From the earliest days of advanced sports analytics, the field has been dominated by baseball statistics “stat” geeks and number crunchers. While the origins of record keeping were just for games and simulations, now the use of advanced statistics and data analytics has become an integral part of the MLB. Other leagues use similar sports analytics methods to record and analyze data, but almost all techniques stem from work done by baseball fanatics and analysts alike. Baseball served as the birthplace for data analytics in sports and continues to drive the discovery of new uses for sports analytics.
Keep Your Eye on the Ball
One of the most innovative and exciting recent developments in baseball has been the ball tracking technologies installed in every stadium across the MLB. Teams and players have always had a general sense of pitch location and type, but with the implementation of sports analytics systems, like PITCHf/x, it’s an exact science. These data analysis systems use multiple cameras placed throughout the ballpark to track every detail imaginable about each pitch thrown. Then the data is analyzed and stored, allowing teams and fans to see the ball’s location, speed, rotation, and movement. Teams then use this information to see which specific combinations of pitch type and location are hurting a pitcher’s performance, and which combinations are getting positive results. A team may notice that one of their pitchers has given up more hits than he usually does in his last few games, so they begin to look for answers in the data collected by the sports analytics system. After looking at the data, they may see that the pitcher has thrown more low fastballs than he usually does, thereby giving up more hits. The team’s coaches can then adjust the pitcher’s tendencies accordingly, hopefully garnering positive results.
Other Uses for Sports Analytics
The league took advantage of the pitch tracking system’s success and went on to install a similar tracking system to record information from every hit ball. Building off the first system, it also uses cameras to capture batted ball speed, elevation angle, contact point, and field direction. Sports analysts, for example, can now pair the information collected from traditional statistics, which focus on the outcomes of at-bats, and these physical data points to see why more home runs are being hit.  The sports analytics systems may also be able to combine hitting and pitching data to determine how balls are being hit against certain pitch speeds and locations.
Not satisfied with just tracking the ball, the MLB also uses sports analytics to track the location of every player on the field.  This system collects information on player speed and acceleration when on the bases or in the field. With further analysis, the difficulty and probability of a player making a catch can be determined, allowing teams to see which players are the best defenders in baseball. Like the other tracking systems, this data can be used in conjunction with that from other data analytics systems to get a complete understanding of how certain pitches are hit, and how players react to them.
Off-field Data Analytics
The most important part of any sports team is usually the players, but the only way that teams can afford to keep their players on the field is with the patronage of their fans. Because getting fans in the park and keeping them there is the primary goal of any team trying to turn a profit, baseball teams utilize big data just like companies outside of the sports world. These teams use sports analytics to improve the fan experience, before, during and after the game. For example, the Milwaukee Brewers analyze every email they receive from fans to find patterns in fan age and income to better target ads and promotions to their fan base.  Other teams record information on everything fans do inside the stadium from what they buy to where they buy it. Teams can also use geo-mapping to inform fans where the nearest bathrooms or concessions are and where the shortest wait times are. The Boston Red Sox use data analytics to change their pricing models to adjust prices based on the popularity of certain games during the year.  By moving away from static ticket pricing, the team can get more new fans into the stadium and potentially turn them into returning fans.
The exciting use of data analytics in sports continues to increase and its powerful impact is being felt in all areas from athletes to the fans. If you’re interested in utilizing data analytics in a sports career, pursuing a master’s degree in data science may be the right career move for you.
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