PLAYER PERFORMANCE PREDICTION SYSTEM

Abstract

ABSTRACT A player performance prediction system (100), the system (100) comprising: a user interface (102) configured to enable a user to enter a plurality of variables associated with a player; a processor (104) communicatively coupled with the user interface (102), wherein the processor (104) is configured to: receive data from the user interface (102), analyse the data using an artificial intelligence (AI)/ machine learning (ML) module (106), and predict performance of the player for future matches.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of sports analytics and, more specifically, to a player performance prediction system designed to enhance the forecasting of individual player statistics and match outcomes through the integration of artificial intelligence (AI) and machine learning (ML) methodologies.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Traditional sports analysis methods frequently depend on manual data collection and subjective evaluation, leading to pote , Claims:1. A player performance prediction system (100), the system (100) comprising:
a user interface (102) configured to enable a user to enter a plurality of variables associated with a player;
a processor (104) communicatively coupled with the user interface (102), wherein the processor (104) is configured to:
receive data from the user interface (102),
analyse the data using an artificial intelligence (AI)/ machine learning (ML) module (106), and
predict performance of the player for future matches.

2. The system (100) as claimed in claim 1, wherein the plurality of variables includes but not limited to name of the player, period of matches, total matches, total runs made by the player, total ball faced by the player, and strike rate of the player.

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