Blackjack pro shows business leaders how to make smarter decisions

When Jeffrey Ma was asked by the Portland Trail Blazers to help the team make more data-driven decisions, he knew there would be ups and downs along the way.

The professional Blackjack player – the M.I.T. grad who inspired the Hollywood movie 21 and the novel Bringing Down the House – was used to taking risks. He had once found himself $100,000 in the hole after a couple of unlucky hands. But he later rebounded to win that money back, plus another $70,000.

Jeffrey Ma talks about his work with the Portland Trail Blazers.

Now Ma runs Citizen Sports, a consultancy for professional sports teams. He was invited by Kevin Pritchard, currently general manager of the Trail Blazers, to create an analytical model of predictive metrics for the NBA.

“That relationship didn’t come without friction,” Ma recalls. “At one point, Kevin stopped the meeting and said, hey, how can we make your numbers look a little more like our numbers?

But Ma countered that his job wasn’t to agree with the team’s coaches. It was to provide an objective viewpoint based on hard information. He’s still advising the team, in addition to the NFL’s San Francisco 49ers.

“Analyzing things from a statistical standpoint is going to tell you things scouts can’t,” he says. “Your scouts can’t watch every game and don’t think in terms of numbers.”

Ma’s message, shared at a Toronto event hosted by the SAS Institute, has a wider implication for the business world: Don’t ignore data that’s being collected and analyzed by your company. Use it to your advantage.

The business world is just scratching the surface of analytics, says Pat Finerty, vice-president of alliances and business development at SAS. There are barriers in the way of unlocking more value from data. Resistance to change is one of them.

“People who’ve found success by doing things the same way as others are hesitant to change,” he says.


Many firms have a central group of workers who focus on data analytics. But all too often that department’s role is seen as little more than number crunching — a facility where other employees can make an inquiry and expect an answer will be automatically trotted out.

Too often business leaders are looking to corroborate what they already know, not learn about what to do next, says Lori Bieda, vice-president of client insights at the Canadian Imperial Bank of Commerce (CIBC).

“When you start to look at what’s actually happening and the trends apparent in your data, you’ll find things that you’re not even aware of and start doing things differently,” she says.

Knowledge workers should have a different mindset – one where they are ready to set aside their ego and accept that data might prove them wrong from time to time, Bieda adds. Having some humility helps with changing course when new information presents itself.

Crafting the right questions to drive business value can also be a challenge. In an information saturated world, it seems that everything delivers metrics and there are a myriad options for returning analysis.

Sports analysts have fallen into the pointless data trap before, Ma says, when trying to prove the “hot hand” theory.

“The idea that someone in basketball makes a bunch of shots, so they’ll make even more shots,” he explains. “That one shot is predictive of the next shot being made. But statistically, that’s hard to prove.”

Since it can’t be proven or disproven, it’s something better left a mystery, he adds. Analysts who tell coaches and players that there’s no such thing as a hot hand tend to lose credibility. Many athletes believe their confidence levels have a big impact on their game.

Businesses built on making data-driven decisions are suddenly finding there’s just too much to track, Finerty says. But the data explosion can be dealt with in the business world.

“Although there’s been an explosion of data, it’s old school math that’s solving this problem,” he says.  Optimizing, text mining and content categorization included.

Centralized analytics departments must break free of the service provider mindset and starting taking on more of a leadership role, Bieda says. Employees with a systematic point of view can make good consultants.

“They’re not waiting for someone to ask, they’re using the data proactively and profitably,” she says.

Jeffrey Ma gives a quick lesson in how to count cards.

Organizations that want to become more data driven in their decision making must make the numbers as simple as possible to understand, Ma says. Try testing analytical models with staff who are most likely to doubt them and gauge their reactions.

“Don’t tell people that you are smarter than them, or the numbers are smarter than them,” he says. “They’ll withdraw and not want to be part of the process.”

But also don’t be afraid to stand up for an objective point of view. Sometimes when all the chips are down on the table, you’ve got to take a risk.

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