Systems and methods for sports ability ratings
A method of generating a dynamic rating for an entity and rearranging an icon associated with the entity on a graphical user interface (GUI) of a computer system. The method includes: receiving a first set of data associated with one or more parameters for the entity; calculating a plurality of ability scores based on the first set of data; generating a first rating based on each ability score of the plurality of ability scores; dynamically adjusting the plurality of ability scores based on receiving a second set of data associated with the one or more parameters for the entity; updating the first rating based on the adjusted plurality of ability scores; automatically rearranging the icon to a position above or below a current position of the icon on the GUI based on the updated first rating.
1 . A method of generating a dynamic rating for an entity and rearranging an icon associated with the entity on a graphical user interface (GUI) of a computer system, the method comprising:
receiving, via one or more data sources, a first set of data associated with one or more parameters for the entity, wherein the entity is a sports team;
calculating, via a deterministic algorithm, a plurality of ability scores based on the first set of data associated with the one or more parameters;
generating, via the deterministic algorithm, a first rating based on each ability score of the plurality of ability scores;
dynamically adjusting, via the deterministic algorithm, the plurality of ability scores based on receiving a second set of data associated with the one or more parameters for the entity;
updating, via the deterministic algorithm, the first rating based on the adjusted plurality of ability scores; and
automatically rearranging the icon to a position above or below a current position of the icon on the GUI based on the updated first rating.
2 . The method of claim 1 , wherein the one or more parameters controls a value in which a single point of data within the first set of data affects the dynamic rating.
3 . The method of claim 1 , wherein the one or more parameters includes a competition-based match importance weighting and/or a game week-based match importance weighting.
4 . The method of claim 1 , wherein the first set of data includes one or more of: a first match report, a first set of player data, a geographical location, and/or a first score report.
5 . The method of claim 1 , wherein the plurality of ability scores includes a first ability score, a second ability score, a third ability score, and a fourth ability score, wherein each of the first ability score, the second ability score, the third ability score, and the fourth ability score are different from one another.
6 . The method of claim 5 , wherein the first ability score is an ability score associated with a country.
7 . The method of claim 5 , wherein the second ability score is an ability score associated with a continent.
8 . The method of claim 5 , wherein the third ability score is an ability score associated with a league associated with the entity.
9 . The method of claim 5 , wherein the fourth ability score is an ability score associated with the entity within a league.
10 . The method of claim 1 , wherein the GUI is represented as a hierarchical rating system in which the icon for the entity represents a sports team.
11 . The method of claim 1 , wherein each of the ability scores of the plurality of ability scores is based on a likelihood of a performance level for one or more of a geographic location, a sporting event location, a sporting team location, or a sports league.
12 . The method of claim 1 , wherein rearranging the icon includes rearranging the icon according to a numeric value of the updated first rating.
13 . A method of generating a dynamic rating for an entity, the method comprising:
receiving, via one or more data sources, a first set of data associated with the entity, wherein the entity is a sports team;
receiving, via the one or more data sources, a second set of data associated with the entity;
automatically calculating one or more rating parameters by inputting the first set of data and the second set of data into a trained machine-learning based model, wherein:
the trained machine-learning based model is trained to learn associations between the first set of data and the second set of data, each of the first set of data and the second set of data associated with (i) one or more scores of a sports match or (ii) one or more game reports of a sports match;
the trained machine-learning model is configured, via the training, to generate, as output, the one or more rating parameters for use in calculating the dynamic rating for the entity, based on the learned associations; and
receiving, via the one or more data sources, a third set of data associated with the entity, wherein the trained machine-learning model generates one or more updated rating parameters based on the third set of data for use in updating the dynamic rating for the entity.
14 . The method of claim 13 , wherein the first set of data includes one or more of: a first match report, a first set of player data, a first geographical location, and/or a first score report.
15 . The method of claim 14 , wherein the second set of data includes one or more of: a second match report, a second set of player data, a second geographical location, and/or a second score report.
16 . The method of claim 13 , wherein the one or more rating parameters includes a competition-based match importance weighting and/or a game week-based match importance weighting.
17 . The method of claim 13 , wherein the updated rating parameters are used as inputs in a deterministic algorithm.
18 . A method of generating a rating report for display on a graphical user interface (GUI) on a downstream entity, the method comprising:
receiving, via one or more data sources, a first set of data associated with one or more parameters for a first entity;
calculating, via a deterministic algorithm, a first plurality of ability scores based on the first set of data associated with the one or more parameters;
generating, via the deterministic algorithm, a first rating for the first entity based on the first plurality of ability scores;
dynamically adjusting, via the deterministic algorithm, the first plurality of ability scores based on receiving a second set of data associated with the one or more parameters for the first entity;
updating, via the deterministic algorithm, the first rating based on the adjusted first plurality of ability scores;
receiving, via the one or more data sources, a third set of data associated with one or more parameters for a second entity;
calculating, via the deterministic algorithm, a second plurality of ability scores based on the third set of data associated with the one or more parameters;
generating, via the deterministic algorithm, a second rating for the second entity based on the second plurality of ability scores;
dynamically adjusting, via the deterministic algorithm, the second plurality of ability scores based on receiving a fourth set of data associated with the one or more parameters for the second entity;
updating, via the deterministic algorithm, the second rating based on the adjusted second plurality of ability scores;
automatically calculating the rating report by inputting the first rating and the second rating into a trained machine-learning based model, wherein:
the trained machine-learning based model is trained to learn associations between the first rating and the second rating, the first rating associated with a rating for a first sports team and the second rating associated with a rating for a second sports team; and
the trained machine-learning model is configured, via the training, to generate, as output, the rating report for use in projecting an outcome between the first sports team and the second sports team, based on the learned associations, wherein the rating report is displayed on the GUI of the downstream entity.
19 . The method of claim 18 , wherein the first set of data includes one or more of: a first match report, a first set of player data, a first geographical location, and/or a first score report, and wherein the second set of data includes one or more of: a second match report, a second set of player data, a second geographical location, and/or a second score report.
20 . The method of claim 18 , wherein the machine-learning model outputs an updated rating report for each instance the first rating and the second rating are updated.