Professional sports management and decision-making have a long history of adhering to practices of past tradition and "gut feeling." Deciding which players to trade, draft, train, and which strategies to use during gameplay is traditionally less about rigorous statistical or numerical analysis and more about the wisdom and collective experience of the managing team. It wasn't until Oakland Athletics General Manager Billy Beane successfully implemented "sabermetrics" (the use of statistical analysis of baseball records, especially when evaluating baseball players) that sports business intelligence showed its face in professional sports.
In his 2003 book (and the subsequent movie) Moneyball, Michael Lewis tells the story of Billy Beane's use of statistics to revolutionize the practice of baseball player evaluation. Rigorous statistical analysis demonstrated that on-base percentage and slugging percentage are better indicators of success than traditional methods of evaluating baseball players. They found that players with these qualities were often undervalued and that they could trade these types of players at a fraction of the cost that other teams were paying. The Athletics looked and played unlike other teams in Major League Baseball at the time, but the methods that led to their success became contagious, changing the face of baseball and professional sports itself.
In 2017, most professional sports teams have analytics experts as part of the staff. Teams will often pool data from scout notes, digitized statistics, and many other sources, prepare it, and store it in a central repository. Next, a team of analysts will conduct various forms of exploratory and structured statistical analysis that will inform managers of which players to draft, trade, and focus on. Here are a few ways in which data sports business intelligence is being used in professional sports.
1. On-Field Sensors
On-field sensors are collecting live data from the field during games. And while a lot of sports leagues are hesitant to adopt sensors for making calls that a referee normally would, the technologies are beginning to be widely implemented.
Companies are starting to use various methods to collect data, such as radio-frequency identification (RFID) tags that attach to equipment to track movement, distance, speed, and other metrics during the game. This data can be used either during the game to inform coaches of the objective performance of their players or afterward to inform future strategic decision-making. New sensor technologies have birthed entirely new metrics that arguably are just as valuable as existing ones. One example of this is MLB's use of Statcast, a method that allows tracking of spin rates of baseballs instead of just the traditional measure of velocity.
2. Wearable Technology
Wearable technologies can obviously help players and trainers stay aware of fitness targets and progress, but wearable technologies can be also be used to track, prevent, and detect injuries in players.
Data collected over the long-term can be used to set baselines for player performance. Deviations from these baselines and abnormal patterns within the data can indicate injuries or other causes of poor or altered performance to coaches and trainers. To be able to conduct these sorts of analyses when it matters, teams need to establish a good line of communication between data experts and managers. Brian Burke, lead analyst and founder of the website advancedfootballanalytics.com thinks that sports teams will "enter a state where there's more data than analysis. Teams need people to make sense of it and find inflection points."
3. Drafting Better Teams
Billy Beane of the Oakland Athletics has clearly demonstrated how statistics can be used for drafting better teams. Today, in the age of data science, sports teams draw on much more data using much more advanced techniques. Teams may develop their own methods for scouting or use the services of specialized companies.
Take basketball, for example. Teams are now collecting film and statistics of players and compiling it into a central database. Film from basketball games is annotated so that examples of a player's skills or aggregated statistics are easy to look up. With sports BI, teams can, for example, identify a player who has a high number of assists and subsequently view videos which exemplify these skills from actual gameplay — all with the click of a button.
4. Getting Fans Involved
Increasingly, data and statistics are not just for experts and sport team managers. Fans are conducting their own analyses, as well, in order to enhance the entertainment experience. Alternatively, these insights are used on popular sports-betting websites. The enthusiasm surrounding analytics has spurred sports leagues to figure out ways in which to engage fans using statistics and new forms of data.
Taking it a step further, it is not uncommon for franchises to hire data-savvy fans that have proven themselves online. Bryan Colangelo, former General Manager and President of the Toronto Raptors, has said that it is wise to hire data analytics specialists for franchise offices.
"There are mountains of opportunity in analytics now. If you're not spending $250K and having two to three people dedicated to it full time, you're probably too light on it."
The benefit of hiring fans for sports analytics position is that they already speak both the languages of sports and statistics. These skills make data-savvy fans the perfect candidates for translating data analysis into actionable coaching and team management strategies.
What's Next for Sports Business Intelligence?
As "data culture" works its way into the world of professional sports, sports management teams are going to increasingly adopt the cutting-edge technologies and trends seen in other industries. Sports management teams will be interested in the heaps of data that will be created by remote sensing devices worn by prospective players. Data will be gathered during training and actual gameplay. It will be used to create performance profiles for players. This data holds a lot of value to recruiters and as a result will be monetized. Collegiate athletics teams and individual sports franchises themselves will sell this data to recruiters and a market of big player data will be born.
Another likely trend will be the use of augmented analytics to help managers consume data and conduct analyses on the fly without the need for experts. The busy schedules of analysts and decision-makers preclude them from being available for each other at all times. Technologies like personal assistant bots will be available at any time and be able to answer analytics related questions posed by managers. Naturally, there are going to be limitations to these systems, and human experts will be always needed for interpretation and more complex analysis.
The need for timely and actionable information in sports franchises make it an industry that is going to benefit immensely from the use of analytics. With the amount of capital that franchises have at their disposal, many will be investing in the research and development of new technologies. In time, professional sports could be leading the world of analytics.