent, wherein the dashboard indicates a number of pending tickets, a number of completed tickets, and one or more metrics associated with the completed tickets.