Stephen Curry, the Golden State Warriors, and the Power of Analytics at Work

A commitment to data-driven decisions is transforming the management of sports. Other industries can — and should — follow suit.

Whether or not their 2016 season ends with a second consecutive NBA championship, the Golden State Warriors are making Silicon Valley proud. They broke the record for regular season wins with 73. They are headlined by Stephen Curry, the dynamic and eminently likeable two-time MVP. They have established themselves among the league’s elite franchises.

Like the “unicorns” along Highway 101, the Warriors have done it all with a deep organizational commitment to data-driven decision making – both on the court and as a business. The three-pointers Steph and running mate Klay Thompson hoist seemingly without abandon are actually grounded in troves of evidence supporting the shot’s relative value. Meanwhile, the business side of the organization is leveraging fan data to more effectively drive ticket, sponsorship, and merchandise revenue.

The Warriors are not the only team pioneering the analytics revolution in sports. Organizations across an increasing number of sports and levels (professional, college, and high school) are capitalizing on data to gain a competitive edge. Indeed, few industries have implemented data-driven decision making as successfully as sports.

What learnings from the sports analytics revolution are applicable to the broader management community? For those seeking to become more data-driven in approach, consider the following:

Adopt a measured mindset. In the simplest of terms, analytics refers to quantitative tools that help organizations find, interpret, and use data to make better decisions. Sports teams understand that other factors such as previous experience and even gut instinct influence the decision-making process. In this context, analytics is a single input, albeit a potentially powerful one.

For any business – sports or otherwise – additional information on the decision in question should be embraced. It’s up to the decision maker to determine the appropriate mix of inputs to minimize risk. More often than not, quantitative data should play an instrumental role.

Focus on the objectives. In the sports industry, the objectives are straightforward and unwavering. Teams are trying to win. Therefore, any decision on which players to sign, trade for, or draft are analyzed through this prism, as is which coach and general manager to hire. On the business side, the goals are typically centered on increasing revenue and enhancing the fan experience.

With clearly defined goals — on which all in the organization are aligned — the use of analytics is more purposeful. The danger for some organizations is getting too caught up in the math and methodology of analytics and losing sight of the business objectives. Staying focused on the task at hand is critical in all disciplines, but most especially when using analytics to make better decisions.

Invest in data collection technology. The sports analytics revolution does not happen without innovation in data collection technology. For starters, the player tracking possibilities are more advanced than ever before. For example, the six SportVU cameras atop all 30 NBA arenas catalogue 25 frames per second, enabling NBA teams to analyze detailed information about offensive and defensive lineups. The NFL, MLB, and NHL have similar technology. In addition, teams are taking advantage of new wearable technologies to optimize the health, wellness, and conditioning of athletes.

For the broader business community, the implication is clear: Move beyond traditional research methods and explore new ways to collect relevant data. Video and sensors are two opportunities mentioned here. Depending on your industry, there will be different capabilities available. And if not, push to find them.

Practice effective data communication. The mounds of data that sports organizations are now collecting not only need to be cleaned and analyzed, but also translated into meaningful information that key stakeholders (many of whom are not data scientists) can understand and act upon. For example, coaches don’t have time to pore over vast amounts of data. Instead, they need to understand the implications of the data and then make a decision quickly. Telemetry Sports has a product for college baseball coaches called Diamond Charts, which tracks where on the field batters hit the ball. During games, with a Diamond Chart in hand, coaches can make decisions on defensive shifts based on the batter’s tendencies. These charts are clear and consistent and deliver the information coaches have come to rely on for in-game strategy.

Data does not mean anything sitting on a spreadsheet. It’s critical to have people and tools that can communicate the data clearly, concisely, and with context to help stakeholders make more effective decisions. In addition, understanding stakeholder needs and tailoring the communications accordingly remains an indispensible skill.

In the end, the ability of a sports team or other business to become data-driven rests largely on the leaders of an organization. Businesses need people with: a measured mindset; an understanding of how analytics can help achieve business objectives; data acumen to make the appropriate investments in technology; and the communication skills to effect change based on what the analytics say. Not unlike their search for the best talent on the court, sports teams have aggressively sought out the best, most versatile data scientists. Other businesses should follow suit.