I had to look twice.... The man is a hitting ... shot blocking machine, If he could be a bit stronger on the puck who knows how good he would be
Sorry, but this shows how flawed your index is. You shouldn't be surprised that Butler has a high grit index, you should realize that if Butler is proven to be gritty by your numbers, your numbers are wrong. I'm not a huge Butler hater like some, but I can agree that Butler is as soft as they come simply by watching him play, despite what your numbers say.
Quote:
Show me a team with 4+ soft players and I will show you a team that is not in the playoffs.
Your 1st post talks about how Vancouver and Sharks are soft. Both made the playoffs. Your argument is kind of collapsing on itself.
Quote:
I think that I have stated my theory that there are no successful teams that have more than 3 soft players getting significant ice-time.
Redwings for one. Datsyuk may have an elevated RGI but it is skewed because he's a takeaway machine and very responsible with the puck. This is due to good positioning and exceptional ability to read the play, not due to battling and body sacrifice. So combine him with Filppula and Brunner and there's your 3.
Quote:
A great part of my concern was that when Gaudreau declares he wants to play in the NHL he would become the 4th or 5th soft player on the Flames roster
There's a difference between soft and shifty. Gaudreau is under-sized but has shown at every level to be able to play smart and avoid being pushed around. Who knows if he'll be able to continue that trend in the NHL but there's no statistical way of evaluating "shiftiness" which he has in spades.
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Is there any argument that Cammalleri, Hudler, Cervenka and Tanguay, Eberle, Hall, Gagner, Hemsky, Horcoff and Justin Schultz are not non-physical players?
No argument at all. But I don't need any stats to tell me that, it's evident by watching them play.
Quote:
They can add one or maybe 2 more soft players but after that there is a tipping point where their team success will fall off. They basically become the Oilers.
What?!?! So lets say they added Cammalleri and Tanguay to their 2012/13team and lost Bolland and Bickell. You really don't think they still win the cup?
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I'm really confused as to how giveaways / takeaways plays into grit. I really don't see the connection. Was Yelle a takeaway machine? I can't really remember.
The fact is you're using subjective statistics to confirm a subjective bias. With that said, I would try something like taking a random list of players, maybe 10% of the league over 4 years, and then I would give each of them a subjective gritty rank. Then I would create a table consisting of each of those players gritty score and every single variable that I thought contributed to grittiness. Then I would run a multiple regression model and refine it until it provided a statistically significant result with an acceptable correlation. Then I would use the model to predict the gritty score of all other players in the league. Then I would try to correlate a team's aggregate gritty score to its winning percentage. Then maybe I would be comfortable at least sharing my findings, even if it's total bunk. I might be using subjective data to predict a completely subjective target value to predict an actual value like winning percentage, but at least I would have put a modicum of thought into it, and not just said that A plus B plus C = RGI therefore soft teams don't win.
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I'm really confused as to how giveaways / takeaways plays into grit. I really don't see the connection. Was Yelle a takeaway machine? I can't really remember.
The fact is you're using subjective statistics to confirm a subjective bias. With that said, I would try something like taking a random list of players, maybe 10% of the league over 4 years, and then I would give each of them a subjective gritty rank. Then I would create a table consisting of each of those players gritty score and every single variable that I thought contributed to grittiness. Then I would run a multiple regression model and refine it until it provided a statistically significant result with an acceptable correlation. Then I would use the model to predict the gritty score of all other players in the league. Then I would try to correlate a team's aggregate gritty score to its winning percentage. Then maybe I would be comfortable at least sharing my findings, even if it's total bunk. I might be using subjective data to predict a completely subjective target value to predict an actual value like winning percentage, but at least I would have put a modicum of thought into it, and not just said that A plus B plus C = RGI therefore soft teams don't win.
This is a reasonable way to go about doing statistical analysis:
Quote:
Originally Posted by V
I would try something like taking a random list of players, maybe 10% of the league over 4 years, and then I would give each of them a subjective gritty rank. Then I would create a table consisting of each of those players gritty score and every single variable that I thought contributed to grittiness. Then I would run a multiple regression model and refine it until it provided a statistically significant result with an acceptable correlation. Then I would use the model to predict the gritty score of all other players in the league. Then I would try to correlate a team's aggregate gritty score to its winning percentage. Then maybe I would be comfortable at least sharing my findings, even if it's total bunk.
This, on the other hand, is just wanking with numbers:
Quote:
Originally Posted by ricardodw
Yelle 2005-6, first year stats were kept, 72 games 101 hits 56 blocked shots 42 take aways and only 18 Give aways.
Personification of a gritty player and totally born out by the stats.
The fact is you're using subjective statistics to confirm a subjective bias. With that said, I would try something like taking a random list of players, maybe 10% of the league over 4 years, and then I would give each of them a subjective gritty rank. Then I would create a table consisting of each of those players gritty score and every single variable that I thought contributed to grittiness. Then I would run a multiple regression model and refine it until it provided a statistically significant result with an acceptable correlation. Then I would use the model to predict the gritty score of all other players in the league. Then I would try to correlate a team's aggregate gritty score to its winning percentage. Then maybe I would be comfortable at least sharing my findings, even if it's total bunk. I might be using subjective data to predict a completely subjective target value to predict an actual value like winning percentage, but at least I would have put a modicum of thought into it, and not just said that A plus B plus C = RGI therefore soft teams don't win.
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This "grittiness" stat is an ass-backwards way to evaluate a team.
Takeaways, Blocked Shots, and Hits are net negatives. High numbers in these stats are an indicator of a bad hockey team, not a gritty one. If this metric you've invented actually has value, it would be in indicating which players are the ones you want to have on the ice the least.
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Well, the data is garbage, the premise is a mess, and, really, that subjective method of setting target variables is also very silly. So I'm not going to waste my time on that.
But I did do something easier that I believe takes this guys idea and at least normalizes it to a point where the numbers actually mean something, even if the data is garbage. My methodology is thus:
I assumed that Hits, Blocked shots and Takeaway/Giveaway differential actually defines grittiness. Silly presumption, I know, but it's somewhere to start.
I took each statistic and divided it by the total TOI for that player. This gives me a value for Hits/min, or BS/min or TGDiff/m. Then I normalized the value by prescribing it a z-value. Basically, (value - average) / stdev. This at least tells you how far off the average this player is in a certain stat. One of the biggest problems I have with his model is that hits and blocked shots and differentials are on completely different scales, yet they aren't weighted in any way at all. At least by normalizing the data I can give them a little bit of reference. Then I just add up the normalized values to provide a value. So normalized hits/min + normalized blocked shots/min + normalized diff/min = RGI.
I have the values for the top 80% of defensemen in TOI for the 2011/2012 season. The bottom 20% of minutes played defensemen obviously skewed the numbers significantly. Also, I used the last full season because I find the half season is too easy to find skewed numbers. Even though this is all nonsense.
I think the results are hilarious, a real who's who of difference-makers, but you be the judge to determine if this provides any value whatsoever.
Unfortunately it looks like garbage when I copy it from excel. Is there a way to make it look like a table?
Top 20:
Player RGI
Mark Fistric 4.975973205
Greg Zanon 3.861565575
Mike Komisarek 3.304797886
Chris Summers 3.293289769
Colten Teubert 3.134423126
Milan Jurcina 3.075349139
Luke Schenn 2.997295421
Anton Volchenkov 2.924629583
Keaton Ellerby 2.558543321
Brooks Orpik 2.533924316
Ryan O'Byrne 2.483385382
Stu Bickel 2.470009632
Ladislav Smid 2.468044708
Matt Carkner 2.352073475
Brett Clark 2.295956087
Rostislav Klesla 2.263849166
Brayden McNabb 2.261849437
Josh Gorges 2.156049342
Mike Commodore 2.125648697
Nicklas Grossmann 1.966998439
The rest:
Steve Eminger 1.956820257 Michael Sauer 1.940822795 Steve Staios 1.913365718 Andreas Lilja 1.872671143 Bryan Allen 1.817998455 Eric Brewer 1.722070623 Deryk Engelland 1.604637471 Andrew Alberts 1.59281374 Toni Lydman 1.578798301 Erik Gudbranson 1.526064069 Mike Weaver 1.488007313 Pavel Kubina 1.486402672 Mike Weber 1.476448398 Adam McQuaid 1.475911918 Aaron Johnson 1.371240238 Robyn Regehr 1.266297514 Derek Joslin 1.2657164 Brian Lee 1.213200465 Bruno Gervais 1.208337192 Aaron Rome 1.207854581 Stephane Robidas 1.17709376 Luca Sbisa 1.172245802 Victor Hedman 1.139304192 Mike Mottau 1.122304446 Niklas Kronwall 1.118928028 Kris Russell 1.108741988 Brent Seabrook 1.091267774 Keith Aulie 1.089034123 Anton Stralman 1.074873599 Brad Stuart 0.999912818 Shea Weber 0.996153748 Andrej Meszaros 0.988394768 Theo Peckham 0.988125059 Roman Hamrlik 0.967136568 Ryan Wilson 0.949980768 Chris Pronger 0.937869568 Sheldon Brookbank 0.937674016 Roman Josi 0.935576239 Adam Pardy 0.897713837 Kevin Klein 0.873108953 Mark Eaton 0.871242746 Jared Cowen 0.866422455 Clayton Stoner 0.845807651 Barret Jackman 0.835811228 Marc-Andre Bourdon 0.828675031 Marc Methot 0.820639782 Andy Sutton 0.818123633 Dennis Seidenberg 0.808776858 Ryan McDonagh 0.779114494 Andrew MacDonald 0.681709128 Anton Babchuk 0.62769422 Justin Faulk 0.624678788 Johnny Boychuk 0.623008824 Nick Schultz 0.615007735 Jan Hejda 0.603440629 Braydon Coburn 0.547101435 Ryan Ellis 0.545108571 David Schlemko 0.513986472 Shane O'Brien 0.452010158 Steven Kampfer 0.422601599 Zbynek Michalek 0.398418489 Adrian Aucoin 0.390935354 Chris Phillips 0.361750033 Cory Sarich 0.33758728 Jaroslav Spacek 0.321935403 Christopher Tanev 0.279453165 Hal Gill 0.253519897 Michael Del Zotto 0.179649805 Carl Gunnarsson 0.175186161 Alex Pietrangelo 0.149901327 Marc Staal 0.136865116 Francis Bouillon 0.124965759 Jason Garrison 0.112952717 Jeff Woywitka 0.05309464 Simon Despres 0.015261542 Tim Gleason 0.011078302 Ron Hainsey -0.023732775 Mark Giordano -0.031649423 Ben Lovejoy -0.049117683 Carlo Colaiacovo -0.065675866 Niklas Hjalmarsson -0.095093578 Johnny Oduya -0.109236385 Nikita Nikitin -0.151581973 Zach Bogosian -0.204689349 Philip Larsen -0.240413446 Fedor Tyutin -0.254333718 Oliver Ekman-Larsson -0.284324986 Matt Niskanen -0.294969605 Jack Hillen -0.324117156 Keith Ballard -0.331761347 Mike Lundin -0.36219539 Andrej Sekera -0.371536198 Kyle Quincey -0.399982133 Dion Phaneuf -0.410628504 Brendan Mikkelson -0.415257504 Sami Lepisto -0.44679986 Marco Scandella -0.495737273 Dylan Olsen -0.504492973 Kimmo Timonen -0.513784022 Alec Martinez -0.549004778 Grant Clitsome -0.595074245 Sheldon Souray -0.597931357 Jakub Kindl -0.63561618 Dylan Reese -0.648952454 Jack Johnson -0.66167131 Derek Smith -0.662068839 Raphael Diaz -0.663851828 Bryce Salvador -0.683398023 Cody Franson -0.686675947 Matt Gilroy -0.69671208 John Moore -0.697599476 Karl Alzner -0.716891373 James Wisniewski -0.730705836 Mark Flood -0.751587948 Ian Cole -0.773876588 Tom Gilbert -0.830079319 Scott Hannan -0.834479308 Jared Spurgeon -0.909759307 Duncan Keith -0.911586575 Henrik Tallinder -0.919694971 Mike Green -0.931819366 Chris Campoli -0.93885322 Dustin Byfuglien -0.941593473 Matthew Carle -0.946494049 Jeff Petry -0.954973349 Dmitry Orlov -0.971068645 Ed Jovanovski -0.999823544 Dennis Wideman -1.020173314 Chris Butler -1.027507544 Paul Martin -1.072049428 Lubomir Visnovsky -1.132687226 John-Michael Liles -1.139164913 Jordan Leopold -1.146137853 Nicklas Lidstrom -1.147191211 Steve Montador -1.163085923 Jamie McBain -1.179507992 Filip Kuba -1.251415828 Brett Lebda -1.328457201 Zdeno Chara -1.343770502 Slava Voynov -1.382647128 David Rundblad -1.391779759 Sergei Gonchar -1.448326878 Ian White -1.472929438 John Carlson -1.501704109 Rob Scuderi -1.527077131 Nick Leddy -1.550823299 Jonathan Ericsson -1.551536069 Erik Gustafsson -1.603735093 Sean O'Donnell -1.620674055 Sami Salo -1.640314574 Kent Huskins -1.651843915 Adam Larsson -1.652984368 Matt Hunwick -1.660858335 Andrew Ference -1.680194315 David Savard -1.81069739 Jay Bouwmeester -1.831494096 Randy Jones -1.8477256 Tomas Kaberle -1.870532223 Cam Fowler -1.879171171 Tobias Enstrom -2.000684161 Christian Ehrhoff -2.001021339 Drew Doughty -2.050147676 Brent Burns -2.074925699 TJ Brodie -2.176675516 Yannick Weber -2.210264098 P.K. Subban -2.215230037 Jake Gardiner -2.297895838 Marek Zidlicky -2.335816089 Cam Barker -2.452376376 Ryan Whitney -2.456279556 Marc-Andre Gragnani -3.018303456 Keith Yandle -3.356180872
Last edited by V; 08-28-2013 at 12:05 AM.
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unfortunately it looks like garbage when i copy it from excel. Is there a way to make it look like a table?
Try exporting the Excel file as tab-delimited text, then put CODE tags around the text when you paste it into your post. That isn't perfect, but it will help some.
(By the way, I always knew Mike Commodore was a top-20 defenceman in the NHL. Glad to have it confirmed by a Real Scientific Tool like RGI. )
Last edited by Jay Random; 08-28-2013 at 12:23 AM.
I don't really see an issue with the concept. These stats are arguably somewhat reflective of the overall attributes of a player's game. Perhaps it could be called something other than grit. Commitment, attention to detail? There are certainly elements to these stats that generally correspond to being either conducive or interruptive to the attempts of the opposition
With respect to the notion that takeaways mean you don't have the puck, and are negative, consider a couple of ideas. Firstly, faceoff percentages for teams are generally 50 percent +/- 3 percent (except Boston at 56 pct). Then factor in that guys like Gary Roberts look at their center who tries to pass to them, and instructs them to dump it in, so they can retrieve it. Some D's can find their way out of that, some have excessive takeaways.
The proposed methodology has gaps, indeed, and the counters also oversimplify. Give the guy due respect for creating an interesting conversation in the doldrums of the off season.
It is not finished or definitive, but I still like the concept.
Last edited by DeluxeMoustache; 08-28-2013 at 01:57 AM.
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I don't really see an issue with the concept. These stats are arguably somewhat reflective of the overall attributes of a player's game. Perhaps it could be called something other than grit. Commitment, attention to detail? There are certainly elements to these stats that generally correspond to being either conducive or interruptive to the attempts of the opposition
With respect to the notion that takeaways mean you don't have the puck, and are negative, consider a couple of ideas. Firstly, faceoff percentages for teams are generally 50 percent +/- 3 percent (except Boston at 56 pct). Then factor in that guys like Gary Roberts look at their center who tries to pass to them, and instructs them to dump it in, so they can retrieve it. Some D's can find their way out of that, some have excessive takeaways.
The proposed methodology has gaps, indeed, and the counters also oversimplify. Give the guy due respect for creating an interesting conversation in the doldrums of the off season.
It is not finished or definitive, but I still like the concept.
Yes. The stat he has created gives, on average, the highest scores to the worst hockey players on the selective teams. It's a stupid stat. Created to simply proves that what Ricardo values most in a player, provides the least advantage to a team winning.
It's incredibly useful in its uselessness. Not only is it not finished, nor definitive, it's a cherry picked stat that proves nothing because statistically, it's fluff, it means nothing, it's a joke.
Last edited by Wastedyouth; 08-28-2013 at 03:50 AM.
I assumed that Hits, Blocked shots and Takeaway/Giveaway differential actually defines grittiness. Silly presumption, I know, but it's somewhere to start.
I took each statistic and divided it by the total TOI for that player. This gives me a value for Hits/min, or BS/min or TGDiff/m. Then I normalized the value by prescribing it a z-value. Basically, (value - average) / stdev. This at least tells you how far off the average this player is in a certain stat. One of the biggest problems I have with his model is that hits and blocked shots and differentials are on completely different scales, yet they aren't weighted in any way at all. At least by normalizing the data I can give them a little bit of reference.
So in your mind:
1) hitting and blocking shots and winning or losing puck battles is a waste of effort? All hockey players should be striving to increase their shots on net and scoring stats? The team with the top two scorers (TB) should win at least most of the time. aside: only 19 of the top 30 scorers last year were on teams in the playoffs.
2) There is no statistical way to show that a player is soft relative to his peers and some players contribute to a team's effort in playing more physical?
3) the leader in the take-ways ( average of the top 30 Def - in 2012 40/yr) needs to weighted to be the equivalent of the leader in hits ( Average of the top 30 hitting D Men 190/yr) and blocked shots (top 30 average 160).
A hit, a blocked shot and a Take-away and a give away are all hockey events. Why in the world would you feel a need to make a takeaway and give away worth 5 hits? or 4 Blocked shots?
1) hitting and blocking shots and winning or losing puck battles is a waste of effort? All hockey players should be striving to increase their shots on net and scoring stats? The team with the top two scorers (TB) should win at least most of the time. aside: only 19 of the top 30 scorers last year were on teams in the playoffs.
2) There is no statistical way to show that a player is soft relative to his peers and some players contribute to a team's effort in playing more physical?
3) the leader in the take-ways ( average of the top 30 Def - in 2012 40/yr) needs to weighted to be the equivalent of the leader in hits ( Average of the top 30 hitting D Men 190/yr) and blocked shots (top 30 average 160).
A hit, a blocked shot and a Take-away and a give away are all hockey events. Why in the world would you feel a need to make a takeaway and give away worth 5 hits? or 4 Blocked shots?
weighting for an explanation
You're kidding, right?
Let's say, for the sake of argument, in any given game, the average number of hits is 20 and the average number of takeaways is 5. With your formula, a takeaway is worth just as much as a hit, even though the instance of them happening is much much less. But therein lies the question, is a takeaway just as important as a hit? Who the hell knows, it's so subjective and situational.
A hit, a blocked shot and a Take-away and a give away are all hockey events. Why in the world would you feel a need to make a takeaway and give away worth 5 hits? or 4 Blocked shots?
weighting for an explanation
I admit I am cherry picking here, but I will respond to this one part of the argument: Because not every event is of equal value. Would you give scoring a goal and winning a faceoff equal weight?
Hell, even by itself one hit is not necessarily as valuable as another hit. You can be credited for a hit for tapping a guy after he's passed the puck and the play has gone up the ice. That is worthless. Or if you were Cal Clutterbuck in Minnesota, you'd get credit for a hit without actually hitting someone.
Also, how do you quantify this?
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1) hitting and blocking shots and winning or losing puck battles is a waste of effort?
No. However, the accumulation of statistics that are assigned to these events are highly suspect, and based on the collection of available data it appears that it is likely impossible to quantify the actual value of a well-timed hit or a blocked shot. Furthermore, it remains to be demonstrated that the takeaway / giveaway statistic has any correlation at all to winning or losing "puck battles". I think what V's analysis demonstrates is that 1) these categories will not work as a means to determine "grittiness (if such a thing can even be isolated in this manner), and 2) your attempted RGI is utterly useless.
Quote:
Originally Posted by ricardodw
...All hockey players should be striving to increase their shots on net and scoring stats?
No. All hockey players should be focused on WINNING and not padding their own statistics in any single category.
Quote:
Originally Posted by ricardodw
...aside: only 19 of the top 30 scorers last year were on teams in the playoffs.
So what? I'm still waiting for you to apply your index to all 30 teams, and to demonstrate its value. The fact that 2/3 of the League's top scorers (which I do not deem to be an insignificant ratio) doesn't have any bearing one way or the other on the value of your attempt to quantify grit.
Quote:
Originally Posted by ricardodw
2) There is no statistical way to show that a player is soft relative to his peers and some players contribute to a team's effort in playing more physical?
At this point, no, there is not. And the reason for this has a great deal to do with the problematic definitions of "gritty" and "soft", as well as the futility in attempting to assign values to these labels through ANY events that are measured within the games played.
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Quote:
Originally Posted by woob
"...harem warfare? like all your wives dressup and go paintballing?"
I wanted to start with this, because this is so important. I do not have pre-conceived ideas in my mind that I try to use stats to verify. That's not how statistics works. I think that's the cardinal rule you're breaking in this thread, and your mind is so made up that you appear to be unable to accept the validity of the other viewpoints that you're seeing in this thread.
Quote:
1) hitting and blocking shots and winning or losing puck battles is a waste of effort? All hockey players should be striving to increase their shots on net and scoring stats? The team with the top two scorers (TB) should win at least most of the time. aside: only 19 of the top 30 scorers last year were on teams in the playoffs.
No, you're misreading things. They aren't a waste of effort at all. They are an indicator that the other team has the puck. This has been mentioned a couple of times in this thread, but you're not seeing it, so maybe I can flesh out the argument for you.
In all the time that people have tried to understand what drives wins there has only been one fundamental answer. Goals. (Throwback to mc in 2006 for those that remember)
The problem is that goals are a rare event, so the advanced statistics world has had to understand what drives goals. And the consensus answer is possession. Possession drives goals which drives wins. It is an undeniable truth that has been demonstrated over and over again. You need to have the puck to score. And you need the other team to not have the puck to keep them from scoring.
This means that metrics that drive possession can be used to predict success. Mind you, you can't just cherrypick a stat that you like and show that it will project success. You need to use valid statistical methods. But that doesn't remove the fact that possession metrics drive wins.
Now think about your gritty stats. Hits, blocked shots and takeaways all increase dramatically when a team does not have the puck. Giveaways are actually an indicator of possession, because you need to have the puck to give it away. Hits and blocked shots are actually more intuitive, because they do not guarantee you got back posession. Just because you hit someone, doesn't mean they gave up the puck.
So to make a long story short, no, hitting and blocking are not a waste of time. But what would be even better would be to not have to hit or block shots because you always have the puck.
Quote:
2) There is no statistical way to show that a player is soft relative to his peers and some players contribute to a team's effort in playing more physical?
I think I just showed you a statistical way. Of course, it's bunk. I also think that it's a narrative that mainstream media likes to use that has little relevance to what's actually happening out there. That's not to say that being soft doesn't affect results. I only mean to say that if a player has attributes that will increase or decrease the potential for success, it will be shown in the real metrics that matter.
Quote:
3) the leader in the take-ways ( average of the top 30 Def - in 2012 40/yr) needs to weighted to be the equivalent of the leader in hits ( Average of the top 30 hitting D Men 190/yr) and blocked shots (top 30 average 160).
Not at all, I was merely trying to add statistical relevance to your model. It makes no sense to add absolute values when the scale of those values don't match. If there are 10x more hits on average than the takeaway/giveaway differential, by adding the absolute values you are saying that hits are 10x as important as the differential. Which, to be completely honest, is perfectly fine, but it's not what you were getting at in your initial analysis. You were just adding values. So I decided to add some statistical relevance by at least normalizing the values so they could be viewed as equals.
There's nothing saying you couldn't add weighting to each value. In fact, that's exactly what a regression model would do for you in the method I had originally proposed you try.
Quote:
A hit, a blocked shot and a Take-away and a give away are all hockey events. Why in the world would you feel a need to make a takeaway and give away worth 5 hits? or 4 Blocked shots?
That's not what I'm doing, though. I am normalizing values so that the value is no longer an absolute value, but its distance from the mean relative to its standard deviation. So now it's not "Player A has tons of hits!! He must be gritty!" but instead we're looking at how many hits he has relative to the rest of the league. This isn't rocket science.
Quote:
weighting for an explanation
Clever.
You know, I thought about this earlier, and it's too bad that people like you who try their hand at stats get slapped down so vehemently, like a lot of comments in this thread, my first one included. I think it's great that people are trying to quantify the nuances in the game. I think that your methods are misguided, but I'm hoping that this post at least gives you a little insight into some statistical methods that you might want to try for future endeavours. There's a lot of good stuff out there worth reading as well, so I strongly encourage you to read into it and get better at this. We need more people interested in stats.
But then I remember that you're the guy that brings up Bouwmeester in every gd thread. So you deserve no sympathy.
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But then I remember that you're the guy that brings up Bouwmeester in every gd thread. So you deserve no sympathy.
That's the point of this thread, ricardodw believes certain things about hockey and he's trying to come up with a stat to confirm it. It's statistical analysis done in reverse, come up with the conclusion and work backwards to quantify it.
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I went and took the 2012-13 stats and did the Hits + blocked + take-aways - give aways and just divided by games played. (KISS)
I took everyone who played 24 or more games for the teams.
I then set the limit for being soft was 1 net physical event per game for a forward. As Defense get more ice time and a greater opportunity for physical events (more blocked shoots that happen without intent) I set this to 1.5.
This analysis gave 96 soft players 3.2/ team - 76 forwards and 20 d-men.
Here is how they are distributed. I was wrong in that Detroit made the playoffs with 6 soft players.... which I thought was near impossible..
You can look at the distribution and interpret the results how you want---
team ---------- Pts ---------- goal diff ---------- soft player count
P - CHICAGO ---------- 77 ---------- 53 ---------- 2
Z - PITTSBURGH ---------- 72 ---------- 46 ---------- 1
Y - ANAHEIM ---------- 66 ---------- 22 ---------- 3
Y - MONTRÉAL ---------- 63 ---------- 23 ---------- 3
X - BOSTON ---------- 62 ---------- 22 ---------- 2
X - ST. LOUIS ---------- 60 ---------- 14 ---------- 1
X - LOS ANGELES ---------- 59 ---------- 15 ---------- 3
Y - VANCOUVER ---------- 59 ---------- 6 ---------- 4
X - TORONTO ---------- 57 ---------- 12 ---------- 2
Y - WASHINGTON ---------- 57 ---------- 19 ---------- 3
X - SAN JOSE ---------- 57 ---------- 8 ---------- 4
X - NY RANGERS ---------- 56 ---------- 18 ---------- 1
X - DETROIT ---------- 56 ---------- 9 ---------- 6
X - OTTAWA ---------- 56 ---------- 12 ---------- 4
X - MINNESOTA ---------- 55 ---------- -5 ---------- 4
X - NY ISLANDERS ---------- 55 ---------- 0 ---------- 3
COLUMBUS ---------- 55 ---------- 1 ---------- 2
WINNIPEG ---------- 51 ---------- -16 ---------- 3
PHOENIX ---------- 51 ---------- -6 ---------- 2
PHILADELPHIA ---------- 49 ---------- -8 ---------- 3
DALLAS ---------- 48 ---------- -12 ---------- 4
NEW JERSEY ---------- 48 ---------- -17 ---------- 4
BUFFALO ---------- 48 ---------- -18 ---------- 5
EDMONTON ---------- 45 ---------- -9 ---------- 7
CALGARY ---------- 42 ---------- -32 ---------- 5
CAROLINA ---------- 42 ---------- -32 ---------- 4
NASHVILLE ---------- 41 ---------- -28 ---------- 5
TAMPA BAY ---------- 40 ---------- -2 ---------- 1
COLORADO ---------- 39 ---------- -36 ---------- 1
FLORIDA ---------- 36 ---------- -59 ---------- 4
The Following User Says Thank You to ricardodw For This Useful Post:
There's nothing left to clarify. We get what you are trying to say.
It's just, your metrics of measuring "gritty" and "soft" are ridiculous, subjective and based wholly on your own biased opinion.
PS. I'm sure you don't care, but your RGI index has made it's way into the twitter sphere and you've become a laughing stock to the Advanced Stats circles. So. Kudos.
had no idea this went viral so I just looked. I have to say, if I ever came up with something significant enough to be mocked in depth by Pension Plan Puppets, I'd be pretty proud of it. no lie.
The Following 2 Users Say Thank You to Inglewood Jack For This Useful Post:
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