Randall Flagg

Kessel deserved Crosby's first Conn Smythe for Stanley Cup Playoff MVP

What a good conversation

Fair. I agree that that's their vision. But I'm just talking about what I see happening on the ice. It's far from a speed game with speed players right now.

I get that that's the idea, but it certainly doesn't work, probably because in today's NHL guys like Vesey and Sheary aren't fast relative to their peers, even though theyre not slow. I mean, every time we play the devils their random crappy fourth liners make my jaw drop because everyone in this league is a freaking speed demon now. And they're even worse at playing at speed than they are skating fast. And Skinner is a beautiful skater, but not in a speedy way really. his game is way less speed to me and way more "barrage the net with shots from in close without getting knocked down because he's figure skater strong" I need to see more of Johansson but he strikes me as another "good skater" whose play identity isn't contributing to a team that's faster than all other teams We still seemed like one of the slowest teams in the league with Eichel, Mitts, Skinner, Sheary, and "mobile defense" last year

FWIW, it'd take like 7 years for the Sabres to adequately transfer their organization over to playing St. Louis hockey. We're very lucky it's not the only way. I didn't see much of a coherent identity in last year's team. I can see one forming in the defense, but the forwards are still completely scattered in my mind.

I'm almost positive that everyone you're arguing with is using the word "heavy" as a symbol for their play style and not their physical weight on planet earth.

Based on their work on the lower boards in the offensive zone, that'd be a safe bet! You know those times when the Sabres play against teams like that, and it's so maddening that theyre incapable of making anything happen? The game in Dallas this season was like that for me. I feel like that times a billion is how Jets/Stars/Sharks/Bruins fans felt in the playoffs

There are countless hockey styles that can be successfully incorporated into a winning team. Playing heavy hockey is still one of them, provided the heavy is of the Blues brand, and not the 2007 Flyers brand (and I don't think anyone sticking up for heavy hockey is insinuating they want Derian Hatcher as their 1D).

Fun fact - using that weighted-shot data that has been talked about, takig into account shot type and location, no NHL netminder had an "easier" job than Jordan Binnington this past year. St. Louis's D is really good. This counter only makes sense if I had said "having a large weight means you will always make the playoffs and if it's not your distinguishing style you can never win a cup." What I actually said was that the Blues used the fact that teams are overwhelmed by them physically in a way that allowed them to be puck-dominant and they won games that way, while also being suffocating defensively, both by raw skill (Dunn), smarts (Parayko, Pietrangelo) and physically snuffing people out (Parayko, Petro, Gunnarsson/Bortuzzo/everyone else) There is no sweeping claim being made here, it's just an observation about this year's cup winner.

The Blues owned the puck their entire playoff run. They 100% used the fact that they could body any opponent at will to receive and retain that puck. IMO it's a push between them and the LA cup teams for who was more effective on the boards, particularly down low. Teach us about Sissons!

Just you wait until we dump Risto and Scandella, and bring in Jake Gardiner ? Don't forget about Borgen though in the 'top prospects' list. And I know what you mean by "capable shutdown guy" so I understand why he skirted your post, but I think McCabe has decent defensive chops. I almost made my summer video focus "all the Dahlin plays we've forgotten" but I felt the thing I did instead was more pressing. I can't wait til I'm done with grad school

It was the one part of his skating that wasn't sublime, and came as a bit of a surprise to me. He lost a lot of races if he had to start from rest, though his top end speed is fine and obviously his edges are ludicrous. It was talked about a fair bit during the season from what I remember, so I don't think I'm seeing things. I would bet money it's just because he still has those skinny 18yo legs. A summer of squatting with Risto will probably do wonders. It feels like the one part of skating that's hard to improve past a certain level without bearing down and adding strength.

Detective time - his instagram story working out with Risto showed Risto doing squats. Usually when guys work out together, they are doing the same things - wouldn't make sense for Dahlin to be focusing on traps and shoulders while Risto hit legs. Therefore, Dahlin's first step will gain the needed explosiveness that was lacking his rookie year.

I already think our defense last year was better than it appeared, but was hindered by the forwards. And our defense got a fair bit better these last couple months.

I'm not joining the club because last night was enough stats reading for the next year or so. Also, I'd never remember to check it anyway

They wouldn't do it, but Rakell would be sweet. It'd probably end up being Henrique. I'd add to turn it into Henrique and Kase.

I think the LTIR of Callahan killed it. Unless he also had a NTC or something. But I always pictured loading up on salary when they still had real cap problems to yoink him outta there. And while I would have no qualms building the package into something that WOULD make them willing to give him up, I don't expect the Sabres to do the same. I still hope they take a shot on a player that is solid now and they think can be special, rather than a Tyler Johnson type.

Orders of magnitude difference there. The "error analysis" done by polling versus that done by any hockey nerd, or any real scientist, are simply not comparable from what I've seen. (I'm sure not all polls are conducted this way, I know a guy like Nate Silver is likely rigorous)

In 2008 it was, at least. as recently as 2017, I found an article that basically confirms it's impossible to find this information anywhere haha. For what it's worth, whatever you think of the data, it still outperforms all basic counting stats in all the ways that have been outlined over the years, and I dont think anyone is miscounting goals, assists, or points! If it didn't, we wouldn't use it.

I am dreadfully sorry for how long this is, I didn't mean it to get that way

I've spent a lot of time looking into this. Here's what I've found, in real time, as I've found it: It depends what kind of data we're talking about. When regression is performed for a model (like in RAPM charts or WAR stuff) I've seen the NHL's official html reports like this get scraped:http://www.nhl.com/scores/htmlreports/20172018/PL020672.HTM Because for those purposes, the main things you need to know are who was on the ice, what the score was, and when shifts start/end etc, and when events happen. The scraping code is available, you just have to dig into the references of a given model (they aren't shy about sharing what they do, it takes me hours to read through (without understanding a lot of) methods, conclusions etc). There are lots of big-data ways to combine the scraped data with other observations, whose natures I'm still looking into, like what will be discussed in the paragraph below. To give a generic answer to your first question - there are "RTSS employees" whose job it is to sit at each game and produce this stuff. We'll get into these guys with more detail later. The NHL also sources on-ice coordinates for shot events, which are the other main thing you're probably thinking of. From what I gather, the NHL isn't the only entity doing this, but when other people do analyses, they don't perpetually record every NHL game year after year, they eventually stop and write a paper with their results. For the guys you usually see here (McCurdy, Tierney, EvolvingWild etc) who create massive series of published papers on all this stuff, it appears that they generally use the NHL's data. One thing I'll say about this exploration I'm undergoing in real time - big data really does have its hands on everything, and I'm surprised at how deep and intricate this stuff goes. There are a lot of people way smarter than me who put stunning amounts of work into this stuff. I'm sifting through academic articles arguing about the impact of shot quality (implying they were using it to generate models then) from 2007. Apparently a data scientist named Ken Krzywicki was integral to the shot quality data generation in 2007? I could be picking up the context incorrectly though. I'm just kinda dumping more info here as I come across it. Apparently that Ken guy was frustrated at shot distance data being consistently under or over reported at certain venues back in 2009, and created a unique model for each arena that took this into account and allowed statistically meaningful comparisons based on the way the employees that year consistently reported. These employees are the "RTSS staff" whose job it is to do what you're basically asking about. He was successful, as far as statistical models go, at smoothing out these differences - before you could generate a model based on the RTSS staff's work, but its predictions didn't match the scoring results seen. A typical linear regression to isolate rink impacts on shot data was performed, and did its job, providing corrective factors for these tendencies. Resulting predictions matched the scoring results much better, being able to control for the "observer bias" of whoever was doing the work those years. Here's the paper: http://hockeyanalytics.com/Research_files/SQ-DistAdj-RS0809-Krzywicki.pdf I So, to the question of "well what if some people count stats differently than others" there are statistical methods that can get around observer bias and apparently have been in use at least since the Sabres last were winning playoff series. Here's a paper from a bunch of stats nerds that does zone entries - they wrote this for an analytics conference. http://www.hockeyanalytics.com/Research_files/Using Zone Entry Data To Separate Offensive, Neutral, And Defensive Zone Performance.pdf This would fall under the "individual project" category I mentioned above. Here's how they acquired data: "2 Data Collection and Assessment Each time a team advanced the puck into the offensive zone, the observers recorded a few key parameters:  The time on the clock  The player who sent the puck into the zone  The method of entry (e.g. carrying the puck in with possession, dumping it into the zone and trying to recover it, or miscellaneous other entries such as shots on goal from the neutral zone) This data was then merged with the official play-by-play, breaking the game into a series of segments from one zone entry or offensive zone faceoff to the next. The number of shots (including those that miss the net) and goals produced in each offensive zone possession were extracted from the play-by-play. This permitted assessments of each player’s contributions with the puck; to additionally identify defensive and off-puck offensive contributions, the list of players on the ice at the time of each zone entry was obtained from the official shift charts. In this manner, 330 games were tracked, covering a full season for the Flyers and Wild, a half-season for the Capitals and Sabres, and approximately 7-10 games for most other teams. For any manually-tracked data, it is important to assess the potential impact of scorer variability. Subjective assessments such as scoring chance counts can show major differences across scorers.[4] Since the puck crossing the blue line is a discrete, objective event, zone entry counts might be expected to be less problematic, but the scorers do still have a few decisions to make. The difference between carrying the puck in and dumping it in is usually clear, but the line between a pass with possession and a dump-in is occasionally tricky, as are some miscellaneous entries (e.g. when a player carries the puck back into his own zone and then turns it over). Additionally, since the goal is to assess offensive and defensive performance, plays where the offense dumps the puck in and goes for a line change without making any attempt to recover the puck were excluded, which introduces a bit more subjectivity. Several games were tracked by multiple observers. Comparing zone entry data from those games permits assessment of the integrity of the data and the viability of comparisons across data sets. Correlation matrices are given in Figure 1, indicating how often observers agreed on a given entry (more than 85% of the time) and what the most common discrepancies were (nearly two-thirds were when one observer omitted an entry that another recorded).The only significant scorer bias appears to be in the number of entries omitted; the distribution of entry types was consistent across observers and there was no apparent tendency for an observer to record his favorite team differently from what a fan of the opponent would record. Dump-and-change plays were explicitly tracked for Capitals games and were typically accompanied by having four offensive players leave the ice within five seconds. Therefore, subjectivity around omissions could be removed by recording every dump-in and algorithmically removing the dump-and-change plays from the NHL shift chart." That's how these particular guys for this particular paper tried to account for their own bias in their data. Here is a scraper that you can use on the RTSS reports if you wanted to: https://pythonhosted.org/nhlscrapi/ Here's some more work on adjusting for unreliability in RTSS event recording, from 2012-2013: http://statsportsconsulting.com/main/wp-content/uploads/Schuckers_Macdonald_RinkEffects_Final.pdf Here's how one man makes heat maps from zone charts he makes from NHL data: https://blog.icydata.hockey/2018/07/08/create-heatmaps-in-php-and-other-languages/ I just found an NHL JSON for a typical game. http://statsapi.web.nhl.com/api/v1/game/2015030411/feed/live It has the event location data!!! finally! I spent like two hours trying to find an example of how I could get a shot location from NHL data. for example, the 11th event recorded in this Sharks/Penguins game took place at coordinate (-69.0 (nice), 22) (I don't know the details of their mesh coordinates off hand, center ice is probably (0,0) with the rink going left-right). It was a wrist shot on Martin Jones by Matt Cullen, saved. THIS is the information, recorded by the RTSS guys whose job it is to do this, that gets turned into most of the charts we see. I dunno if there are other sources that track these things - other people will clearly do their own tracking for smaller projects like posted above, but I wouldn't be surprised if most of the big ones we see use this stuff. moneypuck's about section gives options to download data going back to 2008-09 if you wanted to make this stuff yourself from scratch. There are MASSIVE amounts of data here. Here's a random guy who did a bunch of work so that you can generate a shot chart of any game going back almost a decade, from these game JSON scripts or whatever theyre called. https://public.tableau.com/profile/icydata#!/vizhome/ShotChart_2/ShotChart So, now back to the question of who these people are. It's almost impossible to find details. First, people still regularly have problems with the job they do - reports of sketchy data appear common, which goes to the heart of your question. "castles made on sand" was a phrase used back in 2009, and it appears that I'd be lying to say it still isn't a concern now. It's possible @TrueBlueGED (who I think has been to some of these conferences) can provide details on how big the problem is, and what the community wants to do, or how it feels, about it. But yeah, there's no information for how many people generate this data, if they cross check each other, etc. Enough errors have been found in event logs, and the existence of the observer bias in the first place, appears to indicate that the NHL has a lot of room for improvement in this stuff. Now that I think about it, this is probably the major driving force for getting puck and player tracking chips developed - at the very least, coordinate information will become impeccable, and outright event classification much easier. I wish I had more to offer on this end, since it's basically what your question was. AHA, here's an article with some good info. https://www.nhl.com/news/off-ice-officials-are-a-fourth-team-at-every-game/c-38840 HITS is apparently what RTSS was.This is an old article, I'm sure some things have changed. I don't think shot location data was available when this article was written, that's clearly been incorporated somehow. Ultimately, it appears that caution should be applied with any stat or chart you see, because enough problems have been raised with this data that you can't assume it's all good. At the same time, I'm not sure I see reason to believe entire charts with thousands of minutes of sample size on them are useless or would be inverted with "more correct" data. These are people whose job it is to do this, after all, they aren't monkeys at typewriters or random number generators. I don't have a firm handle on what mistakes get made and how often they're made. Perhaps it's less important to note that Risto has exactly 2.3 zone exit passes per blah blah blah, and more important to look for general trends in lots of metrics, and absorb as much information (both numbers and on film) as you can to make judgments (which is a personal commandment of mine - i still cringe that y'all assume I'm just a stat head - I don't think I've even posted a RAPM chart outside of the post in which I explain how theyre made! Any chart, stat, or single video clip is pretty useless in hockey analysis, the best you can do is combine together as much information as time allows) Stat collecting appears to be about as messy and human as you'd expect it to be. Certainly not useless like one extreme would claim, and certainly not gospel like the other would. We have the info we have, it's not perfect, but it's better than nothing, and should use it responsibly. I trust the analysis done with the data more than I trust the data itself - these guys do a lot of work, and will tell you in mind-numbing detail what they did every step of the way and why!

Tampa would not trade us Anthony straight up, but even if he never grew another inch from what he was, it would be better to acquire him than someone like Tyler Johnson. I'm not even sure TJ would give us better right away, but he'd fall out of favor before Risto turned 27, and he's basically a known commodity with next to zero chance to take another step. I'd rather take a chance on the guy that a good scout thinks can be special, and risk him not getting there. The reason I zoom in on Cirelli (who looked the part next to guys like Stone at worlds as well) is because it's not even a chance or a risk - he could NOT become special and still be more useful three years from now than a Tyler Johnson would be. Maybe it's not Cirelli, maybe they can find a different player like that, but he's the one that gives me the same feels that Point did last year, and the way that went gave me renewed confidence in what I'm seeing down there.

You keep saying very sheltered. The only centers on Buffalo or Tampa that had a higher percentage of defensive zone starts are Larry, Zemgus, Sobotka, Paquette. He had more than any of Stamkos, Point, Eichel, Mitts etc. Just looking at random eastern conference teams, the forwards he saw the most are Domi from Montreal, Huberdeau from Florida, Ovechkin from Washington, Matthews' line from Toronto (he and Johnsson are the most common opponents of Cirelli from Tampa, and played on the same line), the Couturier-Giroux line from Philly. He drew the ROR line in his two Blues matchups based on most common opponents. He drew Toronto's top defense pair more than any other Toronto defense pair. Played against Dahlin more than any other Sabre D, Kessel more than any other Pen forward. In what way is this sheltered? He did this all while every modicum of offensive opportunity was given to Stamkos' line and Kucherov's line, while they were off chasing records, and still at 21 while being a PK staple and 58% dzone starts put up a ~20 goal, ~40 point season. He has a lot more to grow, and a lot more to give already even if he didn't grow, by getting out of the offensive shadow of TWO superstar lines.

Both he and Point are great in each direction of the ice. He was their best penalty killing forward and scored five shorties while doing it.