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, in 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 & 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 (e.g., 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 business, 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 and 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.
You might say this is big-data-driven “Moneyball” taken to the next logical plateau. 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 data analytics, using both established and emerging technologies, can prove transformative for various sports stakeholders:
- In the stands: Fans have access to a wide assortment of sports websites to help them, either while attending the game or anywhere that they have mobile connectivity, check fresh and/or historical stats. The decisions being supported might be wagers or simply to settle an argument with the guy own row up in the cheap seats. Also, if sports teams choose to provide access to the data through mobile apps, fans at the live events could conceivably check current metrics on concession and bathroom wait times, and thereby plan their between-inning or halftime visits accordingly.
- On the field: Referees and umpires could conceivably use analytics to guide and double-check their decisions before they are final. For example, Brousell’s article notes that 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. Still, the technology cannot be used to its full potential until MLB changes its rules so that umpires can consult the system on the field to help them decide how to call specific pitches.
- On the sidelines: Data analytics could conceivably influence coaching decisions during game time, to the extent that field managers choose to equip themselves with mobile devices on the field. Likewise, there’s a potential for wearable sensor technologies to be worn by athletes or embedded in their on-the-field gear, feeding real-time data (e.g., speed, heart rate, hydration, breathing, fatigue, pain, etc.) to managers, coaches, trainers, and physicians. This rich performance data can help them collectively make the right calls in terms of who to play, who to bench, and who to put on the disabled list. Interestingly, some MLB teams are already using rich performance analytics to drive play-by-play defensive shifts, according to this recent Wall Street Journal article.
- At the box office: Data analytics can give teams insights into the factors that influence specific fans’ decision to buy season tickets vs. individual game tickets, or to skip the in-person action altogether in favor of watching on TV or on their smartphones and tablets. 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. Personally, I expect that this cultural shift will take place with astonishing speed across most sports. Much of the change will come from the simple fact that the Millennials are increasingly on the playing fields, in the back offices, and in the crowds. They’ve grown up surrounded by data-driven applications in all other spheres of their existence.
“Moneyball” mania will also be stoked by the fact that data analytics is a proven performance-enhancer. As we all know, professional athletes will always go for that.