Ineffective Math Blazing a Trail in Hockey Analytics

With one blog post, Micah Blake McCurdy moved the hockey analytics community forward.

I think it's amazing that with one blog posts and some nice charts @IneffectiveMath changed the conversation from FenClose to score adjusted — Greg Sinclair (@theninjagreg) April 12, 2015

FenwickClose, which measures unblocked shot attempts when games were within a goal in the first two periods or tied in the third, was the standard possession metric to account for score effects. Its track record predicting playoff success was convincing.

But McCurdy‘s post, from November 13, 2014, was even more convincing. In it, advancing Eric Tulsky’s work here, McCurdy demonstrated clearly and completely: including all shot attempts, regardless of game score, and subjecting them to the formula he devised, was a more accurate and predictive method.

It was a post months in the writing, and years in the making. I spoke with McCurdy on the phone about the process, not only for that post, but for his cutting edge research and data visualization that make him one of the most respected voices in the online hockey analytics community.

McCurdy, born in New Brunswick, now living in Halifax, was passionate about mathematics but only a casual hockey fan until he traveled to Australia to earn his PhD. He said the utter lack of hockey coverage in Australia made him miss the game, and that jumping through hoops to follow the NHL made him more invested.

In turn, watching more games exposed him to broadcasters’ analysis that didn’t set well with his analytical, research-driven mind.

“I couldn’t just hear these things and not research them,” he said. “I remember hearing a stat, something like a team that wins Game 5 to go up 3-2 in a series wins 80 percent of series as if it were impressive. But I looked into it and found that if the games were 50-50, a team leading 3-2 should probably win more like 84% of the time. So teams came back more often than you’d expect.”

He found himself in an uncommon position: having the technical skills to obtain and analyze data, and a passion for hockey.

His strongest influence, however, was not hockey related. The Visual Display of Qualitative Information taught him the value of showing data in a useful way, and data visualization remains a strong suit.

Of course, he read the work of Tulsky, Travis Yost, and Tore Purdy (aka JLikens), but it was Jen Lute Costella‘s dogged, thorough exploration of specific subjects that he found most satisfying.

“Jen explored ideas to death,” he said. If it took three, six, eight thousand words, she did it. She wasn’t content with one season’s worth of data. For me, it became not just how can I understand something, but how can I settle it.”

That approach brought him to the score effects discussion.

“I never understood why we used CorsiClose. Everything kept coming back to the same article, or one comment from Tore, and I had to do a better job.”

For the first years of his work in hockey analytics, McCurdy publicized his work only on Facebook, where the audience consisted of friends not necessarily interested in advanced hockey study. When he joined Twitter, however, he found an interested, responsive community, and it was there he began to ask what people would need to see in order to be convinced that a formula to adjust data for score effects was better than the alternatives.

“I was amazed,” he said, “that I could use Twitter to imagine and predict criticism.”

Therefore, when he published his magnum opus on score effects in November of last year, the response was strongly supportive. But it wasn’t unanimous.

“Nobody was hostile about it,” McCurdy said, “but people were cautious for a while. People were skeptical, asking questions about how I got there, where I got my data. The process of convincing people was slower than it looked. I always believed in the general mantra of ‘don’t throw out data without a good reason.'”

Though predicting the unpredictable playoffs is largely a fool’s errand even for the best metrics, McCurdy felt validated when his score-adjusted formula predicted playoff results ever so slightly better than FenwickClose.

He said he’s constantly learning new angles to the score effects phenomenon. In February at a Washington, D.C. analytics conference, he presented research that demonstrated player deployment is unlikely to be a major factor. That is, coaches aren’t playing their lesser players more often when trailing, at least, not frequently enough to account for such massive differentials in possession.

McCurdy has considered whether coaching systems could be to blame. He cited that the Los Angeles Kings are fighting score effects better than any team in the league. But this piece, from Justin Bourne has made him rethink.

“I’m of the mind at this point it has a lot to do with loss aversion,” he said. “And I think both the leading and the trailing team contribute to the effect. It could be that all teams naturally play at only 80 percent of possible aggressiveness, until they fall behind. So they shed their loss aversion. When winning, players are more likely to chip the puck out of the zone instead of carrying or passing it out. Or maybe their teammates aren’t in positions to receive passes or to support a carry out. That’s why it would take a huge organizational change to make even a tiny change on the ice.”

He continued to say the media could be playing a role. “Players probably are making decisions based on how they’ll be perceived. The guy who scores to make it 4-0 isn’t getting praise in the media. But the guy who turns the puck over to give up a tying goal, different story.”

In addition to score effects study, McCurdy believes the simple question, ‘Which skills are the ones that lead to success’ is one of the next frontiers of hockey research.

“The point of shot metrics is not what happens after them, whether they result in a goal or a save or a block,” he said. “It’s what happens before them. The process. Which is what makes hockey players good. So the next level is determining which are the skills that matter versus the ones that don’t. Identifying specific strengths and weaknesses. Those are things we could gather to talk to coaches about.”

Does that mean he’d welcome the chance to work for an NHL team? He said despite the rash of analytics hirings last summer, there aren’t a lot of jobs available, which means less competition among analytics researchers, and more collaboration—a good thing. Even if there were an NHL job available, McCurdy said his academic background would make him think twice about going silent on Twitter and dedicating all his effort to secretive, behind-the-scenes work.

“I do make a little bit of money doing custom projects for writers,” he said, “but that’s never been the primary motivation. It’s more that hockey is popular and I want to help people understand it. I feel a sense of public duty to do what I can, and when people I respect validate my work on Twitter, that’s very motivational.”