System and method for athletic ability rating

Abstract

A method of generating a dynamic rating for an entity and rearranging icons associated with the entity on a graphical user interface (GUI) of a computer system. The method comprises: receiving a first set of data associated with one or more parameters of the entity; calculating a plurality of capability scores based on the first set of data; generating a first rating based on each capability score of the plurality of capability scores; dynamically adjusting the plurality of capability scores based on receiving a second set of data associated with the one or more parameters of the entity; updating the first rating based on the adjusted plurality of capability scores; the icons are automatically rearranged to a position above or below a current position of the icons on the GUI based on the updated first rating.

Description

Cross-reference to related applications

[0001] This application claims priority to U.S. Patent Application No. 63 / 595,036, filed November 1, 2023, pursuant to 35 USC §119, the entire disclosure of which is incorporated herein by reference. Technical Field

[0002] Various embodiments of the present invention generally relate to methods and systems for rating the capabilities of sports teams. Background Technology

[0003] Assigning ability measures to sports teams, such as football teams worldwide, is a key element in achieving accurate predictions for the sport. Understanding the capabilities of participating teams is helpful, from predicting the probability of winning a match to forecasting a player's expected number of shots on goal. To leverage these models on a large scale within the sport's evolving landscape, it would be beneficial to implement these team ability ratings extensively in both men's and women's competitions.

[0004] Conducting ratings on a large scale can be challenging. For example, most football matches are played between teams within the same domestic league. This makes judging the quality of teams within a league relatively straightforward. However, due to the lack of matches outside the domestic league, comparing the quality of teams from different leagues, countries, and especially continents can be more challenging. This problem is even more pronounced in women's football, where the gap in team ability is greater, and there are even fewer matches between teams from different leagues, countries, and continents.

[0005] The present invention aims to overcome one or more of these aforementioned challenges. The background description provided herein is intended to generally present the background of the invention. Summary of the Invention

[0006] In some embodiments, this document discloses a method. This method generates dynamic ratings for entities and rearranges icons associated with the entities on a graphical user interface (GUI) of a computer system. The computer system receives a first set of data associated with one or more parameters of the entity via one or more data sources. The computer system calculates multiple capability scores based on the first set of data associated with the one or more parameters using a deterministic algorithm. The computer system generates a first rating based on each of the multiple capability scores using a deterministic algorithm. The computer system dynamically adjusts the multiple capability scores based on a second set of data received associated with one or more parameters of the entity using a deterministic algorithm. The computer system updates the first rating based on the adjusted multiple capability scores using a deterministic algorithm. Based on the updated first rating, the computer system automatically rearranges the icons to positions above or below their current positions on the GUI.

[0007] In some embodiments, this document discloses a method for generating dynamic ratings for entities. A computer system receives a first set of data associated with an entity via one or more data sources. The computer system receives a second set of data associated with an entity via one or more data sources. The computer system automatically computes one or more rating parameters by inputting the first and second sets of data into a trained machine learning-based model, wherein: the trained machine learning-based model is trained to learn associations between the first and second sets of data, each of the first and second sets of data being associated with (i) one or more scores of a sports match, or (ii) one or more match reports of a sports match; the trained machine learning model is configured to generate one or more rating parameters as output based on the learned associations for computing the dynamic rating of the entity. The computer system receives a third set of data associated with an entity via one or more data sources, wherein the trained machine learning model generates one or more updated rating parameters based on the third set of data for updating the dynamic rating of the entity.

[0008] In some embodiments, this document discloses a method. The method generates a rating report to be displayed on a graphical user interface (GUI) of a downstream entity. A computer system receives a first set of data associated with one or more parameters of a first entity via one or more data sources. The computer system calculates a first plurality of capability scores based on the first set of data associated with the one or more parameters using a deterministic algorithm. The computer system generates a first rating for the first entity based on the first plurality of capability scores using a deterministic algorithm. The computer system dynamically adjusts the first plurality of capability scores based on a second set of data associated with one or more parameters of the first entity received via a deterministic algorithm. The computer system updates the first rating based on the adjusted first plurality of capability scores using a deterministic algorithm. The computer system receives a third set of data associated with one or more parameters of a second entity via one or more data sources. The computer system calculates a second plurality of capability scores based on the third set of data associated with one or more parameters using a deterministic algorithm. The computer system generates a second rating for the second entity based on the second plurality of capability scores using a deterministic algorithm. The computer system dynamically adjusts the second plurality of capability scores based on a fourth set of data associated with one or more parameters of the second entity received via a deterministic algorithm. The computer system updates the second rating based on the adjusted second plurality of capability scores using a deterministic algorithm. The computer system automatically calculates rating reports by inputting a first rating and a second rating into a trained machine learning-based model, wherein: the trained machine learning-based model is trained to learn the association between the first rating and the second rating, the first rating being associated with the rating of the first sports team, and the second rating being associated with the rating of the second sports team; and the trained machine learning model is configured to generate rating reports as output based on the learned associations, for use in predicting outcomes between the first and second sports teams, wherein the rating reports are displayed on the GUI of a downstream entity. Attached Figure Description

[0009] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various exemplary embodiments and, together with the description, serve to explain the principles of the disclosed aspects.

[0010] Figure 1 An exemplary environment is described that uses a machine learning model to generate dynamic ratings based on one aspect of the disclosed topic.

[0011] Figure 2 An exemplary flowchart for generating dynamic ratings based on one aspect of the disclosed topic is depicted.

[0012] Figure 3 Another exemplary flowchart is depicted for generating updated dynamic ratings based on one aspect of the disclosed topic.

[0013] Figure 4 Another exemplary flowchart for generating a rating report based on one aspect of the disclosed topic is depicted.

[0014] Figure 5 An exemplary graphical user interface for displaying a hierarchical rating system is depicted, based on one aspect of the disclosed subject matter.

[0015] Figure 6 A simplified calculation for dynamic rating is described based on one aspect of the disclosed topic.

[0016] Figure 7 An exemplary graphical user interface for displaying a hierarchical rating system is depicted.

[0017] Figure 8 A flowchart for training a machine learning model is depicted based on one aspect of the disclosed topic.

[0018] Figure 9 An example computing device according to one or more embodiments is described.

[0019] It is worth noting that, for the sake of simplicity and clarity, certain aspects of the accompanying drawings depict general configurations of various embodiments. Descriptions and details of known features and techniques may be omitted to avoid unnecessarily obscuring other features. Elements in the figures are not necessarily drawn to scale; the dimensions of some features may be enlarged relative to other features to improve understanding of the exemplary embodiments. Detailed Implementation

[0020] The general discussion of the invention provides a brief, general description of suitable computing environments in which the invention may be practiced. In one embodiment, any of the disclosed systems, methods, and / or graphical user interfaces can be executed or implemented by a computing system consistent with or similar to that described and / or explained in this invention. Although not essential, aspects of the invention are described in the context of computer-executable instructions, such as routines executed by data processing apparatuses, for example, server computers, wireless devices, and / or personal computers. Those skilled in the art will understand that aspects of the invention can be practiced with other communication, data processing, or computer system configurations, including: internet devices, handheld devices (including personal digital assistants (“PDAs”), wearable computers, various cellular or mobile phones (including VoIP), dumb terminals, media players, gaming devices, virtual reality devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, set-top boxes, network personal computers, minicomputers, mainframe computers, etc. In practice, the terms “computer,” “server,” etc., are generally used interchangeably herein and refer to any of the foregoing apparatuses and systems, as well as any data processor.

[0021] Various aspects of the invention can be embodied in a dedicated computer and / or data processor specifically programmed, configured, and / or constructed to execute one or more of the computer-executable instructions explained in detail herein. While various aspects of the invention, such as certain functions, are described as performing exclusively on a single device, the invention can also be practiced in a distributed environment, where functions or modules are shared among different processing devices linked via communication networks, such as local area networks (“LANs”), wide area networks (“WANs”), and / or the Internet. Similarly, techniques presented herein relating to multiple devices can be implemented in a single device. In a distributed computing environment, program modules can reside in local and / or remote memory storage devices.

[0022] Various aspects of the present invention can be stored and / or distributed on non-transitory computer-readable media, including magnetic or optically readable computer disks, hardwired or pre-programmed chips (e.g., EEPROM semiconductor chips), nanotechnology memories, biological memories, or other data storage media. Alternatively, computer-implemented instructions, data structures, screen displays, and other data according to various aspects of the present invention can be distributed over a period of time via the Internet and / or other networks (including wireless networks) on a propagation medium (e.g., electromagnetic waves, sound waves, etc.) and / or they can be made available on any analog or digital network (packet switching, circuit switching, or other schemes).

[0023] The programmatic aspect of this technology can be considered a "product" or "manufactured item," typically carried on a machine-readable medium or in the form of embodied executable code and / or associated data. "Storage" type media includes computers, processors, etc., or their associated modules, such as any or all of various semiconductor memories, tape drives, disk drives, etc., which can provide non-transitory storage for software programming at any time. Sometimes, all or part of the software can communicate via the Internet or various other telecommunications networks. For example, such communication enables the loading of software from one computer or processor to another, such as from a management server or host of a mobile communication network to a server's computer platform and / or from a server to a mobile device. Therefore, another type of medium that can carry software elements includes light waves, radio waves, and electromagnetic waves, such as physical interfaces between local devices, used via wired and fiber optic networks, and via various air links. Physical elements carrying such waves, such as wired or wireless links, fiber optic links, etc., can also be considered as media carrying software. As used herein, unless limited to non-transitory, tangible "storage" media, the term "readable medium" for a computer or machine refers to any medium that participates in providing instructions to a processor for execution.

[0024] The terminology used above may be interpreted in the broadest and most reasonable manner, even when used in conjunction with a detailed description of certain specific examples of the invention. In fact, the foregoing may even emphasize certain terms; however, any term intended to be interpreted in any limiting manner will be disclosed and specifically defined in the Detailed Description section. The foregoing general and detailed descriptions are exemplary and explanatory only and are not intended to limit the features.

[0025] As used herein, the terms “comprising,” “including,” “having,” “including,” or other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements may include not only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0026] In this invention, relative terms, such as, for example, “approximately,” “substantially,” “roughly,” and “approximately,” are used to indicate possible variations of ±10% in the value.

[0027] The term “exemplary” is used in the sense of “example” rather than “model.” As used herein, unless the context otherwise specifies, the singular forms “a,” “an,” and “the” include plural referents.

[0028] Other embodiments of the invention will be apparent to those skilled in the art upon consideration of this specification and the practice of the disclosed invention. This specification and examples should be considered as exemplary only.

[0029] Various embodiments of the present invention generally relate to rating the abilities of players and teams in sports. As discussed above, existing systems have shortcomings. For example, none of the existing methods cover the application of rating systems in men's and / or women's football on a scale similar to that described herein. In particular, in the context of football, the rating system disclosed herein can be implemented in men's football (covering over 13,000 teams in 183 countries and 413 leagues) and women's football (covering over 2,000 teams in 68 countries and 140 leagues) worldwide.

[0030] Although this article uses football as an example, the techniques described are equally applicable to other team sports such as American football, basketball, cricket, rugby, and baseball.

[0031] The disclosed technology may employ algorithms and / or machine learning methods.

[0032] The technical advantages of the disclosed technology include improved sports match rating reports. For example, some aspects involve determining representative parameters and ratings from numerous data sources to improve the performance, accuracy, and results of the information to be mapped to match graphs. In doing so, the disclosed technology provides improvements over existing solutions. The technology disclosed herein improves sports match rating reports by, for example, generating and dynamically adjusting and refining team ratings based on one or more specified parameters in player performance data, match reports, and / or a hierarchical structure. By employing a process of dynamically adjusting and refining team ratings within a hierarchical structure, the range and number of teams that can be rated, as well as the data considered in generating and adjusting team ratings, are significantly increased, resulting in more accurate sports match rating reports and a more accurate ability to predict match outcomes. Therefore, the technology disclosed herein applies a technical solution for generating team ratings using hierarchical structures in an efficient and scalable manner, which would not be possible without such a technical solution.

[0033] As used herein, a "machine learning model" typically comprises instructions, data, and / or a model configured to receive input and apply one or more of weights, biases, classifications, or analyses to the input to generate an output. For example, the output may include a classification of the input, an input-based analysis, a design, process, prediction, or recommendation associated with the input, or any other suitable type of output. Machine learning models are typically trained using training data, such as empirical data and / or samples of input data, which are fed into the model to build, tune, or modify one or more aspects of the model, such as weights, biases, criteria used to form classifications or clusters, etc. The various aspects of a machine learning model can operate on the input linearly and in parallel via a network (e.g., a neural network) or via any suitable configuration.

[0034] The execution of a machine learning model can include deploying one or more machine learning techniques, such as generative learning, linear regression, logistic regression, random forests, gradient boosting machines (GBM), deep learning, graph neural networks (GNNs), and / or deep neural networks. Supervised and / or unsupervised training can be employed. For example, supervised learning can include providing training data and labels corresponding to the training data, such as ground truth. Unsupervised methods may include clustering, classification, etc. K-means clustering or K-nearest neighbors can also be used, and these can be supervised or unsupervised. A combination of K-nearest neighbors and unsupervised clustering techniques can also be used. Any suitable type of training can be used, such as stochastic, gradient boosting, random seeding, recursive, epoch-based, or batch-based training.

[0035] While several examples in this document relate to certain types of machine learning, it should be understood that the techniques according to the invention are adaptable to any suitable type of machine learning. It should also be understood that the examples above are merely illustrative. The techniques and methods of the invention may be suitable for any suitable activity.

[0036] Compared with existing technologies, the technology disclosed herein has advantages such as (1) being able to analyze as many sports teams as possible; and (2) being able to provide ratings for both men's and women's sports teams.

[0037] The disclosed system relies on a team ability rating system that is flexible enough to provide ratings for teams in any sport and to allow for comparisons between teams from any league in the world within the same sport. For example, teams in men's football (covering over 13,000 teams in 183 countries and 413 leagues) and / or women's football (covering over 2,000 teams in 68 countries and 140 leagues) can be rated, and although the rating systems for both sports are separate, the same or similar algorithms can be used.

[0038] To cover as many teams as possible, the ability rating system is built using final match score data. Many leagues lack detailed in-game information; therefore, the rating system may not be able to rely on the necessary detailed in-game match data to achieve the required coverage.

[0039] A rating system can ensure that the system is built in a way that doesn't require changing the framework for men's and women's football. Men's team ratings may not be compared with women's team ratings because the systems can be separate. However, rating systems can use similar methodologies and / or can learn from each other.

[0040] The publicly available system can be quickly adapted to all teams worldwide. The rating system enables rapid transmission of rating changes between leagues and across continents. Some leagues have very little communication with other leagues, countries, and continents, and therefore require a system that can use fair and easy-to-understand comparisons of sports teams globally. Furthermore, when a team is promoted or relegated between leagues, the rating system can adjust its rating for the new season accordingly.

[0041] As an example, the Elo rating system can be used. The Elo rating could be, for example, a rating system used in sports such as chess. After each match, the system updates the ability rating by comparing the expected result based on each player's / team's pre-match rating with the actual result of the match. If a player / team performs worse than expected, the player / team loses a rating (e.g., associated with a relatively low rating).

[0042] The stronger a team is, the more likely it is to lose its rating due to a poor result, because its pre-match expectations of winning are usually very high. Elo can be a zero-sum system. In other words, for every team that gains or loses an Elo rating during a match, the other team may experience the opposite change. For example, if one team loses 20 rating points, the other team will gain 20 rating points.

[0043] To ensure a rating system can flexibly adapt to the vast number of teams globally, especially those from different leagues, countries, and continents who rarely play each other, making reliable comparative information difficult to provide, a tiered ability rating system can be used. This system assigns ability scores to each continent, country, league, and team. The final ability score is obtained by summing these four ratings together. While this article uses four ratings as an example, it should be understood that any suitable number of ratings can be used. For example, consider Arsenal Football Club, the English top-flight (Premier League) club. Their ability rating is the sum of four separate ability ratings: continent, country, league, and within the league.

[0044] The tiered structure of a league will adjust a team's Elo rating for each match. However, when matches occur between teams from different elements of that tier, the league, national, and continental levels can be adjusted. Only the highest tier of the tier may be affected. Below is an example of Elo rating adjustments.

[0045] Example 1: Manchester United Women vs. Manchester City Women in the English Women's Super League. In this case, only the league ratings of both teams are updated; no other hierarchical values ​​are updated because they are the same (both teams have the same league, country, and continent).

[0046] Example 2: Manchester City (Premier League) vs. York City (National League North) in the FA Cup. In this case, both teams' league ratings can be updated, and their overall league ratings (Premier League and National League North) will also be adjusted.

[0047] Example 3: In the FA Cup, Manchester United and Manchester City (both Premier League teams) face off. In this case, the league ratings of both teams can be updated without updating any other hierarchical values, because the two teams still belong to the same league, even though the competitions are different.

[0048] Example 4: In the UEFA Women's Champions League, Manchester City Women (English Women's Premier League) faces Barcelona Women (Spanish Women's First Division). In this case, the league ratings of both teams, as well as the ratings for England and Spain, can be updated. Alternatively, the league ratings can remain unchanged, as only the highest level of the affected tier structure can be altered (besides the always-updated league ratings).

[0049] Example 5: In the FIFA Club World Cup, Manchester City (Premier League) faced Vasco da Gama (Brazilian Serie A). We updated both teams' league ratings, as well as their continental ratings for Europe and South America. We did not change the league or country ratings because we only adjusted the highest level of the affected hierarchy (in addition to the ever-updated league ratings).

[0050] When updates apply beyond league-wide ratings, a percentage of rating changes resulting from matches may be applied to the highest tier. In the last example above, if Manchester City's Elo rating change based on the final result is +50, a certain percentage of those 50 rating points can be added to Manchester City's league-wide team score, with the remainder added to the European continental rating (and deducted from the South American continental rating). This percentage is a model parameter and can vary for leagues, countries, and continents. 100% of rating changes resulting from league matches are added to the team score.

[0051] When a team is in promotion or relegation, it's crucial to ensure that its new preseason rating is reasonable within the context of the new league. Typically, a team's ability changes significantly during promotion or relegation because promotion brings more funding and relegation brings less. Furthermore, some leagues may have easier promotion and relegation than others, necessitating a model with sufficient generality to reasonably predict a team's initial performance during promotion or relegation.

[0052] Therefore, league change adjustment calculations can be added to this method. For illustrative purposes, an example is provided. For instance, consider the promotion and relegation of teams in the Championship, the second tier of the English football system. The adjustment calculation is based on the percentile of the team's rating within the league it is about to join. It should be noted that the final team rating includes the league rating that will change due to promotion or relegation. Therefore, what needs to be adjusted is the team's ability score within the league.

[0053] For example, in the 2021 / 2022 season, three teams were promoted from the Championship (England's second tier) to the Premier League (England's first tier). Newly promoted teams were considered relatively weaker in their new leagues. Therefore, their league ratings were adjusted based on the lower percentile of the Premier League team ratings from the 2020 / 2021 season. Percentiles are model parameters that are adjusted for each league based on historical knowledge of the ease with which teams transitioned between promotion and relegation between these leagues.

[0054] For teams promoted to a new league, the in-league ratings of all newly promoted teams can be multiplied by a constant that ensures their new average in-league rating equals the lower percentile of the higher-level league's in-league rating (most commonly the 25th percentile). For relegated teams, the same method is used, except the percentile of their previous season's in-league rating is higher (most commonly the 75th percentile). Therefore, it can be ensured that relegated teams' new in-league ratings, on average, are in the higher percentile of the new league (e.g., the average of high-performing teams in the new league).

[0055] The advantage of this method is that it not only takes into account the previous season's performance in the newly adjusted ratings (e.g., the league champion should be rated higher than other promoted teams), but also focuses the new teams' abilities on the possible performance level, which can be optimized during model training.

[0056] In some examples, not all competitions are of equal importance. For instance, the Community Shield is less important than the Champions League final. Therefore, this factor needs to be considered when studying how certain competitions can potentially influence changes in the ability ratings of teams, leagues, countries, and continents.

[0057] Therefore, this rating method can include a match importance factor, which adjusts the "importance value" in Elo's calculation. This value controls how many ratings are available for exchange in a match; higher importance means a team can gain or lose more ratings, and lower importance means they can gain or lose fewer ratings. For example, importance can be adjusted in two ways:

[0058] Based on the importance of the competition. This is based on the type of competition the competition belongs to. For example, domestic leagues and international competitions are generally more important than domestic cup competitions.

[0059] Based on the importance of matches in a match week. A match week is when each team plays its matches in the season. In this case, higher importance is assigned to the first nine match weeks of the domestic league. While these matches may not necessarily be more important to the teams themselves, they are more important for this approach, which focuses on learning from preseason expectations and improving team performance. This may be due to new player signings and is particularly important for teams that have just completed promotion / relegation adjustments.

[0060] The disclosed technology can scale the final rating of each team to generate a value that is easy for the public to understand. For example, in the case of a power ranking system, the final rating of each team can be obtained, and then a rating of 0-100 can be created for each team using a power converter and a minimum / maximum scaling converter, where the best team in the world is rated 100 points (and the worst is rated 0 points) on any given day.

[0061] As discussed in this paper, one or more machine learning models can be trained to understand sports terminology. Therefore, the machine learning models disclosed herein are sports machine learning models. Such sports machine learning models can be trained using sports-related data (e.g., tracking data, event data, etc., as discussed in this paper). Sports machine learning models trained on sports-related data for understanding sports terminology can be trained on sports-related data to adjust one or more weights, layers, nodes, biases, and / or synapses. Sports machine learning models can include components (e.g., weights, layers, nodes, biases, and / or synapses) that collectively establish one or more of the following associations: player and team or league; team and player or league; score and team; rating event and player; sporting event and player or team; win and player or team; loss and player or team, etc. Sports machine learning models can establish associations between sports information and statistics within the context of an event. Sports machine learning models can be trained to adjust one or more weights, layers, nodes, biases, and / or synapses to establish associations of certain sports statistics given the context of an event. For example, a team's win metric can be automatically associated with an opposing team's loss metric. As another example, a static score can be considered a positive attribution for the scoring team and a negative attribution for the losing team. As yet another example, a given score can be ranked relative to one or more other scores based on its relative position compared to those scores.

[0062] Sports machine learning models can be trained on tracking data and / or event data, as discussed in this paper. Such data can include player and / or object location information, movement information, trends, and changes. For example, a sports machine learning model can be trained by modifying one or more weights, layers, nodes, biases, and / or synapses to establish associations about the field surface and / or about the given location of one or more agents. As another example, a sports machine learning model can be trained by modifying one or more weights, layers, nodes, biases, and / or synapses to establish associations about the field surface and / or about the given movement or trend of one or more agents. As yet another example, a sports machine learning model can be trained by modifying one or more weights, layers, nodes, biases, and / or synapses to establish associations between sporting events and corresponding time boundaries, teams, players, coaches, referees, and environmental data associated with the location of the corresponding sporting event.

[0063] Sports machine learning models can be trained by modifying one or more weights, layers, nodes, biases, and / or synapses to establish associations of position, movement, and / or trend information in relation to sports objectives. Sports objectives can be score-related objectives (e.g., points, goals, shots, number of shots, scoring points, etc.), player outcomes (e.g., passes, objects such as ball movement, player positions, etc.), player positions, etc. Sports machine learning models can be trained in relation to sports objectives, game outcomes, player positions, etc., associated with a given sport (e.g., football, American football, basketball, baseball, tennis, golf, rugby, hockey, team sports, individual sports, etc.). For example, an American football-based sports machine learning model can be trained to establish associations or other connections regarding player position information on an American football field. An American football-based sports machine learning model can also be trained to establish associations or other connections regarding sports data about multiple players and American football-specific sports objectives.

[0064] Depending on various factors, the type of one or more machine learning models for a given sport can be determined based on the attributes of the specific sport to which one or more machine learning models are applicable (e.g., generative learning, linear regression, logistic regression, random forest, gradient boosting machine (GBM), deep learning, graph neural network (GNN), and / or deep neural network). Attributes may include, for example, the type of sport (individual or team sport), sport boundaries (e.g., time factors, number of players, object factors, periods of ball possession (e.g., overlapping or independent), field surface type (e.g., restricted, unrestricted, virtual, real, etc.), player positions, etc.

[0065] Depending on the context, a sports machine learning model can receive input data including data related to a specific sport and can generate a matrix representation based on the features of a given sport. The sports machine learning model can be trained to determine the latent features of a given sport. For example, the matrix may include fields and / or subfields related to player information, team information, object information, sport boundary information, sport surface information, etc. Attributes associated with each field and / or subfield can be populated within the matrix based on received or extracted data. The sports machine learning model can perform operations based on the generated matrix. Features can be updated based on input data or updated training data, which is based on, for example, sports data associated with features of a previously untrained model related to a given sport. Therefore, the sports machine learning model can be iteratively trained based on sports data or simulated data.

[0066] Figure 1An exemplary environment 100 that can be used with the techniques described herein is depicted. One or more user devices 112 can communicate via an electronic network 110. The one or more user devices 112 can be associated with a user, such as a user viewing and / or interacting with a generated interactive display, an administrator of one or more components of environment 100, etc. As will be discussed further below, one or more computing systems 102 can communicate with one or more other components of environment 100 via electronic network 110.

[0067] User device 112 may be configured to enable a user to access and / or interact with other systems in environment 100. For example, user device 112 may each be a computer system, such as, for example, a desktop computer, a mobile device, a tablet computer, etc. In some embodiments, user device 112 may include one or more electronic applications, such as programs, plug-ins, browser extensions, etc., installed on the memory of user device 112. In some embodiments, electronic applications may be associated with one or more other components in environment 100. For example, electronic applications may include one or more of system control software, system monitoring software, software development tools, etc.

[0068] In various embodiments, environment 100 may include data storage device 114 (e.g., a database). Data storage device 114 may include server systems and / or data storage systems, such as computer-readable storage devices, such as hard disks, flash drives, disks, etc. In some embodiments, data storage device 114 includes application programming interfaces for exchanging data with and / or interacting with other systems, such as one or more other components of the environment. Data storage device 114 may include and / or act as a repository or source for storing event data, user data, generated displays, output data, etc. (e.g., to be transferred to user device 112 or any of the other components of environment 100).

[0069] In some embodiments, components of environment 100 are associated with a common entity, such as a service provider, account provider, etc. For example, in some embodiments, computing system 102 and data storage device 114 may be associated with a common entity. In some embodiments, one or more components of the environment are associated with an entity different from another. For example, computing system 102 may be associated with a first entity (e.g., a service provider), while data storage device 114 may be associated with a second entity (e.g., a storage entity that provides storage services to the first entity). The systems and devices of environment 100 can communicate in any arrangement.

[0070] likeFigure 1 The depicted computing system 102 may include a data streaming module 104. In various embodiments, the data streaming module 104 is configured to receive a plurality of event data, match data, user data, content, advertising, and / or real-time streaming or streaming audio / video data, and other aspects that may be collected and / or compiled by the computing system 105 or using components separate from the environment 100. In examples, match data and / or event data may include actions performed by players during a match that provide information to a generated interactive display. In various embodiments, data may be received by the data streaming module 104 nearly simultaneously with, or at least substantially simultaneously with, the occurrence of a player's event action. Data may be received by the computing system 102 via an electronic network 110.

[0071] According to some embodiments, the data stream module 104 may receive on-field data or broadcast data associated with a sporting event. Such on-field data or broadcast data can be used to generate the event data discussed herein. For example, such on-field data or broadcast data may be provided to one or more event machine learning models. The one or more event machine learning models may be trained based on training data, including historical or simulated on-field data / broadcast data, historical or simulated event data (e.g., labeled data), historical or simulated event actions, etc. Depending on the machine learning algorithm, the training data may be used to train the event machine learning model by modifying one or more weights, layers, synapses, biases, etc., as discussed herein. Alternatively or additionally, such on-field data or broadcast data may supplement the received event data for validation and / or for generating interactive displays.

[0072] The computing system 102 may also include a display generation module 106. In various embodiments, the display generation module 106 may be configured to generate an interactive display using data received by the data stream module 104. In various embodiments, the display generation module 106 may be configured to generate an interactive display. The interactive display may include at least one graphical representation of one or more of the aspects described herein. In various embodiments, the interactive display is generated in real time when data is received (e.g., by the data stream module 104). The computing system 102 may also include a transmission module 108. In various embodiments, the transmission module 108 may be configured to transmit the interactive display to a user interface.

[0073] like Figure 1The depicted environment 100 may also include an electronic network 110. In various embodiments, the electronic network 110 may be a wide area network (“WAN”), a local area network (“LAN”), a personal area network (“PAN”), etc. In some embodiments, the electronic network 110 includes the Internet, and information and data provided between various systems occur online. “Online” may mean connecting to or accessing source data or information from a location remote from other devices or networks connected to the Internet. Alternatively, “online” may refer to connecting to or accessing an electronic network (wired or wireless) via a mobile communication network or device. The Internet is a global computer network system—a network of networks in which one party at a computer or other device connected to the network can obtain information from any other computer and communicate with parties at other computers or devices. The most widely used part of the Internet is the World Wide Web (often abbreviated as “WWW” or referred to as the “Web”). A “website page” typically encompasses location, data storage device, etc., which is hosted and / or operated by a computer system so that it can be accessed online, and may include data configured to enable programs, such as web browsers, to perform operations such as sending, receiving or processing data, generating visual displays and / or interactive interfaces.

[0074] Despite Figure 1 While depicted as separate components, it should be understood that in some embodiments, a component or part of a component in environment 100 may be integrated with or incorporated into one or more other components. In another example, computing system 102 may be integrated into a data storage system. The data storage system may be configured to communicate with and / or receive / transmit data with other components of environment 100 via electronic network 110. In some embodiments, the operation or aspects of one or more of the components discussed above may be distributed across one or more other components. Any suitable arrangement and / or integration of various systems and devices within environment 100 may be used.

[0075] The methods described herein can be performed on or between one or more computing devices. Figure 2An example method 200 for generating dynamic ratings for sports teams is described. Method 200 can be used to generate dynamic ratings for sports teams for output and user use. For example, a sports team could be an English Premier League (EPL) team, and method 200 can generate an icon for display on a graphical user interface (GUI) that includes the EPL team's dynamic rating relative to other EPL teams and other teams globally. In some examples, a sports team could be an esports team. For example, a sports team could be a League of Legends (LoL) team, and method 200 can generate an icon for display on a graphical user interface (GUI) that includes the LoL team's dynamic rating relative to other LoL teams and other LoL teams globally. The sports-related techniques disclosed herein can be similarly applied to esports teams (e.g., based on the esports team's league, position, hierarchy, etc.).

[0076] Method 200 includes a first step 202, in which a first set of data can be received from data storage device 114 and / or one or more data sources separate from data storage device 114. The first set of data may be associated with one or more parameters related to generating a team's rating. For example, the first set of data may include one or more of the following: a first match report, a first set of player data, geographic location, and / or a first score report. In some examples, the first set of data may be collected manually by one or more users. One or more users may access score reports including matches between multiple teams. The score reports may include the final match score (e.g., a match ending 2-0 or 1-1). For example, the geographic location of a team may be Germany, the United Kingdom, France, the United States, Colombia, Argentina, etc. One or more parameters may include, for example, match importance weights based on competitiveness and / or match importance weights based on the match week. One or more parameters control the impact of a particular match on a team's dynamic rating.

[0077] In step 204, multiple ability scores for the team can be calculated based on the first set of data. For example, a first and second ability score can be generated based on the team's geographical location. The first ability score could be a national ability score. The second ability score could be a continental ability score. A third ability score can be generated for the league associated with the team. This third ability score could be a league ability score. A fourth ability score can be generated within the league associated with the team. This fourth ability score could be a league-specific ability score. While this document describes four ability scores for multiple ability scores, any desired number of ability scores can be used to generate the team's first rating.

[0078] In step 3, 206, a first rating can be generated by summing multiple ability scores or using other applicable relationships. For example, a first rating can be generated by adding national ability scores, continental ability scores, league ability scores, and league-specific ability scores. In one example, the first rating could be the Elo rating for a sports team. The first rating can be incorporated into a hierarchical rating system (…). Figure 5 (As shown) and can assign a position within the tiered rating system to the first rating.

[0079] In step 4, 208, multiple ability scores can be dynamically adjusted upon receiving the second set of data. The second set of data can be associated with one or more parameters related to generating the team's rating. For example, the second set of data may include one or more of the following: a second match report, second set of player data, geographic location, and / or a second score report. The second set of data can be used to update one or more of the following: national ability score, continental ability score, league ability score, and intra-league ability score. For example, receiving a second score report associated with the team can update the intra-league ability score based on the second score report. As another example, receiving a second score report associated with a team, and the second score report detailing matches between the team associated with a league and different teams associated with different leagues. In such an example, the intra-league ability score and the league ability score can be updated. As yet another example, the second score report detailing matches between the team associated with a league and different teams associated with the same league, but in competitions independent of that league. In such an example, only the intra-league ability score can be updated.

[0080] In step 5.210, the first rating is updated based on multiple dynamically adjusted ability scores. This first rating is updated via a deterministic algorithm. The deterministic algorithm can adjust the ability scores based on a first set of data and a second set of data, thereby updating the first rating relative to the hierarchical rating system. For example, in step 6.212, the team's first rating can be represented by an icon in a graphical user interface (GUI). Figure 7 (As shown). When the first rating is updated via a deterministic algorithm, the icon positions can be rearranged within the GUI. The rearrangement of icons within the GUI indicates whether a team's position is improved or worsened relative to other teams in the hierarchical rating system.

[0081] Figure 3An example method 300 for generating dynamic ratings for sports teams is described. A trained, machine learning-based rating model can generate rating parameters. These rating parameters can be associated with a deterministic algorithm to update the dynamic ratings of the sports teams. In step 302, a first set of data can be received from data storage device 114 and / or one or more data sources separate from data storage device 114. The first set of data can be associated with a first parameter related to generating the dynamic ratings of the sports teams. For example, the first set of data may include one or more of the following: a first match report, a first set of player data, and / or a first score report. In some examples, the first set of data may be collected manually by one or more users. One or more users can access score reports including matches between multiple teams. The score reports may include the final match score (e.g., a match ending 2-0 or 1-1). In one example, the geographical location of the sports teams may be Germany, the United Kingdom, France, the United States, Colombia, Argentina, etc. The first parameter may include, for example, a match importance weight based on competitiveness or a match importance weight based on the match week. The first parameter controls the influence value of a particular match on the dynamic rating of the sports team.

[0082] In step 304, a second set of data may be received from data storage device 114 and / or one or more data sources separate from data storage device 114. The second set of data may be associated with a first parameter related to generating a dynamic rating for the sports team. For example, the second set of data may include one or more of the following: a second match report, a second set of player data, geographic location, and / or a second score report.

[0083] In step 306, the second parameter can be output by a machine learning model (e.g., a parametric machine learning model). The machine learning model can be trained using a training data set, which includes historical or simulated characteristics associated with the first and second data sets, based on historical, simulated, or actual match reports (e.g., using tracking data and / or event data), historical or simulated score reports, etc. The machine learning model can receive the first and second data sets as input. Score reports can be provided to the machine learning model as input, or can be accessed by the machine learning model in other ways. The machine learning model learns the association between the first and second data sets. Based on such input data and / or access, the machine learning model can output rating parameters in step 308 for calculating the dynamic rating of the sports teams. For example, the machine learning model can generate and output additional parameters based on one or more scores of a sports match between two teams in a score report or one or more match reports of a sports match between two teams in a match report. In step 310, the machine learning model can update the output rating parameters based on the received third set of data. For each new set of data received, the machine learning model can output updated rating parameters.

[0084] Figure 4 An example method 400 is described to generate a sports team rating report for display on a GUI. Method 400 can be used to generate a sports team rating report for output and user use. For example, the sports team can be an English Premier League (EPL) team, and method 400 can generate a rating report to be displayed on a GUI that includes predictions of the EPL team relative to other EPL teams and other teams globally.

[0085] Method 400 includes a first step 402, in which a first set of data can be received from data storage device 114 and / or one or more data sources separate from data storage device 114. The first set of data may be associated with one or more parameters related to generating a team's rating. For example, the first set of data may include one or more of the following: a first match report, a first set of player data, geographic location, and / or a first score report. In some examples, the first set of data may be collected manually by one or more users. One or more users may access score reports including matches between multiple teams. Score reports may include the final match score (e.g., a match ending 2-0 or 1-1). For example, the geographic location of a team may be Germany, the United Kingdom, France, the United States, Colombia, Argentina, etc. One or more parameters may include, for example, match importance weights based on competitiveness and / or match importance weights based on match week. One or more parameters control the impact of a particular match on a team's dynamic rating.

[0086] In step 404, multiple ability scores for the team can be calculated based on the first set of data. For example, a first and second ability score can be generated based on the team's geographical location. The first ability score could be a national ability score. The second ability score could be a continental ability score. A third ability score can be generated for the league associated with the team. This third ability score could be a league ability score. A fourth ability score can be generated within the league associated with the team. This fourth ability score could be a league-specific ability score. While this document describes four ability scores for multiple ability scores, any desired number of ability scores can be used to generate the team's first rating.

[0087] In step 3, 406, a first rating can be generated by summing multiple ability scores. For example, a first rating can be generated by adding national ability scores, continental ability scores, league ability scores, and intra-league ability scores. In one example, the first rating could be the Elo rating for a sports team. The first rating can be incorporated into a hierarchical rating system (…). Figure 5 (As shown) and can assign a position within the tiered rating system to the first rating.

[0088] In step 408, multiple ability scores can be dynamically adjusted upon receiving the second set of data. The second set of data can be associated with one or more parameters related to generating the team's rating. For example, the second set of data may include one or more of the following: a second match report, second set of player data, geographic location, and / or a second score report. The second set of data can be used to update one or more of the following: national ability score, continental ability score, league ability score, and intra-league ability score. For example, receiving a second score report associated with the team allows updating the intra-league ability score based on that report. As another example, receiving a second score report associated with a team details matches between teams associated with a league and teams associated with different leagues. In such an example, the intra-league ability score and the league ability score can be updated. As yet another example, a second score report details matches between teams associated with a league and teams associated with the same league, but in events independent of that league. In such an example, only the intra-league ability score can be updated.

[0089] In step 5.410, the first rating is updated based on multiple dynamically adjusted ability scores. This first rating is updated via a deterministic algorithm. The deterministic algorithm can adjust the ability scores based on a first set of data and a second set of data, thereby updating the first rating relative to the hierarchical rating system.

[0090] In step 412, a third set of data may be received from data storage device 114 and / or one or more data sources separate from data storage device 114. The third set of data may be associated with one or more parameters related to generating the rating of the second sports team. For example, the third set of data may include one or more of the following: a third match report, a third set of player data, a third geographic location, and / or a third score report. In some examples, the third set of data may be collected manually by one or more users. One or more users may access score reports including matches between multiple teams. Score reports may include the final match score (e.g., a match ending 2-0 or 1-1). For example, the geographic location of a sports team may be Germany, the United Kingdom, France, the United States, Colombia, Argentina, etc. One or more parameters may include, for example, match importance weights based on competitiveness and / or match importance weights based on the match week. One or more parameters control the impact of a match on the dynamic rating of a sports team.

[0091] In step 7.414, the second multiple ability scores for the second sports team can be calculated based on the third set of data. In step 8.416, the second rating for the second sports team can be generated based on the sum of the second multiple ability scores. In step 9.418, the second multiple ability scores can be dynamically adjusted upon receiving the fourth set of data. The fourth set of data can be associated with one or more parameters related to generating the second rating for the second sports team. For example, the fourth set of data may include one or more of the following: fourth match report, fourth set of player data, and / or fourth score report. The fourth set of data can be used to update one or more of the following: national ability score, continental ability score, league ability score, and intra-league ability score.

[0092] In step 10 (420), the second rating can be updated based on a dynamically adjusted set of multiple ability scores. This second rating can be updated using a deterministic algorithm.

[0093] In step 11, 422, a rating report can be generated using a trained machine learning-based model. The rating report can be output by the machine learning model (e.g., a reporting machine learning model). The machine learning model can be trained using a training data set that includes historical or simulated characteristics associated with the first and second ratings, based on historical, simulated, or actual match reports (e.g., using tracking data and / or event data), historical or simulated score reports, etc. The machine learning model can receive the first and second ratings as input. The machine learning model learns the association between the first and second ratings. Score reports can be provided as input to the machine learning model, or can be otherwise accessed by the machine learning model. Based on such input data and / or access, the machine learning model can output a rating report in step 424 to predict the outcome between the two teams. For example, the machine learning model can generate and output a prediction to a downstream entity based on the first and second ratings between the two teams. For each instance where adjustments are made to the first and second ratings, the machine learning model can output an updated rating report.

[0094] Figure 5 An example GUI interface representing a hierarchical rating system is depicted. Teams can be positioned within the GUI to display their ratings associated with each of multiple ability scores. For example, multiple teams are ranked based on their ratings to show their relative strength compared to other teams within the multi-team pool.

[0095] Figure 6 A simplified calculation of the rating performed by a deterministic algorithm is described.

[0096] Figure 7An example GUI interface representing a hierarchical rating system is depicted. Teams can be positioned within the GUI to display their ratings associated with each of multiple ability scores. For example, multiple teams are ranked based on their ratings to show their relative strength compared to other teams within the multi-team pool.

[0097] Figure 8 A flowchart illustrating a method for training a machine learning model based on one aspect of the disclosed topic is provided. Figure 8 As shown in flowchart 800, training data 812 may include one or more of stage inputs 814 and known results 818 associated with the machine learning model to be trained. Stage inputs 814 may originate from any applicable source, including the components or sets shown in the figures provided herein. Known results 818 may be included for machine learning models generated based on supervised or semi-supervised training. Supervised machine learning models may be trained without using known results 818. Known results 818 may include known or expected outputs for future inputs that are similar to or in the same category as stage inputs 814 that do not have corresponding known outputs.

[0098] Training data 812 and training algorithm 820 can be provided to training component 830, which can apply training data 812 to training algorithm 820 to generate a trained machine learning model 850. According to one embodiment, a comparison result 816 can be provided to training component 830, which compares the previous output of the corresponding machine learning model to apply the previous result to retrain the machine learning model. Training component 830 can use comparison result 816 to update the corresponding machine learning model. Training algorithm 820 can utilize machine learning networks and / or models, including but not limited to, deep learning networks such as deep neural networks (DNN), convolutional neural networks (CNN), fully convolutional networks (FCN), and recurrent neural networks (RCN), probabilistic models (such as Bayesian networks and graphical models), and / or discriminative models (such as decision forests and maximum margin methods). The output of flowchart 800 can be the trained machine learning model 850.

[0099] The machine learning models disclosed herein can be trained by adjusting one or more weights, layers, and / or biases during the training phase. During the training phase, historical or simulated data can be provided as input to the model. The model can adjust one or more of its weights, layers, and / or biases based on such historical or simulated information. The adjusted weights, layers, and / or biases can be configured based on the training to a production version of the machine learning model (e.g., a trained model). Once trained, the machine learning model can output its output according to the subject matter disclosed herein. According to one implementation, one or more machine learning models disclosed herein can be continuously updated based on the use or implementation of the machine learning model's output.

[0100] It should be understood that the aspects in this invention are merely exemplary, and other aspects may include various combinations of features from other aspects, as well as additional or fewer features.

[0101] Generally, any process or operation discussed in this invention, which is understood to be computer-implementable, such as the processes shown in the flowcharts disclosed herein, can be executed by one or more processors of a computer system, such as any system or apparatus in the exemplary environment disclosed herein, as described above. A process or process step executed by one or more processors may also be referred to as an operation. One or more processors may be configured to execute these processes by accessing instructions (e.g., software or computer-readable code) that, when executed by one or more processors, cause one or more processors to perform the process. The instructions may be stored in the memory of the computer system. The processor may be a central processing unit (CPU), a graphics processing unit (GPU), or any suitable type of processing unit.

[0102] A computer system, such as the system or apparatus that implements the processes or operations in the examples above, may include one or more computing devices, such as one or more of the systems or apparatuses disclosed herein. One or more processors of the computer system may be included in a single computing device or distributed across multiple computing devices. The memory of the computer system may include the respective memory of each of the multiple computing devices.

[0103] Figure 19 is a simplified functional block diagram of a computer 900 that can be configured to perform the methods disclosed herein, according to an exemplary aspect of the present invention. For example, according to an exemplary aspect of the present invention, the computer 900 can be configured as a system. In various aspects, any of the systems described herein can be the computer 900, which includes, for example, a data communication interface 920 for packet data communication. The computer 900 may also include a central processing unit (“CPU”) 902 in the form of one or more processors for executing program instructions. Although the computer 900 can receive programming and data via network communication, the computer 900 may include an internal communication bus 908 and a storage unit 906 (such as ROM, HDD, SDD, etc.) that can store data on a computer-readable medium 922.

[0104] Although instruction 924 may be temporarily or permanently stored within other modules of computer 900 (e.g., processor 902 and / or computer-readable medium 922), computer 900 may also have storage for techniques used to execute those presented herein, such as those related to... Figures 2 to 4 The instructions 924 described herein are stored in memory 904 (such as RAM). Computer 900 may also include input and output ports 912 and / or a display 910 for connection to input and output devices such as a keyboard, mouse, touchscreen, monitor, display, etc. Various system functions can be implemented in a distributed manner on multiple similar platforms to distribute the processing load. Alternatively, the system can be implemented by appropriately programming a single computer hardware platform.

[0105] The programmatic aspect of this technology can be considered a "product" or "manufactured item," typically carried on a machine-readable medium or in the form of embodied executable code and / or associated data. "Storage" type media includes computers, processors, etc., or their associated modules, such as any or all of various semiconductor memories, tape drives, disk drives, etc., which can provide non-transitory storage for software programming at any time. Sometimes, all or part of the software can communicate via the Internet or various other telecommunications networks. For example, such communication enables the loading of software from one computer or processor to another, such as from a management server or host of a mobile communication network to a server's computer platform and / or from a server to a mobile device. Therefore, another type of medium that can carry software elements includes light waves, radio waves, and electromagnetic waves, such as physical interfaces between local devices, used via wired and fiber optic networks, and via various air links. Physical elements carrying such waves, such as wired or wireless links, fiber optic links, etc., can also be considered as media carrying software. As used herein, unless limited to non-transitory, tangible "storage" media, the term "readable medium" for a computer or machine refers to any medium that participates in providing instructions to a processor for execution.

[0106] While the disclosed methods, apparatus, and systems are described with reference to illustrative data transmission, it should be understood that the disclosed aspects are applicable to any environment, such as desktop or laptop computers, car entertainment systems, home entertainment systems, etc. Furthermore, the disclosed aspects are applicable to any type of Internet protocol.

[0107] It should be understood that in the foregoing description of exemplary aspects of the invention, various features of the invention are sometimes combined together in a single aspect, drawing, or description therein to simplify the invention and aid in understanding one or more of the various inventive aspects. However, this approach of the invention should not be interpreted as reflecting an intention that the claimed invention requires more features than expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects exist in fewer features than all features in a single foregoingly disclosed aspect. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, wherein each claim exists independently as a separate aspect of the invention.

[0108] Furthermore, while some aspects described herein include features included in other aspects but not others, combinations of features from different aspects should be within the scope of this invention and form different aspects, as those skilled in the art should understand. For example, any of the claimed aspects may be used in any combination in the following claims.

[0109] Therefore, while certain aspects have been described, those skilled in the art will recognize that other and further modifications can be made thereto without departing from the spirit of the invention, and it is intended that all such changes and modifications fall within the scope of the invention. For example, functions can be added or removed from the block diagram, and operations can be interchanged between function blocks. Operations can be added or removed to the method within the scope of the invention.

[0110] The subject matter disclosed above should be considered illustrative rather than restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments that fall within the true spirit and scope of the invention. Therefore, to the maximum extent permitted by law, the scope of the invention should be determined by the broadest permissible interpretation of the following claims and their equivalents, and should not be constrained or limited by the foregoing detailed description. While various embodiments of the invention have been described, it will be apparent to those skilled in the art that many more embodiments are possible within the scope of the invention. Therefore, the invention is not limited except as provided in the appended claims and their equivalents.

Claims

1. A method for generating dynamic ratings for entities and rearranging icons associated with said entities on a graphical user interface (GUI) of a computer system, the method comprising: Receive a first set of data associated with one or more parameters of the entity via one or more data sources; Multiple capability scores are calculated based on the first set of data associated with the one or more parameters using a deterministic algorithm; A first rating is generated based on each of the plurality of ability scores using the deterministic algorithm. The multiple capability scores are dynamically adjusted based on a second set of data received in association with one or more parameters of the entity, via the deterministic algorithm. The first rating is updated based on the adjusted multiple ability scores via the deterministic algorithm. Based on the updated first rating, the icon will be automatically rearranged to a position above or below its current position on the GUI.

2. The method according to claim 1, wherein one or more parameters control the influence of a single data point within the first set of data on the dynamic rating.

3. The method according to claim 1, wherein the entity is a sports team.

4. The method of claim 1, wherein the one or more parameters include competition-based match importance weights and / or match week-based match importance weights.

5. The method according to claim 1, wherein the first set of data includes one or more of the following: a first match report, a first set of player data, geographical location and / or a first score report.

6. 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 is different from the others.

7. The method of claim 6, wherein the first capability score is a capability score associated with a country.

8. The method of claim 6, wherein the second capability score is a capability score associated with a continent.

9. The method of claim 6, wherein the third ability score is an ability score associated with a league, the league being associated with the entity.

10. The method of claim 6, wherein the fourth ability score is an ability score associated with the entity within the league.

11. The method of claim 1, wherein the GUI is represented as a hierarchical rating system, and wherein the icon of the entity represents a sports team.

12. A method for generating dynamic ratings for entities, the method comprising: Receive the first set of data associated with the entity via one or more data sources; Receive a second set of data associated with the entity via one or more data sources; One or more rating parameters are automatically calculated 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 the association 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 being associated with (i) one or more scores of a sporting event, or (ii) one or more match reports of a sporting event. The trained machine learning model is configured to generate one or more rating parameters as output based on the learned associations via the training, for use in calculating the dynamic rating of the entity. as well as A third set of data associated with the entity is received via the one or more data sources, wherein the trained machine learning model generates one or more updated rating parameters based on the third set of data to update the dynamic rating of the entity.

13. The method of claim 12, wherein the first set of data includes one or more of the following: a first match report, a first set of player data, a first geographical location and / or a first score report.

14. The method of claim 13, wherein the second set of data includes one or more of the following: a second match report, a second set of player data, a second geographic location and / or a second score report.

15. The method of claim 12, wherein the one or more rating parameters include a competition-based weighting of match importance and / or a competition-week-based weighting of match importance.

16. The method of claim 12, wherein the entity is a sports team.

17. The method of claim 12, wherein the updated rating parameter is used as input to a deterministic algorithm.

18. A method for generating a rating report to display on a graphical user interface (GUI) of a downstream entity, the method comprising: Receive a first set of data associated with one or more parameters of a first entity via one or more data sources; A first plurality of capability scores are calculated based on the first set of data associated with the one or more parameters using a deterministic algorithm; A first rating for the first entity is generated based on the first plurality of capability scores using the deterministic algorithm. The first plurality of capability scores are dynamically adjusted based on a second set of data received in association with one or more parameters of the first entity, using the deterministic algorithm. The first rating is updated based on the adjusted first plurality of ability scores via the deterministic algorithm. Receive a third set of data associated with one or more parameters of the second entity via the one or more data sources; A second plurality of capability scores are calculated based on the third set of data associated with the one or more parameters via the deterministic algorithm. A second rating for the second entity is generated based on the second plurality of capability scores using the deterministic algorithm. The second plurality of capability scores are dynamically adjusted based on a fourth set of data received in association with one or more parameters of the second entity, via the deterministic algorithm. The second rating is updated based on the adjusted second plurality of ability scores via the deterministic algorithm. The rating report is automatically calculated 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 the association between the first rating and the second rating, wherein the first rating is associated with the rating of the first sports team and the second rating is associated with the rating of the second sports team. as well as The trained machine learning model is configured to generate the rating report as output based on the learned associations via the training, for predicting the outcome between the first sports team and the second sports team, 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 comprises one or more of the following: a first match report, a first set of player data, a first geographic location and / or a first score report, and wherein the second set of data comprises one or more of the following: a second match report, a second set of player data, a second geographic location and / or a second score report.

20. The method of claim 18, wherein for each instance of updating the first rating and the second rating, the machine learning model outputs an updated rating report.

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