This week I am joined by my good friend Dan to talk about the Minnesota Wild’s season, including rookie standouts and potential offseason contract scenarios. We look ahead to the NHL Playoffs and discuss whether the Wild have a snowball’s chance in hell to upset the Chicago Blackhawks. We also make our predictions for all first-round matchups.
The final score was certainly not indicative of how Thursday night’s game played out between the Wild and Coyotes. Phoenix ended up with the better Fenwick numbers (23 SOG + 10 MS Wild = 33; 28 SOG + 21 MS Yotes = 49) but to the eye, all their shots were coming from the blue line or the perimeter. Minnesota seemed to be possessing the puck much more than Phoenix, so I’m surprised the numbers came out the way they did. I think part of it is a score effect—to start the third period, the Wild were up 4-1 and Phoenix played very aggressively while Minnesota sat back a bit to end the game.
I tried my hand at tracking scoring chances this evening, and while it was my first effort, I checked them against the PBP after the game:
| Period 1 | Period 2 | Period 3 | TOTAL | |
| Minnesota | 8 | 7 | 2 | 17 |
| Phoenix | 5 | 3 | 7 | 15 |
7, Cullen–1
9, Koivu–1
10, Setoguchi–1
11, Parise–1
15, Heatley–4
16, Zucker–1
20, Suter–2
21, Brodziak–2
46, Spurgeon–1
63, Coyle–1
96, Bouchard–1
I’m not a true believer in momentum, except that the team has been playing better over the last couple of weeks, and they will need to play well as a team to take on the Ducks on Friday. Anaheim is in the “due-for-a-regression” spot that the Wild were in last year, so we’ll see if that starts this week. Backstrom will probably get the night off, so we’ll see if Darcy Kuemper can continue his stellar play and keep the Wild rolling along.
Despite notching an assist on the Wild’s only goal of the game last night, the 20-year old Finn who was styled a Wunderkind entering this year has generally looked like he could not shoot his way out of a paper bag. Mikael Granlund has 16 shots on goal in 12 games this year, just 1.33 per game, while his CorsiOn is a capital-D Dreadful -28.60, which ranks ninth-lowest in the league among forwards with more than five games played. While he has looked better the last couple games and might finally be developing some chemistry with fellow member of the -20 corsi club, Devin Setoguchi, Granlund has just looked overmatched and overwhelmed to me this year.
Meanwhile, fellow rookie Charlie Coyle has found himself in the enviable position of skating alongside Mikko Koivu and Zach Parise beginning with just the second game of his career. Coyle has not dented the scoresheet yet and actually has less shots per game than Granlund, just 6 SOG in 5 GP (1.2 shots per game.) Coyle is one of just five Wild forwards with a positive CorsiOn this year (Koivu, Parise, Coyle, Brodziak, Clutterbuck.) Both are getting difficult minutes, with Coyle’s QoC number at a hefty 1.15 and Granlund’s at 0.80. But one thing Coyle is doing better than Granlund is drawing penalties: 3.1 drawn per 60 minutes vs 1.0 taken for Charlie, 0.4 drawn, 0.8 taken per 60 for Mikael.
Like a lot of folks, coming into this season I thought Granlund wouldn’t have much trouble adapting to the NHL game–he had played at a high level in one of the toughest European leagues for several years, and showed promise in the AHL before getting injured. Now I’m wondering what factors may be contributing to his poor start to his Minnesota career. The rinks are a different size in the SM-liiga, bigger than the NHL but smaller than Olympic size if my research is correct…but Granlund played a number of games with Houston during the lockout so that’s probably not it. I’m starting to wonder if his role as a playmaker and his lack of size and defensive skill aren’t a great match in Mike Yeo’s system. While Coyle gets to skate with All-Stars and his direction is pretty much, “hey kid, shoot the puck when they pass it to you,” Granlund is responsible for setting up guys and creating chances, and thinking three moves ahead in the NHL chess game has got to be tougher than the AHL or the liiga. Plus, Coyle has gotten his assignment and stuck with it, while Granlund has skated a few games with Cullen, a couple with Bouchard, and even one with Konopka. The constant for 64 has been Setoguchi, who has had an equally dreadful season start though he has shown signs of life lately. I don’t really want to get into a chicken v. egg argument, but Granlund and Setoguchi have been tied together for the first quarter of the season and one figures that if Yeo was going to split them up, he would have done so by now.
I feel like I’ve written a lot about line combos the last few weeks, and my reaction has been…alright yeah, I guess I like that. Sure, that could work too. At the risk of being a broken record, I do like the newest iteration of the Wild’s second line with Dany Heatley rejoining Setoguchi with Granlund as the pivot. Heatley and Gooch played together in San Jose, and again last year on the Wild’s top line. They do seem to have some good chemistry, and let’s face it, Heatley hasn’t resembled a first-liner for three years. I’ve had a sneaking suspicion that his place on the team is on the second unit for a while, so we’ll see how he reacts.
I am trying to remain patient with this team, and Setoguchi in particular, but one can only watch so many games where the team goes 10-minutes at a stretch without putting a shot on goal before wondering when it’s time to give guys like Jason Zucker a shot. We may already be seeing signs of this, as Bouchard and his expiring contract were a healthy scratch last night against the Canucks…you can only roll the dice so many times before you have to get new dice.
Shake ‘em up, shake ‘em up, shake ‘em up, shake ‘em. Mike Yeo is playing the lineup card like Ice Cube plays craps. He put Mikael Granlund and Devin Setoguchi on the fourth line with Zenon Konopka earlier this week at Phoenix, and while they seemed to respond to his “message,” the team still lost. Now, Yeo is moving Dany Heatley off the top line, to the second line with Bouchard and Matt Cullen, while Charlie Coyle will get time with Mikko Koivu and Zach Parise versus the Canucks. I think this is less of story than it seems to be on its face…Heatley has not played like a top-liner in years, and if Coyle is the guy they want to see next to Koivu and Parise, might as well get him started earlier. The team is floundering so there’s not a lot to lose. Between the line changes and the trade with the Rangers, I like the club’s approach that the status is definitely not quo–they signed Parise and Suter to build a playoff-worthy team and if players aren’t producing, they need to know that their lineup spot is not safe (unless your name is Matt Cullen…)
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Did you watch the 30 Rock finale? Do you remember the scene where Liz Lemon gets onto an elevator with Conan O’Brien and she shoots him down, prompting him to wonder when he will ever lose his virginity? I bring this up only because the shot had a weird green-screeny look to it, like they weren’t actually in an elevator but on a stage. My theory on this is that because he’s 6’4″ and she’s 5’5″ they couldn’t actually frame the two of them standing shoulder to shoulder, so they had them do it in front of a screen (these are the things that I think about sometimes…) And the only reason I bring this up at all is because, like a lot of people, I’ve seen Ryan Suter’s lousy Corsi stats and have started to wonder about that huge contract and whether he will be the same player as he was in Nashville…we’ll come back to this later.
After watching the Minnesota Wild beat the Chicago Blackhawks earlier this week, I’m staying cautiously optimistic about the team’s potential this year, but I’m not deluding myself either–they still look like a team that’s developing a number of young players and building team chemistry. Between the injuries last year, the new faces this year, and the lockout-shortened training camp/lack of preseason, I’m sticking by my it’s-too-early-to-really-know-anything stance. They hung with the Blues and Blackhawks, undoubtedly two of the conference’s better teams, but they also let the Blue Jackets hang around with them, so I’m not too sure what to make of them yet.
Back to Suter. Imagine you start your NHL career paired up with a guy who’s 6’4″ (without skates) and 230 or so pounds. He’s like Leroy Brown–baddest man in the whole damn town. He clears out the crease with the greatest of ease, lays bone-crushing hits, sometimes goes a little too far, and will go toe-to-toe with any opponent. You play your first seven years in the League with this man, your playstyle literally develops alongside his. Then, you get your opportunity to get capital-p Paid so you go to Minnesota, where they pair you up with this guy. Jared Spurgeon is listed at 5’9″ and 185 pounds. If you believe that, I’d love to talk to you about a fantastic real estate opportunity in beautiful northern Minnesota.
Don’t misunderstand me–I like Spurgeon fine. He’s a great skater, he sees the ice well, knows how to make a good pass, and he’s got a knack for getting out from behind his own net and over the attacking blue line in just a few seconds. Plus he’s played for Mike Yeo for a year so he has more familiarity with the system than Suter. I’ve got a special place for Spurgeon in my heart because he’s about the same size as me, and us little guys gots to stick together. But he’s not a top-line NHL defenseman. His game is too one-dimensional to be getting top-pair minutes, and I think it’s clear that Suter and Spurgeon just don’t have the chemistry–probably because if you cloned Spurgeon and duct-taped the two together then doused the double-Spurgeon with water, it would still weigh less than Shea Weber after he’s eaten a double quarter-pounder. Seriously, if you gave him a two-handed axe, Weber would fit right in fighting alongside Qhorin Halfhand and the Watchers on the Wall. But I digress…let’s get to the numbers.
I’ve wanted to do some Wild usage charts for a little while now, and they’re fairly informative but it’s still too early and because Yeo has had the same 12 skaters all year there is a lot of overlap. Check out Rob Vollman’s page describing usage charts if you need a refresher. In short, the X axis here is percent of offensive-zone starts (excluding NZ,) the Y axis is CorsiRel QoC (quality of competition,) and the bubble is CorsiRel, with solid bubbles representing players who see more shots at the opponent while on the ice, and white bubbles representing players who are getting rubber thrown at them. Numbers are current as of 1-31-13, I whipped up these charts before last night’s Anaheim game:
I wrote earlier this week that I’m happy with the way Mike Yeo is switching up the lines, and that extends to the recent shake-up on the blue line. Suter’s CorsiRel bubble actually overlaps Clayton Stoner’s completely, though it sort of looks like a little Death Star right there on the chart (30 Rock, Game of Thrones, Star Wars–NERD TRIFECTA!) Enter: Jonas Brodin. In a Wild system that’s loaded with prospects, Brodin has earned himself a spot with the big boys, and in just four games, he has impressed the coaching staff enough that it looks like he’ll stick around–plus he and Suter seem to have good chemistry so far. I am going to dig more into the Suter-Spurgeon vs. Suter-Brodin pairings in another article this weekend, though with a podcast still to record and the Super Bowl coming up, I’ll have to find the time somehow.
The top line of Parise-Koivu-Heatley is driving possession for the team and getting an almost exact 50-50 split offensive zone/defensive zone. Koivu and Parise are fantastic 200-ft players, while Heatley is as disinterested in backchecking as I have ever seen in a player. Granlund and Setoguchi are still seeing the toughest competition of anyone on the team, and it’s becoming clear that Granlund just can’t handle it. His numbers are downright lousy so far, and I wonder if Yeo will continue his throw-him-into-the-deep-end approach or if we’ll see a shake-up. I’m just saying, Kyle Brodziak did a fine job centering the second line last year through all the injuries, and Matt Cullen still has a heartbeat. Pierre-Marc Bouchard is getting the sheltered treatment, which is fine by me. He’s been making plays and getting game winners, so just let him do his thing. When Granlund and Bouchard get caught together in the defensive zone, though, it’s just brutal.
I will not overreact to Marco Scandella…I will not overreact to Marco Scandella…I will not overreact to Marco Scandella. But look at that big red bubble! It’s way too small a sample, only three games included here, but that’s an OZ St% of 28.6, a CorsiRel QoC of 1.28, and a CorsiRel of 18.6. Plus his on-ice Sv% was .813, so no favors there. Ok, I got that out of my system. I really think this guy can be a solid top-four defenseman for the Wild, so now that he’s back with the team I’ll continue to eagerly watch his progression.
Alright that’s all for now. Thanks for reading, make sure to follow me on Twitter @Hashtag_Hockey, and check back later today probably for my per-game analysis of Suter-Spurgeon vs Suter-Brodin. Until then, LIVE EVERY WEEK LIKE IT’S SHARK WEEK!
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Matt Cullen is a good ol’ Minnesota boy, the pride of Virginia MN, proud alumnus of St. Cloud State…the prodigal son returneth after playing for five teams before the Wild. He does a lot of things right, he buys into the system, he gives 110%, he skates hard, he’s a mentor to the younger guys. And at the ripe age of 36, he belongs on the third line. He’s a quality guy who plays quality minutes, but he shouldn’t be used outside of a bottom-six role. He makes memorable plays, like late in the game Tuesday at home vs Columbus when the Wild were killing one of the four penalties they took in the third period, Cullen dove for a loose puck and swatted it the length of the ice. He plays committed defense and still has a quick shot but he just doesn’t do enough on offense for me to think he belongs on the second line, where Mike Yeo had been playing him alongside Mikael Granlund until recently.
Tonight’s lines were Parise-Koivu-Heatley; Bouchard-Granlund-Setoguchi; Cullen-Brodziak-Clutterbuck; and Powe-Konopka-Mitchell. While the Bouchard-Brodziak-Clutterbuck line was an interesting experiment and sort of made for a nontraditional pairing, I like the lines that the Wild put out much better–Cullen in the bottom six and a healthy Pierre-Marc Bouchard in the top six. Bouchard showed that he can still put pucks past goalies tonight on a beautiful 200-ft play that started when Marco Scandella collected the puck behind the Wild net and made a nice breakout pass to Bouchard, who dished it off and then had it returned to him as he entered the Blue Jackets’ zone with speed and zipped a snap shot right over Chris Mason’s glove for the game winner. It was the type of play that the Wild just didn’t seem to have the ability to make last season–plus it was just nice to see some production from anyone not named Parise.
The Wild got off the schneid tonight after going 0-2-1 the three previous games. They should have beaten St. Louis but I guess just to hang with one of the top teams in the conference is something to hang your hat on. Though the Wild got just 11 5v5 shots while the Blues had 25, so perhaps the score was deceiving in the first place. Minnesota was able to hang with Detroit for a little while, but that game quickly unraveled.
We might have seen our first glimpse of the top defensive pairing of the future tonight, as Ryan Suter looked a lot better and young Jonas Brodin was impressive as well. A lot has been made about the Wild’s thin blue line, but I am hopeful Scandella and Clayton Stoner can prove themselves to be at least serviceable on defense.
While some may not consider it a “real” rivalry, the Minnesota-Chicago games are always entertaining, and despite the technicality that the franchise moved to Dallas, the two teams always dial it up a notch. The Wild will be undermatched on Wednesday but perhaps they’ll find a way to hand the Blackhawks their first loss of the season.
Updated team-level fancy stats:
Fenwick (all-inclusive): 0.494
PDO (5v5): 0.995
Thanks for reading, and be sure to check out my weekly fantasy hockey podcast, and follow me on Twitter, @Hashtag_Hockey!
It’s a rainy day in Southern California–a perfect time to stay inside and look at some early returns from the Minnesota Wild’s first three games. I’m having trouble navigating the behindthenet.ca site, so for now I’ll have to settle with just a basic look at Corsi-related stats. Once I figure out what I’m doing wrong over there I’ll be able to dig deeper and get into some more thorough analysis. All numbers posted below are even-strength.
Top Line: Parise-Koivu-Heatley
The first thing I see is that Mike Yeo has been capitalizing on the club’s three straight home games and using the top line in an opportune way. All three skaters have been started heavily in the offensive zone (65.2% for Parise and Heatley, 62.5% for Koivu) and all have faced soft competition (CorsiRel QoC of around -2.0 to -2.1 for Parise and Heatley and -1.9 for Koivu.) The skaters have jumped on the opportunity and directed pucks on net–Corsi On right around 7 for the wingers and an astounding 19.05 for Koivu. Obviously the sample is small that that’s impressive. The line has looked very good to my eyes, but I see that they haven’t had great puck luck, the three have on ice Sh% just over 4. This line is absolutely driving the Wild offense right now with CorsiRel of 14.5-14.7 for Heatley and Parise, and 31.4 (!!) for Koivu. We’ll see how the coach continues to use the top line on the road, but if the team wants to make the playoffs, they’ll need to get some offense out of the rest of the roster, which brings us to…
Second Line: Cullen-Granlund-Setoguchi
I wrote earlier this week about how I don’t particularly like Cullen on this line, but I see why Yeo has him paired with Granlund. I hope Yeo mixes up the pairings as the season goes on so for now I’ll hold my tongue. This line has been getting the tough competition–CorsiRel QoC of 2.8 for Cullen, 3.0 for Seto, and 3.3 for Granlund! Their possession numbers are paying the price for it, Granlund has a CorsiOn of -4.5 but Cullen (-10.8) and Setoguchi (-13.9) are seeing the ice tilted against them quite severely. Mike Yeo seems to be sheltering Granlund (OZ Start 60%) for sure and Setoguchi a bit (54.5%) with Cullen getting the nod defensively (45.5%). Granlund and Setoguchi have had a bit better luck, but their on ice Sh% are still pretty low (5.88 Setoguchi, 6.667 Granlund, and a big fat goose egg for Cullen.) I’m sure these usage numbers will change as the sample gets larger but I’m surprised to see this line get the brunt of the tough minutes when I thought they would go to better-established two-way players like
Third Line: Bouchard-Brodziak-Clutterbuck
Brodziak and Clutterbuck have a rep for playing solid 200-ft games, so I’m a little surprised to see their middling competition numbers–Bouchard has 0.5 CorsiRelQoC and the others actually negative, though just barely. Bouchard and Clutterbuck have a CorsiOn just over 5 right now while Brodziak is seeing a lot of rubber flying his way, with a -13.3. All three have OZ Start% north of 50 (56.5 Brodziak, 59.1 Clutterbuck, and 61.9 Bouchard) but they are winding up at the other end of the ice–OZ finsih% approaching 40. No es bueno. I like the composition of this line with the three bringing different playstyles to the table, so perhaps they need some more time to gel, but I’ll be keeping an eye on this line because these early numbers point to them being somewhat of a liability.
I don’t really want to get into the fourth line of Powe-Mitchell-Konopka, but the only number I’ll point out is their on ice Sv% of .909 (TM, ZK) and .917 (DP). Ouch.
Blue Line
The Wild blue line is hurting right now, but Jonas Brodin is slated to get the start tonight against Detroit. Scandella is down in Houston but I liked what I saw from him last year so I hope they bring him up soon. Dumba has been practicing with the team but hasn’t seen game action yet–I’d bet the money in my wallet that they don’t burn a year of his ELC but I sure want to see him get a couple games.
Spurgeon and Suter have seen the toughest competition of the defensemen, though their CorsiRelQoC are only about 0.7 to 0.8. Their CorsiOn is not great though, -9.5 for Spurgeon and -16 for Suter. The goalies haven’t helped either, as Suter has an on ice Sv% at .900 and Spurgeon .889. It looks like Spurgeon has drawn a couple penalties, which is good to see, but until the blue line gets a little clearer I don’t think there’s too much to squeeze out of these numbers.
I’m gearing up for continuing my podcast this weekend, should have it ready by Monday–if you have fantasy hockey questions, send them to hashtaghockey [at] gee mail dot com or tweet me @Hashtag_Hockey. Thanks for reading!
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Backstrom misplays puck, Erat scores NSH @ MIN 1-22-13
^^Having trouble embedding NHL videos into my blog so here’s the link.
Just a few thoughts on the first three games of the Minnesota Wild season so far–plus the start of a recurring column looking at Wild team possession stats.
Thanks for reading, and make sure to follow me on Twitter, @Hashtag_Hockey. Until next time!
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Recently, an interesting article was published to the NHLNumbers website examining the behavior of skaters and their shooting tendencies, or how much they could be considered a “Puck Hog.” The article, written by Ben Wendorf, proposed an elegant way of measuring how frequently a player opts to shoot the puck.
Simply, the metric looks at the proportion of On-ice Fenwick (A’) that was represented by a specific player’s Fenwick shots (A). This number can be thought of as the Attempted Shot Ratio.
Ben found that this number did not correlate to things like shooting percent or quality of teammates, but that players showed the same tendency year after year. Thus, he concluded, “With high repeatability, and little connection to shooting talent, player development, or teammates, this seems much more like a behavioral activity.”
The article got me thinking about some of my own research that I did way back in the first month or so of writing this blog. I was messing around with players’ shots on goal versus missed shots, and determined what proportion of Fenwick shots were shots on goal (SOG/FEN), which I called Shooting Ratio. I had similar findings—that players could be productive even if they missed the net a lot, and the behavior was stable year after year. Something that stuck out to me during that research was the almost perfectly normal distribution of skaters’ ability to hit the net, so it was not much of a surprise to me when I checked with Ben and found that Attempted Shot Ratios also showed that perfect bell curve that we stat heads swoon over.
After mulling things over, I thought it would be insightful to combine Ben’s “Puck Hog” metric with my missed shot data to provide more of a two-dimensional look at player behavior. Or, simply,
How often do players choose to shoot, and how often do they hit the net?
Method:
I decided to look at just Minnesota Wild forward skaters from 2011-12 for this post, although I am working on a bigger dataset to look at more of a sample. I crunched all the numbers to determine players’ even-strength Attempted Shot Ratios (how often does the player take the shot when he’s on the ice) and Shooting Ratios (what proportion of those Fenwick shots are shots on goal) using the formulas listed above. All computations use 5v5 data.
Population Parameters
After initially just comparing the 11-12 Wild players to each other, I realized that I would be better served using ‘population’ data that came out of Ben’s and my research. Specifically,
Attempted Shot Ratio: Mean = 0.244, SD = 0.05 (N = 2177 NHL forwards, 2007-2012)
Shooting Ratio: Mean = 0.737, SD = 0.074 (N = 492 NHL forwards > 10 GP, 2011-12)
Results:
The tables below list Minnesota forwards with their attempted shot ratio and shooting ratio. The z-scores associated with each are calculated using the population parameters listed above. Due to several players who wore the same number at different times, some numbers have been substituted for dummy numbers (Wellman, Taffe, Veilleux.)
| Name | AttShRatio | ASR-Z | Sh Ratio | ShR-Z |
| Almond, Cody (27) | .205 | -.789 | .778 | .551 |
| Bouchard, Pierre-Marc (96) | .265 | .414 | .803 | .885 |
| Brodziak, Kyle (21) | .266 | .430 | .712 | -.338 |
| Bulmer, Brett (19) | .233 | -.229 | .700 | -.500 |
| Christensen, Erik (26) | .205 | -.787 | .727 | -.131 |
| Clutterbuck, Cal (22) | .317 | 1.462 | .686 | -.684 |
| Cullen, Matt (7) | .229 | -.302 | .872 | 1.083 |
| Heatley, Dany (15) | .255 | .221 | .716 | -.280 |
| Johnson, Nick (25) | .281 | .747 | .817 | 1.083 |
| Koivu, Mikko (9) | .218 | -.513 | .748 | .144 |
| Latendresse, Guillaume (48) | .314 | 1.391 | .703 | -.464 |
| Mcintyre, David (34) | .214 | -.594 | .667 | -.951 |
| Mcmillan, Carson (45) | .215 | -0.572 | .571 | -2.238 |
| Ortmeyer, Jed (41) | .383 | 2.774 | .645 | -1.241 |
| Palmer, Jarod (79) | .265 | .426 | .846 | 1.475 |
| Palmieri, Nick (17) | .323 | 1.575 | .747 | .128 |
| Peters, Warren (43) | .273 | .581 | .662 | -1.010 |
| Powe, Darroll (14) | .279 | .700 | .697 | -.538 |
| Rau, Chad (36) | .256 | .248 | .700 | -0.500 |
| Setoguchi, Devin (10) | .284 | .796 | .780 | .578 |
| Taffe, Jeff (97) | .286 | .834 | .750 | .176 |
| Veilleux, Stephane (98) | .228 | -.326 | .696 | -.559 |
| Wellman, Casey (95) | .309 | 1.302 | .706 | -.421 |
| Zucker, Jason (16) | .345 | 2.017 | .800 | .851 |
When charted, the z-scores can be informative because they fall into one of four quadrants on the chart. Attempted Shot Rate is on the X-axis, and Shooting Rate is on the Y-axis. For lack of better terminology, they are I – shooting inclined + accurate shot; II – shooting inclined + inaccurate shots; III – shooting averse + inaccurate shot; IV – shooting averse + accurate shot. (I really dislike using “accurate/inaccurate” here but I just don’t have a better pair of descriptors right now. If you have suggestions please share!)
*Click the chart to view the full-size version.
Discussion:
The first thing that jumps out to me is that the majority of players fall into quadrants I and II…so it’s a team full of puck hogs? Apparently so. Recall that the ASR data here are compared to the five-year population parameters, so the Wild have a lot of guys who choose to shoot the puck. I am trying to avoid value judgements here since a higher value ASR doesn’t necessarily represent “better hockey,” just a different playstyle. On the other hand, Sh Ratio is less subjective–making the goalie make a save is almost always better than missing the net. I know some times a player will shoot wide on purpose to try to make a play, but for now I am using the assumption that SOG > missed shot.
So the Wild had guys who were looking for shots but they had the fewest shots per game and goals per game. What to make of this? I wouldn’t necessarily expect a team-wide pattern to emerge when looking at this kind of plot, but it would seem to make sense that having more of a balance between shooters and set-up guys would produce more quality scoring chances. I would also think that a team distribution like this would indicate that the Wild players were predictable–the opponents probably figured out quickly that guys would be looking to shoot instead of making the extra pass, so perhaps that predictability suppressed their shot totals by making them easier to read. I’m just spitballing here, but if the Wild ranked high in a stat that measured blocked shots against, there might be something to this theory. But honestly at this stage in the research I’m still just digesting the numbers so again, if you have thoughts let’s hear them!
At the individual level, it is important to keep in mind that there isn’t really a “good play vs bad play” distinction here, it’s all just a measurement of behavior. Because we know that both ASR and ShR are normally distributed, it makes the indivudal results more interpretable, but a player who has a high attempted shot ratio isn’t necessarily doing his team any favors if he consistently misses the net more than his teammates. I do like the quadrant setup because it helps classify players. Matt Cullen doesn’t shoot much anymore, but when he does pull the trigger he hits the net more than any of his teammates. Mikko Koivu, known as a playmaker, prefers to make an extra pass rather than shoot, and is about average at hitting the net. Pierre-Marc Bouchard, another playmaker, actually shoots the puck more than I would have guessed, and is an accurate shot. Nick Johnson, despite having a strong tendency for the uncontrolled zone entry, pulls the trigger often and makes the goalie make a save. Dany Heatley is as close to zero on both scales as any player, which is probably a surprise to most of us. Cal Clutterbuck and Guillaume Latendresse have a strong tendency to shoot, but are somewhat wild.
In the end, it is my hope that this kind of multidimensional shooting analysis can be helpful in our understanding of players’ tendencies. This kind of stuff can’t be used in isolation, but rather it all works together to paint a picture and gives us a peek inside the head of the players we enjoy watching. The game of hockey is just a series of decisions, nearly all of which must be made at a speed where there is no time for conscious thought but instead are instinctual. I will be excited to continue this analysis to expand to different teams and different years. If you have questions or comments I would love to hear them. You can comment here on this page or though Twitter, @Hashtag_Hockey. Thanks for reading!
After correctly predicting 49(!) states in the 2008 presidential election and 50(!!) in 2012, Nate Silver is the King of Stat Nerds. He sits on a throne made entirely of TI-83 graphing calculators and he wears a gold-played Casio calculator wristwatch. His fivethirtyeight blog was recently pulling in a staggering 20% of the New York Times web traffic, and while the rest of the pundits were reporting a dead heat in the polls, Silver’s model ended up at more than 90% for Obama leading up to election night.
Silver cut his teeth in baseball sabermetrics before moving to politics, and the backlash he faced in recent months strongly parallels the anti-stats sentiments that are still going strong in the sports world. But now Silver has been completely legitimized, and his recently released book (currently number 17 on Amazon and rising) is going to propel this whole “math” thing into the mainstream conversation, the likes of which we haven’t seen since Freakonomics and Moneyball.
Furthermore, the concept of “big data” will gain steam as the stories are written in the coming weeks about how the Obama administration campaigned more efficiently with less money and mopped the floor with the Romney camp. The zeitgeist is changing, and the legitimacy of data mining, reliance on sophisticated computer models, and quantitative analysis in general is going to become much more accepted in all parts of society. Fancy stats are certainly nothing new in sports, but I genuinely think that with Silver’s domination of the political prediction game, sports stats can hang on for the ride. I wanted to share some of my thoughts on the recent goings on because I really think we are at a crossroads with the way big data and stats are going to be received.
OBJECTIVITY: The Numbers Don’t Care Who Wins
Try to imagine for a minute what would have happened if the election was flipped–if Romney was leading in Silver’s model going into the election and then won. The reception in the media surely would have been different, but I wonder if the classic “I don’t like your numbers, therefore they’re untrue” argument would have been as obvious, or if the ad hominem attacks would have flown so freely.
Silver faced so much backlash because his forecast directly flew in the face of the pundits, and not just the Republicans. The narrative coming from talking heads on both sides of the aisle was that the election was “Razor Tight” (their words, not mine) and right up to November 6th, they were saying it was anybody’s election. Of course, it was in their interest to push that narrative, as they all have airtime to fill and quotas to fill for their magazines or newspapers or blogs. The fact that Silver was making an objective forecast was upsetting because it was so opposite of the status quo. The fivethirtyeight model simply input poll results, weighted them appropriately, and output a prediction of what the electoral votes would be. Silver didn’t set out to create a model that would show Obama was winning…he set out to create a model that would reflect the truth.
Everything in the above paragraph is directly applicable to sports writers and sports researchers. The Old School is mad at the New School because they have made their living on their experience and their “gut calls” and now those things are being invalidated by numerical models. It has been said that the difference between researchers and pundits is that one starts with a question and constructs an argument based around the answers to that question, and one starts with an argument and searches for facts that support the argument. The sports and political worlds both have become so reliant on pundits who until recently have been using just the most basic of stats that this whole “objectivity” thing is still very new, and therefore, scary.
But over time, both political stats and advanced sports stats will gain legitimacy as they persist in the mainstream consciousness. And as long as they are good stats, they will persist (more on this in a bit). And obviously Silver is CRUSHING IT for his part, so there’s no reason to think he’s going anywhere.
TRANSPARENCY: Hey, come over here and look at what’s behind this curtain!
With the Old School way of doing things, the pundits don’t have to be concerned with transparency…they say what they think, they explain it, and boom, there’s your transparency right there. Here’s my opinion, and by definition it’s unfalsifiable so…we’ll just keep going round and round because there is no right or wrong, it’s just all conjecture. With the New School data-driven approach, there is now a need for transparency that didn’t exist before. Without transparency, it’s all too easy to try to discredit stats by calling them biased (see above section on Objectivity.)
Sports researchers (and Nate Silver) walk a fine line when it comes to the transparency of data and formulas (formulae?). They must reveal enough about their methodology so that others can understand what they did, other researchers can help develop the measures, and laypeople can get what the numbers mean. But let’s be really real right now, the way to make money off these kind of things is to withhold enough of the nuts and bolts so that nobody else can figure them out. Silver was incorrectly slammed for tinkering too much with his machine, oversampling Democrats for example, or generally just using his “Magical Formula.” But he does in fact go into great detail about his methodology. I guess what it boils down to is that research should be as transparent as is necessary, but each person will have different levels of comfort when it comes to divulging all the secrets. For Silver’s part, I have been impressed with how open he has been and how much of the nitty gritty he gets into.
It Helps (A Lot) to be Right
It’s hard to imagine Silver’s model being wrong, given that his forecast reached over 90% for Obama. If it was in fact a closer election and it was, say 55/45 then there would have been a much different discussion surrounding his forecast. But over the last two presidential elections, he is batting .990, which is obviously a stellar showing. The nature of Silver’s forecast is such that he puts himself out on quite a limb, predicting each state and the overall winner. Yes, he does in fact give probabilities for each state so if he was wrong he would be able to defend the forecast. But he’s NOT wrong, which is very good for him.
In hockey research I keep coming back to the whole Minnesota Wild thing from the 2011-12 season. Long story short, when the Wild were number one in the NHL, the advanced stats community was vehement about their falling back to earth, based mostly around the PDO statistic. And they were RIGHT, the Wild did regress in a big way. The stats crowd will always have that one in their pocket, and if we ever get a season again, that stat will have a very established place in the stats conversation, not just on the blogs and on Twitter, but I think with the legitimacy gained from last year, it will creep into the mainstream conversation. Just as Silver’s model has been shown to be profoundly correct, the PDO analysis has proven correct and useful.
Final Thoughts
The quote from Moneyball that stuck with me the most (I can’t remember if it’s in the book too) is: “The first guy through the wall always gets bloody.” Nate Silver took a lot of shots in the media this year, but he has come through the other side squeaky clean. His forecast has been amazingly accurate, and I think it’s going to go a long way toward the legitimization of Big Data and statistical analysis not just in politics, but in sports and all other areas of society. I have been very impressed with the way Silver handles his work, and conducts himself in public and in the media. I hope these ramblings have made some kind of sense, I am still forming what I think are the lessons to be learned from his unprecedented success. Let me know what you think about whether and how the fivethirtyeight model’s success will help usher in the Big Data movement and what lessons the sports stats community can learn. Don’t forget to follow me on Twitter @Hashtag_Hockey, and thanks so much for reading!
