Salary is not used as a factor because of differences among sports. For special situations (esports and China) we used two additional categories: other social media - for when the athlete was more relevant on a platform outside the Big Three (Twitch, for example, although no gamers made the top 100) - and, in China's case, Baidu search score, because Google is blocked there. The analysis includes five categories: endorsement money, Twitter followers, Instagram followers, Facebook followers and Google Trends score. The data for each athlete in the pool was then fed into a formula created by ESPN director of sports analytics Ben Alamar that weighs athletes' endorsements, their following on the social media Big Three (Facebook, Twitter and Instagram) and Google search popularity, producing a comparative ranking system. (You can find the 2016 World Fame 100 here.) We also sought input from ESPN journalists around the world, including colleagues in our bureaus in Australia, South Africa, Argentina, Brazil, China, India, Mexico and the United Kingdom. We started with Forbes' annual list of the highest-paid athletes and expanded the pool from there using a variety of domestic and international sources to make sure we didn't overlook any legitimate candidates. The ESPN World Fame 100 is our annual attempt to create a ranking, through statistical analysis, of the 100 most famous athletes on the planet.
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