And I think you’re right — “momentum swings” and other mythological descriptions of games could get a hell of a lot more objective and accurate. Maybe if, instead of keeping track of hard numbers (i.e. the number of seconds spent in each zone), we had a way to rate each shift in terms of how much time the puck spends in each zone. This makes it inherently more subjective, but it would require a lot less work, and it would be a simple “That was a see-saw, back-and-forth shift with equal time spent in both offensive zones, so we’ll give that shift a 5 out of 10.”

Obviously a stopwatch would be preferable to that method, but it all depends on how much out little community is willing to work at this. Maybe if we divided the responsibilities by line and period (e.g. I’d take the Daydream Nation line in the first period, someone else would take them in the second period, etc.)?

The linked Puck Prospectus article will do a much better job at answering questions 1 and 2.

As for your third question, I’m not sure I’d put “momentum changes” into a +/- system. I think it’d be much more effective to quantify other aspects of the shift — such as time spent in each zone, as I suggested - from an objective perspective, then make inferences from the hard data. It may be that shift ratings or “zone time” would eventually lend themselves to be summed up in a +/ style metric, but for now I think the immediate focus should just be on data collection.

I still think the primary focus of any +/- metric should be goals and which events influenced that goal. However, I think it’s too much of a jump to objectively say that a good, non-scoring shift directed influenced a goal. (Behind The Net has an off-the-ice +/- that I admittedly don’t know anything about, but it’s very likely worth looking into. It may relate to this in some way.) While I think it’d be very difficult to connect a good 4th-line shift to a first line goal a minute later, I think it’s absolutely worth quantifying the contribution of that 4th-line shift in some way, shape or form. Maybe “zone time” isn’t the best way to go about it. Do you have any other suggestions?

Obviously there will be different reasons the puck will be in any zone (defensemen often hold the puck behind their own net until their team resets), but a large percentage of play occurring in one offensive zone is hard to discount.

I like the idea of a shift rating +/- system, or any way to quantify how effective a shift is. However, the biggest problem is subjectivity, and while Trixie had some good ideas about how to minimize the effect of subjectivity in a rating system such as that, it’s still based on a subjective rating. She made another good point, though, that data collection is the primary goal here. “Zone time” may or may not have implications as to whether or not a shift is effective — there will always be exceptions any things unexplained by a statistic — but at least it’s objective in nature.

I’m more of a recent hockey fan (I’ve watched the ‘Hawks the last couple seasons — thanks, Rocky), and as a baseball fan for much longer than that I’m obviously inclined to do statistical analysis. And while I’d say that baseball lends itself to statistical analysis in many more ways than does hockey (it truly is a game of stats), hockey can still benefit from statistical analysis. I’m actually very excited about the sites I linked to (PP and BTN), and I think this kind of innovative analysis can really shed a new light on the sport, at least for the common fan.

I think you’d be hard-pressed to deny how the shift towards sabermetrics and “better” stats (like OPS+ and other league-normalized stats) has helped cause an increase in the popularity of baseball within the last decade. When people know more and understand the game better, they enjoy it more. (Well, most of us. The “SHOOT IT!” guys might not have that intellectually-inclined mindset.)

And with the vast number of insightful and passionate fans on SCH alone (I know other SB Nation blogs have done similar community-oriented things like this — South Side Sox does community projections, amongst other things), there’s no reason why we shouldn’t get involved with something like this. At the risk of turning this into some motivational speech, the ultimate goal of these blogs is for fans to educate and inform one another, and we’ve got all the resources here (i.e. people) to do some pretty awesome stuff.

And yes, you’re right, this will be a great summer project.

Also, the score and time left… there is a lot that goes into evaluating a penalty, but I think you’re on to something here too. If we had a metric with specific criteria to measure the “effectiveness” of a penalty, we could evaluate the types of penalties players commit. (Then again, I don’t really need data to tell me that Eager will commit a higher percentage of “dumb” penalties than other players on the team. However, it would be nice to see just how many careless high-sticking penalties Kane committed and how those penalties negatively affected our team.)

This is a good discussion. Everyone knows that these factors play a role in determining the outcome of a hockey game, so I suppose the goal is to determine what exactly that role is and how much influence it has.

This doesn’t really help. The point is to get away from subjectivity. And while I don’t necessarily think your idea is a bad idea — it would likely come up with some interesting and useful results — the goal is to objectively collect data and then do the analysis.

I’m not sure how your dismissal of this conversation as a “nerd off” and your criticism that it would “become so nuanced” are relevant or constructive. Can you expound that a little? (Yes, it would be nuanced; but the more aspects of the game that you can objectively quantify, the better.)

i think we would end up just creating a special stat for a group of players who we all (included coaches and GM’s) know are under-represented when it comes to statistics but whose contribution is significant.

Yes, yes we would. Again, that’s part of the point of this. And maybe the system I suggested or we discussed above isn’t the greatest system, but it’s at least a starting point. If you have any other suggestions — suggestions that don’t hastily dismiss the entire idea without justification or explanation — then I’d be more than happy to hear them.

collecting that data (screened vs unscreened goals) is the harder part because you would need to collect data for all shots where the goalie is screened, including those that result in goals. I’m sure shot data is available but not necessarily the various particulars surrounding a shot, i.e. whether the goalie was screened or not; etc…

just with observation you could look at the goals the Hawks and Wings have scored and make an assesment on frequency of screens but it doesn’t necessarily correlate with effectiveness because you’d need the other data (the missed goal opportunities when a goalie was screened). Plus, and I don’t know how detailed it can get but factors like poor shooting could also account for missing the net when the goalie is screened. Sometimes Buff parks his rear in the crease very effectively but you get a wildly off target shot from Seabrook or Campbell near the point. In that case it’s not the screen that is ineffective, but the shooter.

ok, i’ve probably said too much, but I think your question is a good one, and it’s something I’d love to know as well. (I’m just not sure how easy it would be to collect such data without re-watching each game and quantifying each shot). As a side note: I wonder if being on the pp provides for more goalie screen opportunities and thereby more sucess or not?