The Athlete Revenue Analytics API is a testament to the intersection of sport and finance, providing a comprehensive and nuanced view of athletes' financial trajectories over the years. This dynamic interface provides seamless access to a comprehensive database of athlete earnings over more than three decades. By encapsulating the financial evolution of sports stars, the API provides information on sponsorship deals, salaries, earnings and other revenue streams that have contributed to their impressive wealth.
With a RESTful architecture, the API features a standardized interface that allows users to seamlessly interact with the vast repository of athlete earnings data. Users can retrieve video data in JSON format. This direct interaction ensures accessibility to a wide range of users, regardless of their preferred programming language.
In addition to total revenue, the API provides a detailed breakdown of how athletes accumulate their wealth. This includes sponsorship income, salaries, winnings and other revenue streams. This granularity allows for a nuanced analysis of the financial strategies employed by athletes to diversify their sources of income.
The API encompasses data from 1990 to 2023, allowing users to track the financial trajectory of athletes over an extended period of time. This historical context is crucial to understanding the factors that have influenced athlete revenues and the changing dynamics of the sport economy.
In conclusion, The Athlete Revenue Analytics API API opens a door to the financial narratives of athletes, unraveling the stories of their wealth accumulation over the years. With its robust architecture, real-time updates and historical data, the API allows users to explore and understand the financial dynamics that have shaped the world of professional sports.
It will receive parameters and provide you with a JSON.
Sports Analytics Platforms: Integrate athlete earnings data for in-depth analytics, enabling insights into the financial and performance aspects of athletes.
Financial News Outlets: Provide real-time updates on athlete earnings, contributing to sports finance reporting and financial news coverage.
Fantasy Sports Applications: Enhance fantasy sports apps by incorporating athlete earnings data, offering users a unique perspective for player selection.
Brand and Sponsorship Analysis: Analyze the impact of endorsements and sponsorships on athletes' overall earnings, aiding marketing and branding strategies.
Educational Platforms: Integrate historical earnings data into educational platforms to teach students about the financial dynamics of professional sports.
Besides the number of API calls, there is no other limitation.
To use this endpoint you must indicate a year between (1990 and 2023) in the parameter.
Top ten by year - Endpoint Features
Object | Description |
---|---|
year |
[Required] |
[{"rank":"1","name":"Tiger Woods","sport":"golf","nationality":"USA","earnings* ($ million)":"1050","previous year":"1"},{"rank":"2","name":"Floyd Mayweather","sport":"boxing","nationality":"USA","earnings* ($ million)":"650","previous year":">10"},{"rank":"3","name":"Kobe Bryant","sport":"basketball","nationality":"USA","earnings* ($ million)":"480","previous year":"2"},{"rank":"4","name":"Phil Mickelson","sport":"golf","nationality":"USA","earnings* ($ million)":"460","previous year":"6"},{"rank":"5","name":"David Beckham","sport":"soccer","nationality":"UK","earnings* ($ million)":"437","previous year":"5"},{"rank":"6","name":"Roger Federer","sport":"tennis","nationality":"Switzerland","earnings* ($ million)":"430","previous year":">10"},{"rank":"7","name":"LeBron James","sport":"basketball","nationality":"USA","earnings* ($ million)":"428","previous year":"6"},{"rank":"8","name":"Manny Pacquiao","sport":"boxing","nationality":"Philippines","earnings* ($ million)":"420","previous year":"6"},{"rank":"9","name":"Eli Manning","sport":"American Football","nationality":"USA","earnings* ($ million)":"399","previous year":">10"},{"rank":"10","name":"Terrell Suggs","sport":"American Football","nationality":"USA","earnings* ($ million)":"383","previous year":">10"}]
curl --location --request GET 'https://zylalabs.com/api/3093/athlete+revenue+analytics+api/3276/top+ten+by+year?year=2010' --header 'Authorization: Bearer YOUR_API_KEY'
To use this endpoint you must indicate a year between (1990 and 2023) and a ranking in the parameters.
Top paid by rank and year - Endpoint Features
Object | Description |
---|---|
year |
[Required] |
rank |
[Required] |
[{"rank":"1","name":"Cristiano Ronaldo","sport":"Soccer","nationality":"Portugal","earnings* ($ million)":"136","previous year":"3"}]
curl --location --request GET 'https://zylalabs.com/api/3093/athlete+revenue+analytics+api/3277/top+paid+by+rank+and+year?year=2023&rank=1' --header 'Authorization: Bearer YOUR_API_KEY'
Header | Description |
---|---|
Authorization
|
[Required] Should be Bearer access_key . See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
To use this API the user must indicate a year and a ranking to obtain the best paid athletes.
The Athlete Revenue Analysis API is a tool designed to provide a comprehensive view of the financial aspects of athletes.
There are different plans suits everyone including a free trial for small amount of requests, but itβs rate is limit to prevent abuse of the service.
Zyla provides a wide range of integration methods for almost all programming languages. You can use these codes to integrate with your project as you need.
Each endpoint returns JSON data containing detailed information about athlete earnings, including rankings, names, sports, nationalities, and earnings in millions of dollars.
Key fields in the response data include "rank," "name," "sport," "nationality," "earnings ($ million)," and "previous year," providing a comprehensive view of athlete financial standings.
The endpoints require parameters such as "year" (ranging from 1990 to 2023) and "ranking" for the GET Top paid by rank and year endpoint, allowing users to customize their data requests.
The response data is organized as an array of objects, each representing an athlete's financial information, making it easy to parse and analyze for various applications.
The GET Top ten by year endpoint provides a list of the top ten earners for a specified year, while the GET Top paid by rank and year endpoint offers detailed earnings for athletes based on their rank and year.
Data accuracy is maintained through rigorous sourcing from reputable financial reports and sports analytics, ensuring reliable insights into athlete earnings over the years.
Typical use cases include sports analytics, financial news reporting, fantasy sports applications, brand analysis, and educational platforms, leveraging athlete earnings data for various insights.
Users can analyze the earnings trends, compare athletes across different sports, and assess the impact of sponsorships and salaries on overall financial success, aiding in strategic decision-making.
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