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Big Data in Sports

If you're a sports fan like me, you might be crunching every number related to the four teams remaining in the NCAA Basketball Tournament. The Final Four is this weekend, which means that many fans, whether they root for Gonzaga, UCLA, Baylor, Houston, or a team that has been eliminated, are still analyzing every detail prior to the big games.


Seeing all of the coverage on ESPN about the tournament, who is going to win, who you should bet on and whether you should bet against the spread or not, it all has me contemplating my short two decades as a sports fan. To think about just how much the coverage of sports has changed since I started paying close attention in the early 2000s. Wow.


Now, for those older, more experienced fans, I can only imagine the changes that they've witnessed over 30, 40, maybe even 50 years. And I'm not just talking about seeing the green field on color TV for the first time. While this was certainly an amazing innovation to how we view sports, so many changes have occurred in the decades that have followed.





Sports coverage in the twenty-first century is different than it was even at the end of the last century. One main difference? Big data.


That's right, what many of us think of as creepy big brother going after our private information also impacts the sports industry in a substantial way. Sports is a money making industry, and more information has allowed it to grow incredibly large.


There is certainly some overlap when it comes to big data use in sports. Taking social media posts, locations, search history, and so much more, professional sports organizations will target advertising for ticket sales to certain individuals. Similar processes are implemented for sports related apparel shopping, sports streaming sales, and more. This is just like online department stores and other businesses perform target marketing.


But even beyond this widespread method, sports organizations take other steps to get ahead. Knowing that putting a winning product on the field is more profitable, scouting has become big business. Scouts in all sports travel to find prospects for their organizations as young as middle schoolers. Scouts and other personnel record stats from even recreational or school leagues, compiling them to determine their best possible next course of action. These stats, often found online now for even high school sports, are recorded for a purpose.


One of the best examples of big data in sports is baseball. There is arguably no other sport with so many sophisticated statistics used to closely evaluate every player in a multi-level minor league system, on top of high school, college, and overseas prospects. Humans could not possibly calculate the significance of all of these stats on their own. In comes big data.


I mean stats like WAR, OBP, ERA, SLG, BA, SB, and a seemingly infinite amount of alphabet soup. All this just to attempt to get the best team together, and the right matchups on the field to provide a winning team for an organization so they can sell tickets and merchandise.


Professional sports teams have always wanted to win, and done their best to do so. But in a digital world, there are so many other available avenues to gain an upper hand. Big data has entered every part of our lives, even the parts we consider an escape. Yes, sports is entertainment, but it's also big business, which mean big data is an inevitable part of the industry's future.

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