As professional sports lean ever more heavily on data and analytics, the contest between experience and analytics is intensifying.


Today’s game is quants versus managers, and the outcome may go into overtime.

What’s at stake? Seemingly everything: trophies, revenues, funding and fans, not to mention the sheer thrill of victory, particularly when the game is elite professional sports. Where managers’ and owners’ “gut” once ruled, analytics insights are fast becoming a standard part of a coach’s playbook.

Even so, two recent sports examples showcase how difficult it is to balance analytics with experience, even with big investments in analytics.

In February 2014, The Wall Street Journal ran a story that details “one of the greatest comebacks in sports history:” Oracle Team USA’s dramatic win against Emirates Team New Zealand in the America’s Cup competition that took place in San Francisco Bay between the Golden Gate Bridge and Alcatraz Island:

Largely because of team owner Larry Ellison, the founder of software giant Oracle Corp. and one of the world's richest men, Oracle had all the advantages conferred upon the incumbent, plus some. The 11 sailors were a collection of international superstars. The engineers who designed the yacht and the programmers who built the software used to plot strategy had no peer. Oracle's computer simulations suggested the AC72 — which cost at least $10 million to build — wasn't just the better boat in the final, it was the fastest sailboat ever to compete for the Cup, capable of 48 knots, or about 55 mph.

Team USA was predicted to out-sail New Zealand, even upwind. But, with the America’s Cup going to the first team to win nine races, Team USA was getting its tail whipped by Team New Zealand through the first seven.

Oracle Team USA’s problem was tactical: the data was telling Skipper Jimmy Spithill to do one thing — sail at a tighter angle to the wind — and his experience was telling him to do something else. Finally, in the eighth race, “with his team's prospects getting dimmer by the hour,” according to The Wall Street Journal, “Mr. Spithill decided it was time to stop obeying the computers and start thinking like sailors.”

In a stunning upset, Oracle Team USA won the America’s Cup 9 to 8.

Across the Atlantic, where football (and not the American version) rules supreme, another sort of upset is occurring: Despite it being one of the last holdouts in major sports, more resources are being pumped into football data and analytics than ever before. And players are being measured on every conceivable move. With some very clever outcomes, according to an article in the NewStatesman that details a finding by Manchester City’s data analyst that inswingers — kicks that curve in toward the goal — are the most winning kicks in the league. The team manager, however, felt that outswingers — which swerve away from the goal — were the killer kicks. When Manchester’s manager capitulated to the analyst’s findings, the team scored 15 goals from inswinger corners (the most in the Premier League). And it won the title.

The results from analytics wins like Manchester’s has been summed up by The Guardian, which points out that there is a clear power shift taking place at some football clubs:

At a time when the average tenure of a Premier League manager is just over one year — seven have already been sacked this season — the idea of entrusting all elements of player recruitment and long-term strategy to the manager is anachronistic. That certainly seems to have been the conclusion of the owners at Manchester City and Liverpool, as well as a club such as West Bromwich Albion, which shares power between the manager and a director of football, or sporting and technical director as they now call the position.

But, as in the example with Team USA, that doesn’t mean the final point has been played in the game of quants versus managers.

In his report, Analytics in Sports: The New Science of Winning Thomas Davenport, author, professor (at Babson College), researcher (at the MIT Center for Digital Business) and co-founder of the International Institute for Analytics, writes that the use of analytics in sports is not without its challenges.

“Foremost among them is the traditional culture of many teams… Even when considerable data and analytics are available to support key decisions, they may not employ them over their intuition and experience. In short, demand from key decision-makers for sports analytics is considerably less than the supply of data, technology, new metrics, and analytics.”

It takes, in some cases, a head-turning event — a big win like Manchester’s or pressure from competitors — to get analytics fully in the game. And once deployed, a continuous cycle of innovation is required to beat those competitors who are also utilizing analytics. According to Davenport:

When most fans think of analytics in sports, they think of their use to enhance team performance: to select the best possible players, field the best possible teams, and make the best possible decisions on the field or court…. These analytical approaches are still accepted, but they rarely confer any sustainable competitive advantage, unless the team is continually pursuing new approaches and insights.