Method and system for artificial intelligence club fitting

1. A method for fitting a golf club to a golfer, the method comprising:

inputting a plurality of club properties into a device, the device in communication with a SQL server comprising an artificial intelligence (Al) engine;

inputting shot data for a player's swing into the server;

sending a fitting ID and a shot ID as inputs to a python script stored on the server;

identifying active club type and swings that strike a golf ball from the data on the server;

loading a corresponding machine learning model for each club type selected from drivers, irons, fairways,

and hybrids, wherein each model is trained on active products only to avoid possibility of recommending out-of-stock products;

inputting club IDs and average swing specs to the model;

inputting club data comprising at least one of head, loft, shaft flex, shaft model, average head speed, average attack angle, or average path angle

outputting fitting data comprising at least one of head, loft, shaft flex, shaft model, shaft weight, or a probability of optimal fit; and

processing loops until the Al engine repeats a club recommendation.

2. A method for fitting a golf club to a golfer, the method comprising:

inputting a plurality of club properties into a device, the device in communication with a SQL server comprising an artificial intelligence (Al) engine;

inputting shot data for a player's swing into the server;

sending a fitting ID and a shot ID as inputs to a python script stored on the server

identifying active club type and swings that strike a golf ball from the data on the server;

loading a corresponding machine learning model for each club type selected from drivers, irons, fairways, and hybrids;

inputting club IDs and average swing specs to the model;

inputting club data comprising at least one of head, loft, shaft flex, shaft model, a verage head speed, average attack angle, or average path angle

outputting fitting data comprising at least one of head, loft, shaft flex, shaft model, shaft weight, or a probability of optimal fit; and

processing loops until the Al engine repeats a club recommendation;

wherein the each model has been trained on past swing data and the resulting recommendations made by a plurality of fitters.

3. A method for fitting a golf club to a golfer, the method comprising:

inputting a plurality of club properties into a device, the device in communication with a SQL server comprising an artificial intelligence (Al) engine;

inputting shot data for a player's swing into the server;

sending a fitting ID and a shot ID as inputs to a python script stored on the server;

identifying active club type and swings that strike a golf ball from the data on the server;

loading a corresponding machine learning model for each club type selected from drivers, irons, fairways, and hybrids;

inputting club IDs and average swing specs to the model;

inputting club data comprising at least one of head, loft, shaft flex, shaft model, a verage head speed, average attack angle, or average path angle

outputting fitting data comprising at least one of head, loft, shaft flex, shaft model, shaft weight, or a probability of optimal fit; and

processing loops until the AI engine repeats a club recommendation; wherein a plurality of components are compatible.

4. A method for fitting a golf club to a golfer, the method comprising:

inputting a plurality of club properties into a device, the device in communication with a SQL server comprising an artificial intelligence (Al) engine;

inputting shot data for a player's swing into the server;

sending a fitting ID and a shot ID as inputs to a python script stored on the server;

identifying active club type and swings that strike a golf ball from the data on the server;

loading a corresponding machine learning model for each club type selected from drivers, irons, fairways, and hybrids;

inputting club IDs and average swing specs to the model;

inputting club data comprising at least one of head, loft, shaft flex, shaft model, a verage head speed, average attack angle, or average path angle

outputting fitting data comprising at least one of head, loft, shaft flex, shaft model, shaft weight, or a probability of optimal fit; and

processing loops until the Al engine repeats a club recommendation; wherein the top three club options are presented in order of total probability.

5. A non-transitory computer readable medium storing instructions that optimize the fitting of a golf club to a golfer, when executed by a processor, cause the processor to:

inputting a plurality of club properties into a device, the device in communication with a SQL server comprising an artificial intelligence (Al) engine;

inputting shot data for a player into the server;

sending a fitting ID and a shot ID as inputs to a python script stored on the server;

identifying active club type and swings that strike a golf ball from the data on the server;

loading a corresponding machine learning model for each club type selected from drivers, irons, fairways,

and hybrids, wherein each model is trained on active products only to avoid possibility of recommending out-of-stock products;

inputting club IDs and average swing specs to the model;

inputting club data comprising at least one of head, loft, shaft flex, shaft model, average head speed, average attack angle, or average path angle

outputting fitting data comprising at least one of head, loft, shaft flex, shaft model, shaft weight, or a probability of optimal fit; and

processing loops until the Al engine repeats a club recommendation.

6. The non-transitory computer readable medium according to claim 5 wherein the models have been trained on past swing data and the resulting recommendations made by a plurality of fitters.

7. The non-transitory computer readable medium according to claim 5 wherein a plurality of components are compatible.

8. The non-transitory computer readable medium according to claim 5 wherein the top three club options are presented in order of total probability.