Application of Data Analytics in Sports

In today’s information age, the use of data is becoming increasingly prevalent in the sports industry. There are very few organizations that use analytics as extensively as professional sports. The usage of this data spreads far and wide whether it be to assess player performance, player selection or injury prevention. Although there are several barriers that remain before data analytics is truly integrated with professional team’s core values, there has been evidence that the use of this data can help push team’s in the right direction towards their success when used properly.

The challenges with data analytics in sports is that it is not at the forefront of the minds of executives and general managers who are making decisions regarding players based on performance. There is still some distrust as to how useful this data will be especially when you consider that there are so many different metrics to track in sports. Jim Tobin from SAS stated that “Teams are collecting massive amounts of player performance data - more than they know what to do with”(Tobin 2). Ultimately data will become less useful when there is too much of it available because it is more difficult to interpret that data. The point of the data is to have the sports performance analysts be able to tell a story that summarizes the data so that executives can make decisions based on this information.

You will be hard pressed to find an executive or manager in professional sports who took the job that they have in the front office due to their passion for analytics. The demand for managers and executives is extremely high given the amount of data that is being analyzed throughout various sports leagues. In comparison to some major healthcare organizations, as wealthy as the owners are they do not have the means or resources to make huge investments in the tech and analytical tools given that they place such a big focus on player salaries. In fact, these teams do not come close to having the ability to take care of the infrastructure of those systems to analyze the data. “Professional sports teams are, by and large, small businesses” (Davenport 2). This means that these teams do not have the financial resources to invest in maintaining that data infrastructure. Right now, we are simply scratching the surface of what this data allows us to do although not all sports teams have invested in it wholeheartedly. There are many teams have used it to their advantage and have had success with its implementation.

Player performance analytics has been proven to help aid decision-makers determine some success on the field, but that is not by relying solely on analytics which can lead to some bad decisions. Some of the most highly analytical teams are the Boston Red Sox and The New England Patriots who have made a concerted effort to utilize data to their advantage by assessing the value of players they would like to acquire. On a smaller scale, players like Tom Brady have taken it upon themselves to become a “student of error” by using the analytics to assess his own performance. By understanding the game on this level, he puts himself in a position to find new ways to further his development as a player. When that is coupled with hard work off the field it allows him to reap the benefits of his persistence. Utilizing this data can give smaller market teams an advantage where many would not see one by investing the proper resources into the analytics. This gives them the ability to maximize every dollar that is spent on their players by using metrics to determine their value on the field.

The problem lies when teams rely too heavily on analytics for their decision making. For example, the Patriots typically do a great job finding talent for good value such as Aaron Hernandez, but there are some factors that you just cannot account for through analytics. Some of those decisions do not pan out and Hernandez would be exhibit A given that he’s currently in jail for murder. The value of data is definitely real, but there are tradeoffs that you must account for when using the data. As stated in the article, “Analytics has not and will not replace strong players and good coaching as a recipe for team success, but they have certainly become established as important augmentation for those basic success factors” (Davenport 13). There is more to sports than simply looking at data, but the data gives us the ability to analyze players in various contexts. Making changes because of data on a small scale will help teams make decisions regarding personnel. For example, a player in the NBA could be statistically better with a specific set of players on the court. It isn’t something you would notice as a coach during a game, but you would know if you analyzed the data afterwards. There are so many uses of data, but organizations are trying to find the right balance and figure out what works for their own team success.

Business Analytics in sports is treated as second tier information to executives in the front office. This is mainly due to the fact that these analytics do not contribute to the success of the team on the field. One manager stated that he “felt somewhat like second-class citizens compared to team performance analytics staff”(Davenport 16). This is not at all surprising because a professional sports team’s number one objective is to maintain success on the field. This has not stopped teams from using business analytics and adopting table stakes applications such as ticket pricing, fan loyalty analysis and fan satisfaction metrics. These applications provide opportunities for increased revenue. For example, there are two methods used for ticket pricing which include variable pricing and dynamic pricing. Variable pricing helps teams who are successful and will end up charging more for the tickets because of that success. They also will charge more when they are playing against popular teams. This works well for a team such as The New England Patriots because they can charge more due to the success of the team on a consistent basis. The San Francisco Giants were the first team to implement variable pricing in the MLB and they saw additional revenues of over $500,000. Other teams may prefer dynamic pricing because they base their prices on a variety of factors. This includes team opponent success, league rankings and even the weather. Although business analytics may take a seat to sports performance analytics, they are still valuable in helping create revenue for professional sports teams.

The best method in using analytics in sports is to use a top-down approach in professional sports organizations. In order for the use of analytics to be effective everyone in the front office must be on the same page. If leadership has doubts about the use of analytics, then it will not be sustained or effective in its implementation. The main competitive advantage that teams need is to acquire proprietary data in order to set themselves apart from other teams. There are many ways for teams to utilize data to their advantage, we will see its usage increase in the coming years but still figure out ways to set their teams a part through the way they leverage their data.