Sport is just a branch of the entertainment industry. As such, I don't expect that big data and analytics (a la Moneyball) will play a more decisive role in athletic competitions than they do in, say, determining whether Broadway actors exit stage right or stage left after the climactic scene. Under any future scenario, the trained professionals who perform for us out on the field or in the arena will continue to let their skills and intuition guide their in-the-moment decisions. The same applies to the people who recruit, hire and manage them.
It's best to put Moneyball in the larger media and entertainment perspective. The analytical muscle behind a sports team will focus on financial performance first and foremost, and, to the extent that superior on-the-field performance contributes to the bottom line, that as well. From a bottom-line standpoint, the central concern of any sports team is how to boost ticket sales, concessions, TV viewership and other revenue-producing activities. That depends on the team's success in improving fan engagement and experiences. And that, in turn, depends both on the usual marketing, advertising, promotions and pricing decisions, and on various controllable and uncontrollable factors of the overall fan experience (such as whether a good team is being fielded, whether the stadium is well-designed and maintained, whether the weather and traffic cooperate on game day).
Sports teams, like any businesses, will rely on advertising, marketing, promotions and so on to get their message out and engage with fans. To further those ends, they will call on the skills of predictive modelers, social listening analytics professionals, attribution specialists, microsegmentation analysts and other specialties to determine whether they are likely to maximize sales, revenues, profits, satisfaction and so forth. The results of statistical and other analytical models will be used for decision support by various stakeholders, including but not limited to the back-office and field-management staff.
To get a sense for the decision-support potential of data analytics in the sports industry, I recommend a recent article by Lauren Brousell. Her discussion touches on the following decision scenarios where these technologies, using both established and emerging approaches, can prove transformative for various sports stakeholders:
In the stands. Fans have access to a wide assortment of sports websites to help them check fresh and historical statistics. They can do this while attending the game or anywhere there's mobile access. The statistics-driven decisions that this might support might often be wagers. Or they might simply involve settling an argument with the guy one row up in the cheap seats. Also, if sports teams choose to provide mobile access to the appropriate data, fans at live events could check current metrics on concession and bathroom wait times. In so doing, they could better plan their between-inning or halftime visits to those ballpark amenities.
On the field. Referees and umpires might use analytics to guide and double-check their decisions before they are final. For example, Major League Baseball has installed Pitchf/x technology in all 30 MLB stadiums to track pitches during games. This enables more accurate determination of strike zones. However, the technology cannot be used to the fullest until MLB changes its rules to let umpires consult the system on the field to help them decide how to call specific pitches.
On the sidelines. Data analytics could influence coaching decisions during game time, to the extent that field managers choose to equip themselves with mobile devices. Likewise, there's a potential for wearable sensor technologies to be worn by athletes or embedded in their on-the-field gear. These devices might feed real-time data (like speed, heart rate, hydration, breathing, fatigue and pain) to managers, coaches, trainers and physicians. This rich performance data could help management make the call in terms of who to play, who to bench and who to put on the disabled list.
At the box office. Data analytics can give teams insights into the factors that influence specific fans' decisions to buy season tickets versus individual game tickets, or to skip the in-person action altogether in favor of watching on TV, smartphone or tablet. Also, teams might be able to leverage social sentiment and survey data to determine whether specific segments of the fan base are more likely to attend games at specific times of the day, or days of the week.
- In the back office. Data analytics can also, per the Brad Pitt and Jonah Hill characters in the movie "Moneyball," be used to guide decisions to draft, trade, recruit, promote and dismiss players and coaching staff. As the article notes, a 360-degree rich-data portrait of an athlete's record as well as predictions of their their future performance can be the general manager's ace in the hole during off-season contract negotiations.
Whether the sports world truly leverages these technologies to the fullest depends on changing ingrained habits and practices, both on the field and off.