LIVE PREDICTION OF PLAYER PERFORMANCES IN TENNIS

1 . A computer-implemented method comprising:

receiving, by one or more processors, pre-match data for a match between a first player and a second player from a data store;

utilizing, by the one or more processors, one or more prediction models to generate a pre-match prediction based on the pre-match data;

receiving, by the one or more processors, in-match data for the match in progress;

generating, by the one or more processors using the one or more prediction models, a live match prediction based on the in-match data and the pre-match prediction, wherein the live match prediction includes a prediction of what player will win a next point, a final score prediction, a final set score prediction, a final tie break score prediction, a final game score prediction, one or more final player statistics, a predicted serve, a predicted winner location, a predicted winner type, or a rally-count;

utilizing, by the one or more processors, a simulator to generate an additional metric based on the live match prediction, wherein the additional metric includes one or more match events that indicate a change in win-probability greater than a threshold;

identifying, by the one or more processors using the one or more prediction models, at least one player that has a best performance during the one or more match events based on the additional metric; and

outputting, by the one or more processors, a visual representation corresponding to the live match prediction and the at least one player on a display.

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

receiving, by the one or more processors, input from a user device, wherein the input provides a possible score for the match; and

generating, by the one or more processors, one or more updated predictions based on the input from the user device.

3 . The computer-implemented method of claim 1 , wherein the pre-match data comprises player strength information for the first player and the second player, player style information for the first player and the second player, a playing surface type, or one or more weather conditions.

4 . The computer-implemented method of claim 1 , wherein utilizing the simulator to generate the additional metric based on the live match prediction further comprises:

generating, by the one or more processors via the simulator, a clutchness metric based on the live match prediction, wherein the clutchness metric is based on one or more points of the match that yield a result greater than the threshold.

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

generating, by the one or more processors, a plus-minus metric for the first player and the second player based on the live match prediction, wherein the plus-minus metric represents a difference between an expected point amount the first player is expected to win and the second player is expected to win and a point amount the first player won and how many points the second player did win.

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

tracking, by the one or more processors, via at least one calibrated camera and at least one tag, the pre-match data corresponding to the match between the first player and the second player;

and storing, by the one or more processors, the pre-match data in the data store.

7 . The computer-implemented method of claim 6 , wherein the at least one tag includes a tag worn by the first player or the second player or wherein the at least one tag includes an embedded tag in at least one object.

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

receiving, by the processor, pre-match data for a match between a first player and a second player from a data store;

utilizing, by the processor, one or more prediction models to generate a pre-match prediction based on the pre-match data;

receiving, by the processor, in-match data for the match in progress;

generating, by the processor using the one or more prediction models, a live match prediction based on the in-match data and the pre-match prediction, wherein the live match prediction includes a prediction of what player will win a next point, a final score prediction, a final set score prediction, a final tie break score prediction, a final game score prediction, one or more final player statistics, a predicted serve, a predicted winner location, a predicted winner type, or a rally-count;

utilizing, by the processor, a simulator to generate an additional metric based on the live match prediction, wherein the additional metric includes one or more match events that indicate a change in win-probability greater than a threshold;

identifying, by the processor using the one or more prediction models, at least one player that has a best performance during the one or more match events based on the additional metric; and

outputting, by the processor, a visual representation corresponding to the live match prediction and the at least one player on a display.

9 . The non-transitory computer readable medium of claim 8 , further comprising:

receiving, by the processor, input from a user device, wherein the input provides a possible score for the match; and

generating, by the processor, one or more updated predictions based on the input from the user device.

10 . The non-transitory computer readable medium of claim 8 , wherein the pre-match data comprises player strength information for the first player and the second player, player style information for the first player and the second player, a playing surface type, or one or more weather conditions.

11 . The non-transitory computer readable medium of claim 8 , wherein utilizing the simulator to generate the additional metric based on the live match prediction further comprises:

generating, by the processor via the simulator, a clutchness metric based on the live match prediction, wherein the clutchness metric is based on one or more points of the match that yield a result greater than the threshold.

12 . The non-transitory computer readable medium of claim 8 , further comprising:

generating, by the processor, a plus-minus metric for the first player and the second player based on the live match prediction, wherein the plus-minus metric represents a difference between an expected point amount the first player is expected to win and the second player is expected to win and a point amount the first player won and how many points the second player did win.

13 . The non-transitory computer readable medium of claim 8 , further comprising tracking, by the processor, via at least one calibrated camera and at least one tag, the pre-match data corresponding to the match between the first player and the second player; and

storing, by the processor, the pre-match data in the data store.

14 . The non-transitory computer readable medium of claim 13 , wherein the at least one tag includes a tag worn by the first player or the second player or wherein the at least one tag includes an embedded tag in at least one object.

15 . A computer system comprising:

a processor; and

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

receiving pre-match data for a match between a first player and a second player from a data store;

utilizing one or more prediction models to generate a pre-match prediction based on the pre-match data;

receiving in-match data for the match in progress;

generating, using the one or more prediction models, a live match prediction based on the in-match data and the pre-match prediction, wherein the live match prediction includes a prediction of what player will win a next point, a final score prediction, a final set score prediction, a final tie break score prediction, a final game score prediction, one or more final player statistics, a predicted serve, a predicted winner location, a predicted winner type, or a rally-count;

utilizing a simulator to generate an additional metric based on the live match prediction, wherein the additional metric includes one or more match events that indicate a change in win-probability greater than a threshold;

identifying, using the one or more prediction models, at least one player that has a best performance during the one or more match events based on the additional metric; and

outputting a visual representation corresponding to the live match prediction and the at least one player on a display.

16 . The computer system of claim 15 , further comprising:

receiving input from a user device, wherein the input provides a possible score for the match; and

generating one or more updated predictions based on the input from the user device.

17 . The computer system of claim 15 , wherein the pre-match data comprises player strength information for the first player and the second player, player style information for the first player and the second player, a playing surface type, or one or more weather conditions.

18 . The computer system of claim 15 , wherein utilizing the simulator to generate the additional metric based on the live match prediction further comprises:

generating, via the simulator, a clutchness metric based on the live match prediction, wherein the clutchness metric is based on one or more points of the match that yield a result greater than the threshold.

19 . The computer system of claim 15 , further comprising:

generating a plus-minus metric for the first player and the second player based on the live match prediction, wherein the plus-minus metric represents a difference between an expected point amount the first player is expected to win and the second player is expected to win and a point amount the first player won and how many points the second player did win.

20 . The computer system of claim 15 , further comprising

tracking, via at least one calibrated camera and at least one tag, the pre-match data corresponding to the match between the first player and the second player; and

storing the pre-match data in the data store.