Photo Credit: Kelley L Cox - USA TODAY Sports

Stats vs. Eye-Test is Dead – There is Only the Acceptance or Denial of Evidence-Based Analysis

For as long as sports have been around, they’ve been romanticized. We like to weave stories around athletes who perform impressive feats at the unlikeliest of times, and the amalgamation of these tales, passionately stitched together, produces the mythology of a player, a team, or a sport.

For a long time now, statistics have disrupted this mythology. They don’t buy into the big moments, treating them like any other event. They aren’t fooled by fluctuations in samples known to be affected mainly by luck. And, in general, they’re all business and very little fun.

I can see why the average sports fan, and hockey fans in particular, would be wary of statistics. Not only do they tend to poke holes in their favourite storylines, but there has also been a tendency to poke holes in beliefs that are not only commonly held, but seem to backed by common sense.

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Among the targets of the advanced stats movement have been face-off percentages, blocked shots, zone start ratios, quality of competition, and of course plus-minus. It’s generated a significant amount of skepticism in the uninitiated, many of whom often wonder if stats folk are watching the games at all.

The Dichotomy

Thus the stats versus eye-test debate was born. This was pitted as a dichotomy, with each faction having an opposing opinion on every event and concept with the game. As the story goes, the stats community thought the eye-test community placed too much emphasis on intangible elements, while the eye-test community felt that numbers couldn’t possibly capture the randomness and nuance of a continuous and flowing sport such as hockey.

Perhaps both “sides” would have a point. Statistics will always struggle to explain every single thing that happens out on the ice. There will also always be a debate as to how much weight should be given to certain events and characteristics, and whether aspects like leadership and clutch will ever be measurable, or if they’re even worth measuring.

Unfortunately, this whole two-sided debate is built on a lie: there is no such thing as Stats versus Eye-Test.

The entire structure is based on a false dichotomy. The problem is that there are no fans worth their salt on the “stats side” that don’t also watch the games and generate opinions based on things that they see. To suggest such a thing is merely a tactic designed to undermine the power of a stats-driven argument. And without a doubt, this tactic is frequently used.

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Both sides are capable of using the eye-test, and indeed both sides will on many occasions. The only actual debate is the acceptance of statistics as a trustworthy explanation of what fans are seeing, or aren’t seeing.

Denial of the Scientific Method

As a card-carrying member of the eye-test-only camp, what you are really stating is your denial of evidence-based analysis — the denial of the scientific method.

It’s a bold stance to take. History hasn’t been kind to those who refuse to go along with an evidence-based approach. Let’s go through a brief and non-exhaustive review of some of the casualties of the scientific method.

  • Geocentricism – the concept that the sun and the stars revolve around the Earth rather than it being the other way around.
  • Flat Earth Theory – the idea that the Earth is flat, instead of spherical.
  • Alchemy – the theory that contained claims that included, among others, that one could convert base metals, like lead, in noble metals like gold.
  • Astrology – the study of how the movement of celestial objects affect human traits and affairs.
  • Phrenology –  the belief that one could read bumps on a person’s skull to learn about their personalities.

Each of these once-widely accepted theories was put to rest when testable and repeatable evidence was mounted, overcoming commonly held beliefs. This is the function of the scientific methods, one of the most essential techniques in human history. The method is simple, yet profoundly effective. You test, observe, and record. Make a note of what the evidence indicates. Build on the ideas that are supported, and discard those that are not, even if you favoured them.

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If you continue to refuse to believe how evidence-based analysis can see things that you can’t and make conclusions that don’t jive with your assumptions, the ideas above are the types of ideas with which you’ve aligned yourself. Pseudoscience, disproven ideas that look utterly ridiculous with the knowledge that we have now.

Society isn’t done with these types of controversies either. We’re witnessing one of the biggest ones right now in the climate change divide. One side has a figurative mountain of evidence behind it, while the other refuses to let science tell them something that they cannot perceive with their own senses. This debate, I think, provides a very relevant comparison to evidence-based analysis in hockey.

Consider the following explanation of weather versus climate from Neil deGrasse Tyson on Cosmos: A Space Odyssey. In the video, Tyson walks a dog along a beach. The dog veers back and forth, testing the limits of its leash, but all the while heading in the same general direction. The dog’s path is weather, Tyson reveals, while his path is climate. This is how scientists can predict the climate of the planet, even if they struggle to predict weather on a day-to-day basis.

The same can be said of events in hockey. Our ability to predict individual games is measured in likelihoods, not assurances. Shots in hockey are tiered based on their level of success. Some are blocked before coming close to the net; some miss the net; some are stopped by the goalie. It’s only a select few that land in the back of the net. It’s also only these that are required for a victory.

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Only one in every 20 or so attempted shots becomes a goal, on average, and it’s difficult to pinpoint which shots those are going to be, even with what we’ve nailed down regarding shot quality. But the fact that in some games there are goals three goals for every 20 shot attempts and in others there are none doesn’t change the fact that the average will force the shooting percentage to balance out over the long term. This is how regression to the mean works.

It’s also why shots are a more reliable indicator of future success than goals are. By and large, we know that eventually a specific percentage of them are likely to go in the net, so their value can still be objectified, and there are roughly 20 times as many of them. So we can feel comfortable making some light conclusions based on shots after 20 or 30 games, while goals (or wins, for that matter) can be misleading even on a season scale. That’s how analysts used statistical evidence to accurately predict the downfall of Patrick Roy’s Avalanche and Bob Harley’s Flames after their pop-up playoff runs in 2013-14 and 2014-15 respectively.

Parsing Meaning from Randomness

Despite a history of predictability at large scales, many assume that hockey is simply too complicated for numbers to grasp. I think the following points from Garret Hohl and Felix Sicard sum up convincing rebuttals to this proposition.

Firstly, in spite of hockey’s obvious inherent randomness, predictive models have been able to predict single-game results at well above success levels of 50%, which is what you would expect if the results were completely random. At season scales, predictions have an even greater rate of success.

Secondly, there is either a sense of extreme self-importance or extreme naivety associated with the idea that the sport of hockey is too difficult for math to comprehend. We, the race that is predicting changes in the climate of the planet, that uses mathematics to determine with accuracy how many asteroids will pass between the Earth and the moon in the next hundred years, that has cured a myriad of fatal diseases, landed astronauts on the surface of the moon, and split atoms apart, cannot possibly devise a way explain 10 grown men using sticks to whack a disc of vulcanized rubber around a sheet of ice.

I’m forced to assume that this logic is paired with a lack of understanding or appreciation of those other accomplishments. It seems like there would be a large amount of overlap between these doubters and people that deny climate change, think vaccines cause autism, believe the moon landing was faked, and are more familiar with the Big Bang Theory TV show than the theory that explains all of existence.

Missing the Forest for the Trees

The thing is, hockey analytics is an evidence-based endeavour, and by definition, that means that there is plenty of evidence out there to back up its claims. How often do you see people that denounce the predictability of hockey analytics back up their claims with evidence?

Rather, I think the case is somewhere between a lack of interest and a lack of understanding. Opponents of analytics tend to miss the forest for the trees: the forest, in this case, being evidence-based analysis and the trees being singular entities like Corsi and regression. Many seem to get so focused on their dissatisfaction with a single metric that they can’t be bothered to understand the movement as a whole. They associate Corsi directly with analytics, and when this one basic metric fails to line up with their expectations, they lump the entire idea of analytics together and thoroughly dismiss it.

The truth is, there is no one measure or one technique that defines hockey analytics. Analytics is not a stat, or a collection of stats. Analytics is hockey’s representation of the scientific method. To cast it aside is to deny one of the of our species’ greatest assets because of a case of cognitive dissonance: the stats didn’t tell you what you wanted them to, and therefore they are flawed.

There is so much more information out there, if you are simply willing to look. It might seem daunting for the uninitiated, but aggregators like NHL Explainers on Twitter, or the website MetaHockey.com have put in a lot of effort to ensure that the wealth of research is easily searchable and accessible.

As I said, there is no Stats versus Eye-Test. There is no dichotomy. What there is is a continuum on which your trust and comprehension of evidence-based analysis lies. Some are more comfortable than others with allowing stats to tell them something that they haven’t seen, even if they both have faith in the measures.

At the other end of the spectrum, you have the group of people that are certain that analytics will never be able to explain hockey. Right next to them, you have people like Andrew Walker, that claim that they see value in analytics, but that it just isn’t quite developed enough yet. This is generally an excuse for a lack of understanding or investigation into what these people are dismissing. Do you think Walker has spent much time on Hockey Graphs studying the efficacy of predictive models? Researching why some skills are overrated, and others are underrated? I’d be shocked if he had and is still able to make the claims that he puts forth on twitter, radio, or television. Mixing that with the pompous attitude required to tell others what they do or do not understand without even bothering to understand the evidence behind the other’s position, and what you get is a stubborn refusal to accept advancement.

Progressivism versus Traditionalism

I get that this is a difficult time for some members of traditional media, with bloggers and fans sitting at home fact-checking their every claim. Gone are the days when radio personalities can be taken at their word.

Gone too are the days when you can guarantee whether a player has been “good.” What defines good is not the same in every person’s mind. While some are still swayed by production, like Derek Dorsett’s surprising seven goals so far this year, others take into account data that is known to predict future outcomes, such as being horrifically outshot every time he’s on the ice. Are seven goals definitely “good” if even the staunchest defenders acknowledge that it won’t continue, or is it simply lucky? And if it were luck, would that really be so bad? Luck is why the games are played after all.

I don’t expect people like Andrew Walker to ever fully accept or understand the value of analytics. If anything, he’s headed steadfast in the opposite direction, declaring on Saturday and again on Monday that the advanced stats community has “lost him.”

And that’s fine. People who are wired a certain way aren’t ever likely to change. It took the Catholic church hundreds of years to accept that it was wrong about the location of the Earth relative to the sun and stars. Donald Trump and his cronies aren’t ever likely to fully grasp the concept of climate change.

The difference here is that the Catholic church and the Republican government have the power to stand in the way of systemic change in favour of evidence-based reasoning. Traditional media members like Walker have no such power. They will sit on the sidelines and express their doubts, ever falling onto deafer ears. When is the last time a radio host was hired by an NHL team to consult or even head up a department?

The advanced stats community, as it were, is certainly far from infallible, and they don’t always get things right. However, those that accept the use of logic, reason, trial and error, empiricism, and science, are going to be the ones pushing progress and making a difference when all is said and done. History has repeated itself in this regard many times over, and it will continue to do so in the future. I’d say “pick a side,” but as I’ve established, there are no sides here. There is only the acceptance and denial of evidence-based analysis. If Walker and his ilk choose to remain in the realm of denial, I’d suggest they enjoy their relevance in the field while they still have it. That era is coming to an end.

  • North Van Halen

    You stat guys are so cute when you’re defensive and as always, using the stats that favour your arguments while omitting those that don’t. Other than a few old school exceptions, I think modern stats have been a embraced as a tool to enhance the eye test. The problem is some people want us to accept that stats tell the whole story when they don’t come close.
    I love the old ‘Moneyball’ arguement but anyone that watched baseball back then could tell you the team Billy Beane assembled was much less about the players he ‘recruited’ using his Moneyball techniques and much more about the pitchers Oakland had. In 2002 & 2003, the years the book covers, Oakland had the best ERA in the American League both years and in all of baseball for 2003. An AL team against the DH with the best ERA in baseball. You think the Billy Beane ‘Moneyball All-stars’ do so well with Jays pitching staff? How about Theo Epstein and Boston without the highest salary in baseball?
    I like the stats for context but to tell me I have to accept them as the be all end all is ridiculous. Maybe one day but not today, not even close.

      • North Van Halen

        Please enlighten me. I’m pointing out statsheads revere Billy Beane and Moneyball yet it wasn’t his Moneyball that won anything, it was the fact they had the best pitching in baseball. This guy walks, this guy takes pitches, all irrelevant if the pitchers werent the best in baseball. He didn’t find Mulder, Lincecum, Hudson et al through Moneyball, they were solid draft choices. Moneyball darlings enhanced the team, they were definitely not the reason they won.
        Did you read or watch Moneyball to know what I’m talking about?

        • North Van Halen

          Oh and by the way his pitching staff was pretty pissed about the book/movie because it gave the most important part of his team next to no credit. The truth didn’t enhance the ‘Moneyball’ image or make for a good story so it was practically omitted, sound familiar?

        • stillers

          i know what you’re talking about. I feel like you missed the point of the article. It’s about Stats vs. Eye-Test. You are talking about old school stats versus advanced stats when referring to ERA versus Moneyball type stats.

          • North Van Halen

            I’ll try to make this simple for you. ERA is an old school stat much like goals assists & points. Not once has this site trotted out goals, assists or points to prove a point other than to tell me their stats prove a certain players points/goal/assists are unsustainable (see Dorsett, Derek). The whole point of Moneyball was that Billy Beanes genius with advanced stats built a great team. He’s the baseball version of George Chayka. But fact is he was smart enough to draft 3 generational pitchers (through old school eye ball test scouting) and they were the real reason the A’s won. Some would argue Moneyball & stats had almost nothing to do with their success.
            So when someone implies I’m a troglodyte because I question the relevance of the stats and the way they are presented it reminds me of Moneyball. Clear enough?

    • Mellowyellow

      My question is.. who accepts the stats or analytics as the end all be all for making decisions.. Certainly, we haven’t heard any GM come out and say, I make decisions based solely on the stats or whatever predictive analytics they’ve employed.

      At the end of the day the stats are just stats.. For example, stats don’t employ physics or biological principals. If your stats tell you something contrary to physics principals.. You think even the most basic statistician will tell you the limits of stats. Stats in hockey don’t tell you the players level of effort, if the numbers are from the start of the season when play is alot choppier and the refs are still trying to adjust to the new rules..

  • If I’m reading the article correctly, I think it mischaracterizes the debate. Stats vs. Eye is really promoting the strengths of one perspective against the shortcomings of the other. In the past, the “Eye” test was the only test so there was no debate. Then computing power and a new hockey paradigm created the analytics movement, which adopted a different perspective to play and compensate for the shortcomings of the Eye test (e.g. inability to quantitatively categorize play, to process types of plays that weren’t really observed). However, there is a double movement as Stats gets rightfully critiqued – people aren’t predictable, abstract concepts but unpredictable and dynamic humans. In certain cases, stats win. In other cases, eye test wins.

    What I bothers me is the implication that evidence-based analysis is infallible. One can reject an evidence-based analysis if it is faulty. Pick a problem: insufficient sample size, correlation/causation fallacy, interpretative bias. I think Brian Burke said it best at one of the hockey analytics conference (you can find it on Youtube), analytics only tells me about the past and can’t predict the future. He hates analytics but would sell the farm to get an predictive system that actually works. But the thing is…you’ll never find that. You see this problem in modern economics – we’re not mathematical models. Economists say that we’re all “rational utility maximizers” but heterodoxical thinkers like Hungarian historian Karl Polanyi say we’re motivated by prestige, not utility maximization. Graham Allison studied the Cold War Crisis and debunked the idea that Kennedy and Khrushchev made the decisions that maximized their positions – rather they were subject to human flaws such as organizational shortcomings and politicking.

    To shift the debate from Eye vs. Stats to Evidence or Ignorance is another false dichotomy. An accurate interpretation of evidence does not guarantee an outcome. You try to interpret the information available and take a position. Sometimes it works out, sometimes it doesn’t.

  • To be honest, I didn’t read the whole article. I gave up part way through. Here are some thoughts to summarize:

    Analytics is a tool, one of many, used to evaluate players. This tool has value, as do others, and should be used.

    To only use analytics to evaluate players is foolish. Check in with Florida and Arizona to confirm.

    The “us against them” thing is not a thing. It only comes across like that when analytic guys come over to my table to argue, when all I want to do is watch the game. Which leads me to make the following statement: The analytics community has this in your face, aggressive, and seemingly angry way of forcing their knowledge on to others. They feel it’s their duty to educate those silly non believers, often in a disrespectful way.

    So, I already feel analytics has a part to play and I don’t need junior with an empty wallet and a brewsky in his hand schooling me.

  • Gampbler

    Until I see some historical analytics, explaining player progression or team performance then I will be a little skeptical of analytics as a whole. That’s not to say there aren’t some useful stats put out that do have a valid place in the hockey world. Analytics should be able to tell a story for every player drafted and build in reverse the reasons why they were or weren’t successful at the NHL level. If that can’t be done, then how are we to put any faith in going forward? The reason Bill James et al turned baseball on it’s head, was because they were able to prove success before it happened at a pretty high rate. Much like baseball, though analytics takes time and a much larger body of work then one game and that is where there is a disconnect with me, when I see an advanced stat paraded out between periods or after a game which really means very little.

    If we put all of our faith in Corsi, then the top four teams Carolina, Edmonton, Montreal and Columbus should be at or near the top of the league in points in the near future. Or will they not be able to sustain their high percentage of shot attempts that they have had in the first almost quarter of this season?

  • goalfiSh


    This is hilariously bad. I actually logged in for the first time since the Drance days to reply to this poop.

    1. In sports we are talking about human beings – not naturally occuring scientifc events. You can use scientific methods to observe and predict the rate at which the stars rotate around the Earth because it is a constant observed in nature. It’s physics. You can’t measure how a hockey player is going to react in game 7 of the Stanley cup finals up by a goal with 2min left in the game and a faceoff in his own zone after an icing vs the other teams second line. You can’t measure the mental fortitude of a player in that situation. Or the skill level or conditioning of the athlete. How he reacts is a randomly occuring event dependant on sooooo many X factors and variables. When you compare it to geocentrism it’s apples to watermelons and completely laughable!

    This is patently ridiculous.

    What an absurd arguement.

    I like both stats & eye test, and this just lost all respect.

    Somebody please cancel his internet subscription asap!

  • Laxbruh15

    The only thing that’s dead is the relevance of this article. Creating a model that’s slightly above fifty percent over the course of multiple seasons is irrelevant to the individual and the season itself. The other issue is that you don’t understand the correlation between winning and shots. Shots alone are meaningless, the only reason why more shots are typically associated with wins are because scoring chances typically create shots. To have the mentality that shots create scoring chances limits the amount of chances you’re going to get and incidentally, wins. The dallas eakins strategy of attempting to get as many shots off as possible regardless of where they’re from or the value of the play itself led to an offensively crippled team with some of the highest end offensive talent in the league at the time. The team with the best scoring chances is going to be the team that typically wins. That’s where guddy comes in. He drastically cuts down on high scoring opportunities when he’s on the ice against the best players. He soaks up the most difficult minutes and starts which allows for other players to take offensive roles. He unlocks the full potential of the team.

    • Whackanuck

      Guddy struggles both with the eye test and the analytics. If you want to make a case you need to use the right facts-not easy with a plethora of analytical stats nor a fan’s bias for or against #44.

      • Whackanuck

        If you’re going to trash this, show me data, either analytic or eye test (a .gif perhaps) that shows me Gudbranson is a good defenceman. Hint: he doesn’t cut down scoring opportunities but so far this year goaltending has usually bailed him out.

  • Laxbruh15

    This is not evidence, it’s highly biased “analytics,” which by definition is based upon individual interpretation. You using numbers does not equate to empirical evidence. It’s the opposite, it’s typically an indicator that the information is false when you can’t back it up with an adequate explanation and you start insulting people that disagree with you.

  • If we accept that the eye test and analytics are merely “tools”, then an analogy will help clear up the debate. If the “eye test” is a hammer and analytics is a “screwdriver”, what is the better tool to build a house? Are nails or screws better for building the frame? What’s more important is *how* the tools are used (i.e. design and execution).

    An even better analogy (I’m dating myself here) is that old Chunky soup commercial where Rowdy Roddy Piper (FORK!) faces off against Mr. Wonderful (SPOON!) to see what’s the better way of eating Chunky soup.

  • Rodeobill

    I wonder if perhaps instead of a binary interpretation of people’s opinion of stats, if maybe it is a spectrum going from one extreme (Stats are the only valuable information to consider) to the other (Stats are completely useless). In between are people with varying degrees of respect for the accuracy and usefulness of stats as a tool to assess and predict in hockey. Is it possible that some people get so fixated after hours, weeks, and years studying (and defending) stats that they lose their own lack of bias and become antagonized by the other side of the spectrum? A side filled with people who filled with “fans,” who for some reason have become convinced that stats wants to reduce all the human and storybook aspects that draws them to the sport and their team (the aspects that they RELATE to) to simple numbers and math. In a sense, people on both extremes have baited themselves into a false argument. Stats does not necessarily threaten the enjoyment and human aspects of identifying and rooting for your team, stats is a gathering of information based on certain variables, the definition of those variables and the accuracy with which they can be used is not infalable, and goes through a continual process of redefining in a quest to become more accurate.

    It is a false debate I should think, as often “pro stats” articles and comments really are just trying to say “it is improbable Dorsett continues playing as such” which according to statistical models assessed by the current definition of variables is probably true. What the “fan” side of the conversation perhaps is interpreting this as a diss and an effort to take away due respect for good work done by one of his teams heroes. The artificial debate comes from two sides having two completely different discussions and thinking they are responding to each other.

    • KCasey

      The real problem I have with the Dorsett sustainability arguement isnt that I feel they are dissing him, its the only bringing it up whe he scores. Every time the game recap goes something like, ‘Dorsett scored again but he wont be able to keep scoring, Gudbranson is terrible and they need to trade him, Boeser had 3 shots, he is the best player on the Canucks team, he didnt score but what an amazing kid, so remarkable’. It just gets old. Obviously Dorsett cant stay on pace to have a 40 goal season, McDavid opened the season with a hat-trick…dont think he is gonna hold the pace and get 200 goals this year either…doesnt mean that it wasnt exciting and cant enjoy every single time it happens.
      As to this article saying the analytics is science and therefor shouldnt be scritinized so harshly because science weighs and measures and calculates and therefor is more reliable to the world is false to extents. Example 1: Science said they determined milk is very good for you. Oops….changed our minds its bad for….ooops a little is actually good for you, oh sorry we changed our minds again, its good for certain things when used in small amounts but more studies need to be done. Convincing. Example 2: Ibuprfen is completely harmless and can shoud be used to fight inflammation…oh sorry guys we got it wrong…it causes heart attacks. Thanks science. Example 3: carbs are good for you….carbs are bad for you…some carbs are good for others are bad…oh actually all carbs are good if takin before and after workouts otherwise there fattening…actually sorry carbs that dont contaim gluten are acceptable…we think but were still studying. I could do this all day but the point is science, like analytics is constantly changing and addapting its knowledge….which is totally fine and absolutely acceptable in order to get it to the finely tuned systems that actually hold real value in terms of consistancy for predictions. However in the meantime it is a little frusterating for the stats squad to talk to average fan as ill informed and ignorant because they can see flaws in your systems that you guys yourselfs have deemed to be in its infancy and not fully capable yet of what your hoping it will do in the future. So than the conversation comes off as you are all dinosaurs because you are not convinced by are partially completed models that havent worked as desired or proven anything over a long term but we have an idea of it can do in the future so use it right now in ways you cant possibly understand because we ourseleves arent even entirely sure what is or isnt important in the grand scheme of what to measure and calculate. Its like Ford making the first car and telling everybody to stop riding bikes as much and start using his new vehicle even though he hasnt competely finished the engine.

      • Rodeobill

        Good points, I agree with them all. I just think that sometimes as fans we can sometimes be a little oversensitive or reactionary to articles written by someone who might be a fan of statistics more than hockey (and I agree that most articles seem to even fall short of objectivity be it in tone, or selection of evidence, points of contention, etc.) I get my fix more from people like you and others commenting on the site most the time. I don’t always agree with every comment, but trolls aside, I always enjoy reading and discussing our team from a fan’s perspective with other people who are obviously fans too (of hockey and our Canucks) as opposed to fans of statistics.
        Hmm. I wonder if this blog was changed from a hockey blog to a stats blog if it would change anything, and fans of stats could peruse and comment.

      • Even the greatest scientists have made critical errors. Einstein didn’t believe that the universe was expanding and had to update his theory of relativity when he was proven wrong. Hawking believed that nothing escaped black holes until it was discovered that radiation did. I remember his admission of error a few years ago because a large contingent of the scientific community held their breath until he elaborated so they could fix all of the work that was derived from his black hole theory.

  • crofton

    “predictive models have been able to predict single-game results at well above success levels of 50%” What does that mean…..80%? 54%? Because a 54% corsi stat seems to be realllllllly good, but I for one, would not call that “well above 50%” and “Our ability to predict individual games is measured in likelihoods, not assurances” and yet all I have seen touted here is Corsi, when we are expected (rightly so) to take all stats inclusively, and that Corsi itself is assuring us of their “predicted” results. Furthermore, scientific method is not suited to defining the human condition. No matter how scientifically you isolate, break down or whatever, you can never remove the human element. Another question for the Corsi be all and end alls…How would you statistically account for intimidation? As in…Keep your head up when so and so is on the ice? How about heart? Lastly, to crap on posters here because they point out the flaws in your logic is inaccurate. Most here acknowledge advanced stats have a place in analyzing our game. What they don’t do is allow it is the only tool…the same, but in reverse of your accusations, after all, most of you stats bloggers here say the eye test is completely irrelevant. Stats have their place, it’s just not on the throne.

    • Brent

      Definitely an interesting video, thanks for distracting me from my work. Another aspect to consider is the idea of strong and weak link sports. Basketball is a strong link sport. A dominant player like Michal Jordan can completely dominate a game. Just give him the ball and stand back. The surrounding players are not as important. Soccer is a weak link sport, with the number of players, the size of the field and defensive strategies, a single player can not dominate the same way as in basketball. You need a lot of support players to help set up the stars, the weakest links on the team are important, possibly more important than skill of one start player. Picked up on this in a episode of revisionist history by Malcolm Gladwell. People he talked to who has studied soccer said that teams would be better off hireing 4 good players rather than one superstar. Anyway, I suspect that Hockey would be a weak link sport. As the video says, your best players can only be out there so long. The one exception I think would be goal tending. A really good goal tender can steal you a bunch of games. Like we did when we had Luongo and last the last couple of years with Ryan Miller

  • Ken Priestlay Fan

    I read the articles on this site because I think the statistical analysis of sports (in this case hockey) is an interesting and valuable pursuit. It adds to the game and to the understanding of the game- both for the ‘common man/woman’ and the professional game. I think the thing that gets my back up is the denial that the instincts of an individual who’s spent the majority of their life studying, playing and absorbing the sport has any value. I appreciate that’s not what you’ve said above, but it is what’s been said on this site countless times before; “McEwan Is A Waste Of A Contract”, countless hatchet jobs on Gudbranson/Sutter/Dorsett (though Guddy is trying really hard to prove that analytics work this season). Statistics need to be used dispassionately and sometimes it seems that your passion for stats and for hockey get in the way of that in my opinion

    Sports of all kinds can and have been won by applying statistics to find new ways of winning. But the gut instincts of a guy with hundreds of games under their belts has also won many, many gaudy trophies

  • Naslund

    The Pro-Life vs Pro-Choice example is a weak one. Although I don’t align myself with the Pro-Life point of view, Pro- Choosing whether the fetus lives or dies can logically be interpreted as being “Pro” being able to choose death. Similarly, the climate change debate is just that, debatable. Again, I believe that the climate is changing. Can that be attributed to man’s activities, or is it just part of a cycle? Very likely it is caused by man, but there is still room to debate.
    It is a very weak rhetorical method to introduce debatable points as evidence for a completely different argument.
    Stick to hockey arguments to prove your hockey point of view.

    • Brent

      Actually in terms of climate change, there really is no room for debate. CO2, like other GHGs absorb and re-radiate infrared radiation. without a greenhouse effect, the globe would be much (~33 °C) colder.
      increasing GHG concentrations will alter the planetary radiative energy balance, restricting the outward emission of infrared radiation. a planetary system with more incoming radiation than outgoing radiation will warm
      These tenets are objectively inarguable. The nuances of climate change (exact climate sensitivity, regional variations, ecological implications) are open to evolution as the science improves, but the foundational physics to be wrong would require a complete overhaul of our understanding of physics and thermodynamics. There are many indicators of increased warming that have been documented: glaciers are retreating, tree lines and other species are shifting poleward and upward, temperatures are rising over the land and oceans, the ocean’s heat content has gone up, and sea level is rising. The cause is clear – increased CO2, a greenhouse gas whose warming properties have been know for over 2 centuries. Its present concentration is the highest for at least 800,000 years, and possibly longer.

      In terms of pro-life and pro-choice, a woman has the right to choose rather than a bunch of old white men telling her what to do.

      • Cocomo

        “In terms of pro-life and pro-choice, a woman has the right to choose rather than a bunch of old white men telling her what to do.”

        Only if we can conclusively prove that the fetus inside her is not an autonomous human being. If it is, then no, she doesn’t have the right. So, whether or not and/or at what point that bundle of cells becomes a person is largely what the debate should be centred around. Let’s talk about when brain activity develops, when there’s spinal cord, a heartbeat, etc. There is a scientific answer to this moral question. Also, nice try with the identity politics deflection. Who gives a hoot in hell what their colour or gender is? If you’re not addressing the substance of their argument, you have no business being in the discussion. Apart from that, you do know that most “minority” groups in the Western world are far more socially conservative than the national averages within the places they live, right?

        • Naslund

          The fact that you two are arguing about these points proves my point. The author wants to make an argument and he uses as evidence points that he assumes that everyone agrees with. Clearly everyone does not agree, hence, it is a weak argument to attempt to prove a point of view about hockey analytics with debatable issues.

  • Whackanuck

    Analytics does clarify PAST events that may or may not reflect what a fan watching games, especially on TV, concludes.
    I’m most curious about the annual PGS stats about the likelihood of a rookie making the NHL. This is a frequently touted exercise by these bloggers but we have yet to see any verification that it has meaning.
    What happened post Tallon revival, to all the Nation stats gurus that went to work for Florida anyway?

  • Betty

    “Firstly, in spite of hockey’s obvious inherent randomness, predictive models have been able to predict single-game results at well above success levels of 50%”
    No one would take as science or evidence based fact a methodology that helped you be slightly more accurate than a coin toss.
    This all seems written by someone who knows that science is good, data is good, “advanced stats” uses numbers and data therefore must be good.

    Remember, before we realized the Earth revolves around the sun there were competing models (think Plato) that were accepted as the result of reason and logic (how can you argue against reason and logic etc.) Just because you have a model that you think is based on evidence, doesn’t make it reliable.

  • krutov

    couldn’t read this article. premise was too asinine to engage.

    anyone else remember a couple of years ago when single posts on canucks army might get 60 likes or trash votes?

  • Doodly Doot

    Stats vs. Eye Test is a fake issue, but let’s pretend this article is meaningful. The real subtext to the simplistic issue Jeremy has framed is: from what area of influence ’should’ management assumptions be made concerning players? Commenters here have taken the bait.

    Analytics, at their very best, provide useful generalizations to help inform and shape assumptions.

    At their worst, they contaminate, influence and degrade superior assumptions with misleading data.

    Superior decision-making from quality assumptions includes dozens of factors of which analytics are just ‘one’. Intuition, discreet knowledge, apparent facts, actual outcomes, synergies, revelations, axioms… the list can go on and on. Each management team prioritized their list of factors, but you get the idea.

    I’ve read many times from the analytics apologists that when facts diverge from analytics it’s just ‘luck’. I have not read anything yet about how ‘luck’ is discerned, accounted for and filtered out of the raw data to ensure quality. Hmm… convenient oversight?

    The zealotry for analytics in media is seems nothing more than intent to manipulate. Textbook Herman/Chomsky propaganda model. Clearly it’s working with the majority of commenters here (both legit and fake). Quixote Davis. Tilting at windmills in the metatheatre of hockey.