Hockey Confidential Read online

  The Oiler-loving, stat-creating Ferrari, though, had two simple rationales for the nom de plume.

  “I liked Andy Kaufman,” Ferrari said, “and at the time, no one used their real name on the Internet. I still generally don’t. Being a bit older [Ferrari was born in 1967], I guess I’m not comfortable with how kids pour out their whole lives on Facebook and put everything about themselves on the Internet. It’s a terrible idea, I think. I’m a private person. I also had a professional life and I didn’t want people I was working with to know I was a hockey nerd. It’s not something you’re proud of.”

  Ferrari always loved hockey. He played it growing up. He had a deep and abiding passion for the Edmonton Oilers—still does, although the Oilers’ streak of missing the playoffs, eight straight years and counting, has pretty much mutilated his heart. It was originally his fascination with gambling and sports betting, though, that got him into blogging and analytics. That was also, in part, what led him to depart—at least in the public sense.

  “I came from a sports betting background, that was my initial interest,” he said. “I loved hockey, played it, coached it, but until I got into the [sports betting] industry and saw how they looked at the game, I realized I really knew nothing about hockey. All the stuff I thought I knew was wrong. That changed my life.”

  Eventually, though, as much as he enjoyed being a “nerd” and trying, through his site, to come up with ways of understanding how the outcomes of games were determined, it was in his best interest to go underground, to lower his profile.

  “I was hurting myself,” he said. “I was helping to make other people smarter, which is good, but not if you make them too smart. Not if you come from my [sports betting] background.”

  If Ferrari did any media interviews, other than comments he made or dialogue he had on stats-related websites, there’s no record of them. He’s been like a ghost to most. So why do an interview with me in 2014, a few years after he’d stopped blogging regularly and mostly dropped out of sight?

  “I guess I’ve become a bit less concerned about [putting myself out there] because lately, everyone seems to be more open about it [on the Internet],” Ferrari said, noting that he still has no interest in revealing his true identity, other than being comfortable in saying that, as of 2014, he was living in Chicago and working as a trader in the stock market there.

  The man is something of a #fancystats legend, primarily as a bright mind and innovator, but also because of a well-known salty disposition, acerbic online attitude and, of course, his shadowy, anony­mous existence. Even a fellow analytics legend like Gabe Desjardins didn’t know Ferrari’s real surname or that Ferrari had moved from Edmonton to Chicago.

  “I think I know Vic’s real first name and I think I’ve got an idea of where near Edmonton he lives, assuming he still lives there,” Desjardins said before Ferrari gave the interview for this book. “I honestly don’t know his last name or if he still lives where I think he lived or what he’s doing or what he does for a living. He once told me, ‘I don’t do interviews. I’m from a different generation.’ He’s a mysterious man.”

  Mysterious, yes. Some would say even mythical.

  “Mythical?” Ferrari said, laughing. “People can imagine all sorts of shit. I am pretty special, though, don’t kid yourself.”

  That last part was a joke. He laughed hard.

  But when the history of hockey analytics is written, it’s no joke: Ferrari and his Irreverent Oiler Fans website will almost certainly be identified as the cradle of #fancystats civilization.

  This new Corsi concept didn’t exactly take the entire hockey world by storm right out of the gate. It was, however, a topic of much discussion on Ferrari’s website. One of the commenters who frequented the site was Calgary native Matt Fenwick, a mechanical engineer with a passion for hockey and the Flames who, at that time, was living in Lethbridge. He had an ongoing Internet debate with another fan, Cameron Thomson (known by the handle RiversQ), on Ferrari’s site because Fenwick felt strongly that blocked shots should not be included in the Corsi calculation. The two of them would go back and forth on the merits of each other’s argument.

  Ferrari was posting the Corsi numbers of Oiler players on his site at the time, but in November 2007, he started posting a second column of numbers beside Corsi, simply calling the new column “Fenwick”: shots on goal plus missed shots, but not blocked shots.

  “I just liked Matt Fenwick; he’s smart and he’s a really nice guy,” Ferrari said of why he started posting Fenwick numbers. “I had already made Corsi famous; I wanted to make Matt Fenwick famous. That was pretty much it. I’m not sure it really matters whether blocked shots are counted or not—it’s still basically the same thing. It’s which way the puck is going. It’s all about possession. It was at times, I thought, a nonsensical argument in some ways, but I thought Matt should have his own stat. I really like Matt.

  “I named all the stats after people. I was criticized for that—people said they’re stupid names. But I think hockey stats get more talk in regular [non-hockey] circles than any other statistics. I live with nerds, I live in the trading world, I know people who know metrics inside and out and only casually follow hockey, but they know the [hockey] stats by name. That’s because they’re cool [names].”

  So Fenwick, the engineer who fancied himself more a fan of the game than a numbers geek—“I’m not really very mathy,” he said—ended up with his very own stat with his very own name. How cool is that?

  “Well,” Fenwick said in April 2014, “it’s getting a lot more interesting all the time. It was one thing to see it on the web, on a stats site, but when ]Edmonton Oiler left winger] Taylor Hall is dropping references to Fenwick in his postgame comments or Brendan Shanahan is talking about it on his first day on the job with the Toronto Maple Leafs [as team president], that’s not really something I ever could have imagined.”

  Again, that 2013–14 NHL season, when Hall and Shanahan dropped Fenwick references will be remembered as #fancystats’ coming-out party.

  Ferrari’s Irreverent Oiler Fans site turned out to be Ground Zero not only for Corsi and Fenwick, but also what has become the third element of the advanced hockey stats trilogy: PDO.

  PDO stands for . . . well, it doesn’t really stand for anything. At least not anything that relates to the statistic, which adds shooting percentage to save percentage to get a numerical value that appears to have great predictive powers. PDO was the Internet name Oiler fan Brian King used when he commented or posted on Ferrari’s site. King played a video game on the old Nintendo 64 system, called Perfect Dark. That’s where the PD part came from. And the O, to make it PDO?

  “It had no meaning,” King said. “I just put down PDO, it means nothing, it has no significance.”

  Seriously.

  First Vic Ferrari, and now PDO?

  Again, you can’t make up this stuff.

  In late August 2008, the then 20-year-old King and a bunch of Oiler fans, including Ferrari, were batting around Corsi and Fenwick talk as it related to the Oilers when King—I mean, PDO—posted the following: “Let’s pretend there was a stat called ‘blind luck.’ Said stat was simply adding shooting percentage and save percentage together. I know there’s a way to check what this number should generally be, but I hate math, so let’s just say 100 per cent for shits and giggles.”

  King maybe didn’t fully realize it at that precise moment, but he had stumbled upon the embryonic form of what is now widely regarded as the most tried-and-true advanced stat in hockey, PDO: the one number that, in all probability, tells you whether a player or a team is either incredibly lucky or unbelievably unlucky, and in which direction the player’s or team’s performance is highly likely to trend in the future.

  “PDO is great,” said Ferrari, who further advanced King’s initial concept. “It’s about the role of chance in the game, it’s about understanding that relatio
nship with chance.”

  In a span of less than two and a half years—from March 5, 2006, when Ferrari first publicly documented his Corsi revelation, to August 29, 2008, when King, “for shits and giggles,” stumbled upon PDO, with the creation of Fenwick’s Fenwick tucked in between—hockey’s advanced statistics community had been given three building blocks with which to launch an offensive on the sleepy, non-numerical hockey world that had no idea what was coming.

  Ferrari, Fenwick and King. Corsi, Fenwick and PDO. The Big Three—the hockey analytics equivalent of Larry Robinson, Serge Savard and Guy Lapointe.

  Okay, it’s the moment of truth. It’s time for a Grade 12 math fraud to try to explain Corsi, Fenwick and PDO to many who, I suspect, are as numerically challenged as myself.

  Remember when I said advanced stats aren’t all that advanced? It’s true—sort of.

  Let’s start with Corsi, which is the difference between two teams’ shot attempts while playing even-strength, five-on-five hockey: shots on goal plus missed shots plus blocked shots, minus the same items for the opposition.

  Let’s assume your team had 10 shots on goal, missed five shots and took five shots that were blocked. That’s 20 shot attempts for you.

  Let’s assume my team had eight shots on goal, missed four and took four more that were blocked. That’s 16 shot attempts for me.

  Corsi is the difference between the two. It can be expressed in a number of ways. We could say you are plus-four (the difference between your 20 and my 16) or I’m minus-four. But it’s most often, and best, expressed as a percentage. That is, of the 36 total shots taken by both teams in the game, you had 55.5 per cent. I had 44.5 per cent. There’s your Corsi.

  So, obviously, any number over 50 per cent is better than a number under 50. It doesn’t get much more basic than that.

  “The thinking behind it,” Ferrari said, “is that the more you have the puck, the more likely you are to win.”

  You might ask, why count shot attempts? Wouldn’t counting goals make more sense?

  Fair enough. A team’s goal differential—the number of five-on-five goals it scores minus those given up—can be a valuable metric, but statistics tend to be more effective the larger the sample size. In an NHL season, teams usually score between 200 and 300 goals. They often take more than 2,400 shots, plus all those missed shots and blocked shots. So the sample size for attempted shots is so much greater than that for goals scored—maybe 10 times as great. Many goals are scored as a result of skill and artistry, but most involve a great amount of luck or random bounces. Corsi gives you a volume of events that helps to offset the high degree of luck when using a smaller sample size.

  You may also ask, Why even bother tracking Corsi? What’s the point of it?

  Corsi’s value is that it has been proven, time and again, to provide a reasonably accurate representation of puck possession. That makes total sense. The more shots you attempt, it stands to reason, the more you have the puck on your stick. Just to be sure, researchers have used a stopwatch to actually measure puck possession in specific games, and the corresponding possession ratios inevitably correlated to the Corsi numbers. This is not fiction.

  Now, positive puck possession doesn’t absolutely guarantee victory—as noted earlier, the 2013–14 Colorado Avalanche were 25th in the NHL (47.4 per cent) in Corsi rankings, but finished with the third most points in the entire league. But the probability of a team’s success is greatly enhanced with positive puck possession. Intuitively, I think we can all agree that makes sense.

  That’s not exactly an advanced notion, if you think about it, although the non-believers remain dubious of Corsi’s usefulness and/or validity as a predictive number because, well, the numbers don’t always add up.

  It is by no means perfect, not even close.

  In 2013–14, for example, three of the NHL’s top 10 Corsi teams—New Jersey at No. 4 (54.6 per cent), Ottawa at No. 8 (52.2 per cent) and Vancouver at No. 9 (52 per cent)—didn’t make the playoffs. And, of course, there was the glaring case of the Avalanche, who became to the #fancystats skeptics what the Leafs were to the numbers crowd.

  “Each [exception] is different,” Desjardins said.

  The Devils, for example, went an incredible 0-for-13 in shootouts.

  “I think we can agree that is just bad luck,” Desjardins said. “With average luck in the shootout, the Devils make the playoffs.”

  This is also where that line about advanced stats not being that advanced falls apart. Guys like Desjardins and Dellow can, and will, explain in great and complicated detail the whys and wherefores of Ottawa and/or Vancouver’s misleading Corsi numbers. If I let them do that here, trust me, you’d get as lost in the numbers as I did with logarithms in Grade 12.

  That, of course, infuriates the anti-numbers advocates, who believe the #fancystats guys trumpet the validity of their numbers when they’re proven to be “right,” but find ways rationalize the hell out of them when they’re perceived to be “wrong.”

  The reality is that the stats guys know there’s no one magic number that is right all the time; there will always be outliers and exceptions. They will be the first to tell you luck, or sheer randomness, is a big part of hockey and no metric can actually measure or forecast all the bounces. They’ll also note Corsi is a five-on-five, even-strength stat that doesn’t take into account special teams or goaltenders or a host of other factors that help determine the outcomes of hockey games. Therefore, while using Corsi, the #fancystats disciples will often apply it in conjunction with other metrics. But that’s not something non-numerically inclined fans can always get their heads around. It starts to get complicated, putting the “advanced” in advanced stats.

  When all is said and done, though, whatever frailties, real or imagined, exist within Corsi, the #fancystats crowd will tell you the data makes them right a lot more than they’re wrong, that the probability of a good Corsi equalling a good hockey team is far greater than not.

  Of course, we could make all of this even more complicated. But we won’t do that now, other than to note there are myriad derivatives from basic Corsi.

  Corsi can be a team metric or it can be applied individually to any player. Also, you can factor in whether faceoffs originate in the offensive or defensive zone, the quality of opposition a player faces, even the quality of teammates he plays with. You can measure a player’s Corsi when he’s on the ice in relation to his team’s Corsi when he’s not on the ice (a metric called Corsi Relative). It goes on and on . . .

  There is, however, one further aspect of Corsi that can’t be glossed over: the “close” factor.

  Most Corsi references you see are what are termed “close,” which simply means the shot attempt calculations are made based on when the score is either tied or the teams are separated by only one goal in the first two periods. What the stats guys quickly figured out is that, once a team is ahead or behind by two or more goals, there is either a conscious (through coach’s orders) or unconscious (through players instinctively “protecting” a lead) decision to alter behaviour to play a more conservative, defensive-minded game. If there were no distinction between “close” games and the others, Corsi’s integrity would be severely compromised because of a radical change in mindset affecting how the game is played, depending on the score.

  This concept is known as “score effects.”

  “There is an almost universal tendency for NHL teams to get a greater share of the shot attempts when they are behind than they do when they’re tied,” Dellow said. “They also get a greater share of the shot attempts when they’re tied than they do when they’re leading. For example, the 2013–14 Bruins had a five-on-five Corsi percentage, when leading by one, of 50.3 per cent. With the score tied, their Corsi was 55.7. When the Bruins were trailing by a goal, their Corsi was 59.3.

  “One of the funny things about this is the ‘If we just started the gam
e like we played the third period’ postgame quote, which so many fans of bad teams have heard. It’s funny to hear these quotes from people who have spent years in the NHL, seeing this phenomenon play out over and over. They aren’t going to start games like that unless they start with a one-goal deficit, which isn’t going to help them win games.”

  Still with us?

  Good, that’s as far as we’ll go with Corsi.

  Now, on to Fenwick, which is going to be really easy because everything you read about Corsi still applies. The only exception is that Fenwick doesn’t include blocked shots. Otherwise, all the equations, standards and terms of reference are identical.

  So, you ask, why bother with it if it’s so closely aligned with Corsi?

  Well, Matt Fenwick was of the opinion that, while Corsi was a very good proxy for puck possession, if you wanted a metric to more accurately reflect scoring chances, blocked shots should be removed from the equation. A blocked shot, after all, is not a scoring chance. Neither is a shot from the blue line, but that gets counted in Fenwick, as it does in Corsi. Again, as was the case with a researcher actually timing puck possession to ensure Corsi was a good proxy, the same thing has been done with scoring chances and Fenwick. They match, more or less. Fenwick is, therefore, considered a valid proxy for scoring chances. Again, this is not fiction.

  The difference between the two for #fancystats aficionados may be no different than your preference for Coke or Pepsi.

  Corsi has perhaps better brand recognition. Because Corsi tracks more events, it tends to illustrate puck-possession trends more quickly than Fenwick. Stats guys will tell you that, in the short run, Corsi may be a preferable metric. But because Fenwick is perceived as a little more accurate than Corsi as a proxy for scoring chances, over the long haul, many feel it’s superior to Corsi. My sense is the #fancystats cognoscenti tend to slightly prefer Fenwick, but will utilize both, since multiple studies have shown puck possession and scoring chances to be linked anyway. For the most part, Corsi and Fenwick tend to mirror each other pretty well, and on those rare occasions of marked differences, the nod seems to go to Fenwick.