Big data is popping up everywhere. Every industry is starting to discover its usefulness, and it is improving business right and left. It's easy to imagine a place for big data analytics in the medical industry, or in the world of sales, but can big data be relevant in sports, too?
One of the best-known uses of big data in sports was documented in a book (and later film), Moneyball. Billy Beane, the general manager of the Oakland A's, used “sabermetrics” to assemble a competitive baseball team in spite of the team's lower-than-average revenue. According to Wikipedia, sabermetrics “is the empirical analysis of baseball, especially baseball statistics that measure in-game activity.”
Big data analytics tools allowed Billy Beane to see which players could contribute in a big way without costing the team an arm and a leg. He was able to successfully put together a team with players who had the potential for success but were undervalued by most baseball clubs, and were, therefore, cheaper to pick up. This big data analytics approach was so successful, most Major League Baseball teams have since hired full-time sabermetrics baseball analyst to help determine draft picks and trades. Billy Beane single-handedly proved that big data works for baseball.
Helping the players play
Big data visualization not only helps teams succeed, but it can help individual athletes achieve peak performance. Motion tracking technology has been in use for quite a while, and it can now be used to help professional athletes. Cameras can be used to track players during games and practices, which can lead to the collection of millions of data points. Specialized scheduling algorithms can be created for each sport and for each team to measure how effectively and efficiently a particular player is performing during a given game or practice. This can help coaches determine how to best utilize a player.
Some teams and trainers are even using wearable technology to gather data about an athlete. With precision data, team managers, trainers, and medical staff can monitor stress levels when an athlete is playing or training to help cut down on injuries. Having a big data analytics tool can give athletes the information they need to improve their game and stay healthy.
Sports organizations need to be in tune with their fans. Sports teams can collect data about fans' feelings to improve their experience and make them feel like they're part of the team. What do they think about how the team is performing? How do they feel about players that the team has acquired or let go?
One of the best ways to find data about fans is to check out social media websites they frequent. The better a team knows their fans, the better experience they can offer. Data-savvy teams can also gather data at the stadium. If fans use social media to check in on arrival, the data can be analyzed to determine how far fans are willing to drive to see a game. That can inform how the organization will promote upcoming games and fan activities.
There is already technology that allows you to order a drink via an app and pick it up when you arrive. Perhaps in the future, fans will be able to order food and drinks before arriving at the stadium and have them delivered to their seat. This, in turn, provides more data so a sports team can further personalize a fan's experience at a sporting event. Big data software can help fans enjoy a game on a new level.
There are definitely many ways big data analysis can be used in sports. Big data can be used to change how the game is played, how athletes are taken care of, and maybe one day enhance the fan experience even more. Big data can give sports teams the competitive edge just like it can for any other business. I have talked about 3 critical ways big data is used in sports. Are you in a sport field that is in need of big data? Leave me a comment, I can't wait to hear from you.
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