Patent Application
20230364487
The present invention relates generally to the field of players' rating systems. More specifically, the present invention relates to a novel baseball players' rating system designed to provide ratings of baseball players using a plurality of performance parameters and weighting factors. The system eliminates the effect of personal opinions and enables selection of the No. 1 baseball player in the world by pure statistics. Further, the system can be used to assist in predicting the outcome of an individual or team match up. Finally, the system will create a unified world base ranking. Accordingly, the present disclosure makes specific reference thereto. Nonetheless, it is to be appreciated that aspects of the present invention are also equally applicable to other like applications, devices, and methods of manufacture.
By way of background, Baseball is a very popular sport throughout the world and specially in the United States. In fact, Baseball has been played for over a century. It is considered to be one of the oldest and most traditional sports and has a large and dedicated fan base, with millions of individuals attending games and watching broadcasts every year. In professional baseball, rating and rankings of baseball players are crucial for both teams and fans. Traditionally, awards, selection of players in teams, draft choices, and more, have been based on the opinions of sports analysts, coaches, and fans rather than solely on objective numerical values.
While statistics are a crucial part of evaluating player performance, they have their limitations. Baseball statistics do not differentiate between the strength of the opponent faced, which can significantly impact the player's performance. For example, a player who performs well against weaker teams may have better overall stats than a player who plays against stronger teams but performs slightly worse. Similarly, statistics cannot account for the different stages of the season, such as the beginning, middle, and end, which can affect a player's performance.
Geography is another crucial factor that can impact player performance, and it is not always accounted for in statistics. Players who play in different regions or at higher elevations may face different weather conditions, which can impact their performance. Additionally, players who play in more hitter-friendly ballparks may have inflated statistics compared to players who play in more pitcher-friendly parks.
Overall, determining the No. 1 player in the world in professional baseball using pure statistics can be challenging. There are numerous factors to consider beyond the player's numerical performance, including the quality of their opponents, the stage of the baseball season, and the geography of the games played. Therefore, there is a need for a ranking system that can take into account all of these factors to make a more objective determination of player performance. Therefore, there exists a long felt need in the art for an improved baseball players' ranking system. There is also a long felt need in the art for a baseball players' ranking system that includes additional parameters for rating and ranking baseball players. Additionally, there is a long felt need in the art for baseball players' rating system that enhances use of statistics for a more objective and correct ranking of baseball players. Moreover, there is a long felt need in the art for a baseball players' ranking system that uses body of work, results for performance, various game situations, geographical location, and more for rating players. Further, there is a long felt need in the art for players' rating system that enables individuals to identify the best player. Finally, there is a long felt need in the art for a baseball players' rating system that reduces the effect of a personal opinion on the No. 1 baseball player in the world and improves the use of statistics to make an objective determination.
The subject matter disclosed and claimed herein, in one embodiment thereof, comprises a computer implemented method for determining an objective rating for a baseball player based on the player's performance data and weighting factors. The method comprising the steps of receiving performance data of a player, calculating a plurality of performance parameters including at least a batting average, an on-base percentage, a slugging percentage, an on-base plus slugging, a fielding independent pitching, wins above replacement, and earned run average, including dynamically generating weighting factors for each performance parameter, wherein the weighting factors include at least work of a player, results for performance of a player, player's performance in different game situations and geographical locations, normalizing each performance parameter, and calculating an overall rating for each player using a machine learning model utilizing the weighting factors and normalized performance parameters. The ratings are used for determining the best player, ranking players, and are displayed on an application installed in client devices.
In this manner, the players' rating method of the present invention accomplishes all of the forgoing objectives and provides users with a method for determining worldwide professional baseball player rankings and rating levels. The method includes additional factors such as body of work, results for performance, various game situations, geographical location, and more, thus determining the No. 1 baseball player in the world by using pure statistics.
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed innovation. This summary is not an extensive overview, and it is not intended to identify key/critical elements or to delineate the scope thereof. Its sole purpose is to present some general concepts in a simplified form as a prelude to the more detailed description that is presented later.
The subject matter disclosed and claimed herein, in one embodiment thereof, comprises a method for determining a baseball player rating for a baseball player based on the player's performance data. The method comprising the steps of receiving performance data of a player, calculating a plurality of performance parameters including at least a batting average, an on-base percentage, a slugging percentage, an on-base plus slugging, a fielding independent pitching, wins above replacement, and earned run average, including dynamically generating weighting factors for each performance parameter based on at least a work of a player, results for performance of a player, player's performance in different game situations and geographical location, normalizing each performance parameter using an average of the particular parameter across all or some of the baseball players, and calculating an overall rating for each player using a machine learning model utilizing the weighting factors and normalized performance parameters.
In yet another embodiment, the weighting factors are dynamic and vary among players and can also vary for the same player with the course of time, based on the types of fields on which a specific player plays games/matches.
In yet another embodiment, the machine learning algorithm adjusts the weights based on historical player performance data and is periodically updated to incorporate new performance trends.
In yet another embodiment, a baseball player ranking system is disclosed. The system comprising a plurality of clients configured to access a server via a network, each of the clients including a software application for displaying ratings and other statistics of baseball players, the server comprising a processing unit, one or more databases for storing a plurality of information including baseball player personal information and playing information, the information received from a plurality of third-party sources including baseball organizations, fan communities, media outlets and more, the one or more databases coupled to the processing unit for storing the plurality of information, a ranking processing engine configured for generating real-time rating and rankings of baseball players, the ranking processing engine including a machine learning module configured to apply a plurality of supervised weighting factors to generate the real-time ratings, and a rating database for storing the real-time ratings and rankings of the players and providing the rankings to the clients via the software application.
In yet another embodiment, the overall rating of a player is calculated using the formula f(w1x1+w2x2+ . . . +wn*xn), wherein x1, x2, . . . , xn are the normalized scores for performance parameters and w1, w2, . . . , wn are the corresponding adjusted weights assigned by the K-vector based on the weighting factors.
Numerous benefits and advantages of this invention will become apparent to those skilled in the art to which it pertains upon reading and understanding of the following detailed specification.
To the accomplishment of the foregoing and related ends, certain illustrative aspects of the disclosed innovation are described herein in connection with the following description and the annexed drawings. These aspects are indicative, however, of but a few of the various ways in which the principles disclosed herein can be employed and are intended to include all such aspects and their equivalents. Other advantages and novel features will become apparent from the following detailed description when considered in conjunction with the drawings.
The description refers to provided drawings in which similar reference characters refer to similar parts throughout the different views, and in which:
The innovation is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding thereof. It may be evident, however, that the innovation can be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate a description thereof. Various embodiments are discussed hereinafter. It should be noted that the figures are described only to facilitate the description of the embodiments. They are not intended as an exhaustive description of the invention and do not limit the scope of the invention. Additionally, an illustrated embodiment need not have all the aspects or advantages shown. Thus, in other embodiments, any of the features described herein from different embodiments may be combined.
As noted above, there is a long felt need in the art for an improved baseball players' ranking system. There is also a long felt need in the art for a baseball players' ranking system that includes additional parameters for rating and ranking baseball players. Additionally, there is a long felt need in the art for baseball players' rating system that enhances use of statistics for a more objective and correct ranking of baseball players. Moreover, there is a long felt need in the art for a baseball players' ranking system that uses body of work, results for performance, various game situations, geographical location, and more for rating players. Further, there is a long felt need in the art for players' rating system that enables individuals to identify the best player. Finally, there is a long felt need in the art for a baseball players' rating system that reduces the effect of a personal opinion on the No. 1 baseball player in the world and improves the use of statistics to make an objective determination.
The present invention, in one exemplary embodiment, is a baseball player ranking system. The system includes a plurality of clients, each of the clients including a software application for displaying ratings and other statistics of baseball players, a server, one or more databases for storing a plurality of information, the information received from a plurality of third-party sources, the one or more databases for storing the plurality of information, a ranking processing engine configured for generating real-time rating and rankings of baseball players, the ranking processing engine including a machine learning module configured to apply a plurality of supervised weighting factors to generate the real-time ratings, and a rating database for storing the real-time ratings and rankings of the players and providing the rankings to the clients via the software application.
Referring initially to the drawings,
Each client of the clients 102, 104, 106 includes a software application 110 for displaying rankings and other statistics of baseball players. The software application 110 can be designed to be installed in different types of clients and different types of operating systems. The software application 110 allows users to access the functionalities offered by the server 108. The clients 102, 104, 106 can communicate with the server 108 using suitable communication interfaces via a network 112, such as the Internet. The clients 102, 104, 106 and the server 108 can communicate, in part or in whole, via wireless or hardwired communications, such as Ethernet, IEEE 802.11x wireless, Zigbee, or the like.
The server 108 can utilize various Web data interface techniques such as the Common Gateway Interface (CGI) protocol and associated applications (or “scripts”), Java™ “servlets”, i.e., Java™ applications running on the Web server, or the like to present information and receive input from the clients 102, 104, 106 via the application 110. The server 108 is coupled to one or more databases 114, 116 for storing a plurality of information 118 including but not limited to baseball player's personal information, result of performance, geography locations, strength of schedule and more. The stored information 118 is received from a plurality of third-party sources 120 including baseball organizations, fans communities, media outlets and more.
The databases 114, 116 are coupled to a ranking processing engine 122 configured for receiving the stored information 118 and processing to generate real time rankings for the players. The ranking processing engine 122 includes a machine learning module 124 for applying a plurality of supervised weighting factors for generating rankings for the players as described later in the disclosure. A ranking database 126 stores the real time rankings of the players and are provided to the users on the application 110.
Thereafter, in the next step 206, additional features including statistics as illustrated in
The system 100 continuously updates the rankings of the players based on new data of the players as it becomes available in the databases 114, 116, such as the results of new games and updated player information. This helps ensuring that the rankings remain up-to-date and accurate over time.
More information such as batting average, wins above replacement, on-base percentage, earned run average and more are displayed using the “More Information” tab 308. It should be noted that the user interface 300 can display information for a plurality of baseball players simultaneously.
Based on at least body of work of a player, results for performance of a player, player's performance in different game situations and geographical location of the fields/stadiums, a weighting factor is dynamically generated for each performance parameter (Step 406). Body of work refers to a player's overall performance over a longer period of time, such as a season or multiple seasons. The parameter takes into account the player's consistency and durability, as well as their ability to perform well against a variety of opponents and in different situations. Results of performance refers to a player's performance in individual games or events. It can include factors such as the player's statistical performance, their impact on the outcome of the game, and their ability to perform under pressure (e.g. hitting in clutch situations).
Game situation refers to the specific circumstances of each game or event, such as the score, inning, outs, and baserunner situations. It considers how well the player performs in different situations, such as hitting with runners in scoring position or pitching in high-pressure situations. Geographical location refers to the location of the game or event, such as whether it was played at home or on the road, in a hitter-friendly or pitcher-friendly ballpark, or in a region with different weather or climate conditions. It takes into account how well the player performs in different environments, and whether their performance is affected by external or environmental factors such as altitude or humidity.
The weighting factors are dynamic in nature and can vary among the players and can also vary for the same player and over the course of time. Table 2 provides exemplary weighting or scaling factors provided to the performance parameters in accordance with one embodiment of the present invention.
Then, in the next step, each parameter score is normalized using an average of the particular parameter across all or some of the baseball players (Step 408). It should be noted that Step 406 and Step 408 can be interchanged during the execution for generating a normalized score for each player. For normalization, below table shows the formula used.
Then, a K-vector or any other similar machine learning model is used for calculating a player's overall rating and ranking (Step 410). Overall rating is calculated using the formula:
Overall rating: f(w1x1+w2x2+ . . . +wn*xn);
Certain terms are used throughout the following description and claims to refer to particular features or components. As one skilled in the art will appreciate, different persons may refer to the same feature or component by different names. This document does not intend to distinguish between components or features that differ in name but not structure or function. As used herein “baseball players' ranking system”, “ranking system”, and “system” are interchangeable and refer to the world baseball rating system 100 of the present invention.
In this regard,
Notwithstanding the forgoing, the world baseball rating system 100 of the present invention can be of any suitable configuration as is known in the art without affecting the overall concept of the invention, provided that it accomplishes the above stated objectives. One of ordinary skill in the art will appreciate that the world baseball rating system 100 as shown in the FIGS. are for illustrative purposes only, and that many other configuration and design of the world baseball rating system 100 are well within the scope of the present disclosure.
Various modifications and additions can be made to the exemplary embodiments discussed without departing from the scope of the present invention. While the embodiments described above refer to particular features, the scope of this invention also includes embodiments having different combinations of features and embodiments that do not include all of the described features. Accordingly, the scope of the present invention is intended to embrace all such alternatives, modifications, and variations as fall within the scope of the claims, together with all equivalents thereof.
What has been described above includes examples of the claimed subject matter. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the claimed subject matter, but one of ordinary skill in the art may recognize that many further combinations and permutations of the claimed subject matter are possible. Accordingly, the claimed subject matter is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.
The present application claims priority to, and the benefit of, U.S. Provisional Application No. 63/341,146, which was filed on May 12, 2022, and is incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63341146 | May 2022 | US |
