Systems and methods for sports ability ratings

A non-transitory computer readable medium comprising instructions which, when executed by a computing system, cause the computing system to perform operations comprising:

• generating, via a deterministic algorithm, a first rating for a first entity based on a first plurality of ability scores associated with a received first set of data;
• dynamically adjusting, via the deterministic algorithm, the first plurality of ability scores based on receiving a second set of data associated with the first entity;
• updating, via the deterministic algorithm, the first rating based on the adjusted first plurality of ability scores;
• generating, via the deterministic algorithm, a second rating for a second entity based on a second plurality of ability scores associated with a received third set of data;
• dynamically adjusting, via the deterministic algorithm, the second plurality of ability scores based on receiving a fourth set of data associated with the second entity;
• updating, via the deterministic algorithm, the second rating based on the adjusted second plurality of ability scores;
• learning, via a machine learning model that receives the updated first rating and the updated second rating as inputs, one or more associations between the first rating and the second rating;
• and
• outputting, via the machine learning model, a rating report based on the learned one or more associations between the first rating and the second rating to a GUI of a downstream entity.

A system comprising:

• an application server;
• a pre-processing agent;
• a processor;
• and
• a memory having programming instructions stored thereon, which, when executed by the processor, performs operations comprising:
• generating a first rating for a first entity based on a first plurality of ability scores associated with a received first set of data;
• dynamically adjusting the first plurality of ability scores based on receiving a second set of data associated with the first entity;
• updating the first rating based on the adjusted first plurality of ability scores;
• generating a second rating for a second entity based on a second plurality of ability scores associated with a received third set of data;
• dynamically adjusting the second plurality of ability scores based on receiving a fourth set of data associated with the second entity;
• updating the second rating based on the adjusted second plurality of ability scores;
• learning, via a machine learning model that receives the updated first rating and the updated second rating as inputs, one or more associations between the first rating and the second rating;
• and
• outputting, via the machine learning model, a rating report based on the learned one or more associations between the first rating and the second rating to a GUI of a downstream entity.