Interestingly enough, sports represent one of the best fields to study for those interested in learning data science. With American sport, at the least, data has become a defining part of how teams operate and perform. As a result, leaders in each sport have begun the process of collection more and more data, often to a much more specific level than in the past, to determine exactly why certain teams succeed and which players have the best "advanced stats" to prove their value
The sport I am most knowledgeable on for this process would be the NBA. The league, as it's often referred to as, has embraced data analytics and collection to an extent rarely seen in other sports. They've begun using a technology referred to as Sports VU , which uses cameras behind each backboard in an arena to record all sorts of stats on player movement and their offensive/defensive skills. For example, the NBA now can report how fast different players move down the court, how long they each hold the ball for, and even the distance they travel each game. It offers an incredible opportunity, as this is all very new information, so fans of the sport can use all of it to try and determine which variables most indicate good performance, and can also track the rise of a new great player/ the decline of an older one. It's made all the more interesting because the league just signed a lucrative new TV deal, meaning that the maximum salary available to players has risen drastically as well. What this ultimately means is that the amount of money being thrown around over the next few years will be very high, so teams will have extra incentive to analyze their data as best as they can to find who to sign to their team and what salary they deserve to be offered
The MLB has also recently made data a big part of their approach to success. The Houston Astros, for example, hired GM Jeff Luhnow and director of decision sciences Sig Mejdal, formerly of NASA, and the duo now lead a nine-man sabermetrics staff that includes a medical risk manager and a mathematical modeler. They've focused on "defensive shifts" on balls in play since they began their tenure, and that has had a large effect in how the Astros are run and how they approach the game as a whole. The Tampa Bay Rays have similarly approached analytics with gusto, this time with the desire of finding the best undiscovered players for their team. Since 2008, the Rays have the lowest cost/win of a team with a record over .500, with their $700,000 figure far below the league-leading Yankees and their astronomical $2,400,000/win costs. It goes to show that analytics can help teams find better-fitting players too, as they reveal insights that before would have been impossible to accurately get on all the prospects in the league.