Snap Shots: Shot Attempts vs Scoring Chances in the Fancy Stat era

Hey y’all! I wanted to put up one more quick post before taking the rest of the month off for the holidays. One very clear trend that the Minnesota Wild have been exhibiting this year–particularly in the last few weeks–is a real propensity to generate scoring chances while limiting the opponent’s chances. Take a look at the shot maps from Saturday’s game against the Coyotes (who are among the worst in the league, but Minnesota has done this against a lot of teams): Continue reading Snap Shots: Shot Attempts vs Scoring Chances in the Fancy Stat era

HTH pod ep 50 pt 1 – Disciplined Aggression, Spurgeon da god


In part-1 of this week’s mega episode, Bob and Dan discuss the crippling cold weather in Minnesota, then jump into the hockey birthdays and discuss the Disciplined Aggression Proxy stat that Bob worked on all week. What is it, what does it try to measure, and which NHL and Wild players show the best disciplined aggression? (Hint: Spurgeon da god)

#MNWild Disciplined Aggression – The Kids are Alright

I’ve spent the week looking at a stat called the Disciplined Aggression Proxy (DAP), which attempts to show which players play a physical game without taking many minor penalties like slashing or interference, thereby putting the team at a disadvantage for two minutes. If you missed the first two posts for some reason, check out Part 1 and Part 2. Go ahead, I’ll wait. These numbers won’t mean anything if you don’t know the methodology. Continue reading #MNWild Disciplined Aggression – The Kids are Alright

Hand-Crafted Fancy Stats: Disciplined Aggression–Part 2

Earlier in the week, I wrote about a statistic called Disciplined Aggression Proxy, which gives us a better idea of which players play a very physical game, but do not take a lot of penalties in the process. If you didn’t read part 1, check it out now because there will be no other preamble today, I’m just going to jump into the results.

On Monday, I looked at just 2016-17, which I admitted was too small of a sample size. For today’s analysis, I used stats from the beginning of the 15-16 season, and used a cut-off of 50 games played. I suppose 50 games is still pretty small, ideally we’d want to look at 80-100 games or more. But, as we will see, a lot of players in the sample have about that many games anyway. In the tables, I will include GP so we have an idea of how long a player has been able to keep up a high DAP. I am sort of thinking of it like ERA in baseball–a closer who has a 2.0 ERA is great, but a starting pitcher who has the same ERA over a couple hundred innings pitched is a bit more impressive. Continue reading Hand-Crafted Fancy Stats: Disciplined Aggression–Part 2

Hand-Crafted Fancy Stats: Disciplined Aggression

I was talking to a lady on Twitter recently about the concept of ‘data science’ and what it means. Basically, she was frustrated that the term is so vague, “How do you *science* data anyway?” she said. Her comments got me thinking about data science in the world of fancy stats. We almost take for granted nowadays the availability of the possession-based metrics like Corsi, Fenwick, zone deployment, etc. But, what people may not think about is the reason those advanced stats are available is that coders (data scientists) figured out how to take the NHL’s official play-by-play data and parse out who was on the ice for each shot attempt, which players were deployed for face-offs, and so on. The reason we have access to those stats each night is that we have automated the process–we got the computers to scrape the data and perform the calculations to give us the stats we are all familiar with. It would be literally impossible to take the data and work it through manually each night, there just aren’t enough hours. So, our understanding of hockey is very much shaped by the processes we are able to automate. This is one of the many reasons that I am so excited about RFID tracking, or the SportVU (a.k.a. “missile defense”) cameras that have been utilized in the NBA. The frustrating thing about it is that many arenas share NBA and NHL, so the opportunity is there for us hockey fans, but we are not able to get that awesome data.

People who have been reading me for a while might know that every so often, I like to dust off stats that people have developed that cannot be automated, and thus do not get looked at very much, but I still think provide useful information. Back in the day, researchers had to take a long time to collect and clean the data they wanted before writing it up. Now, we just go click, click, click, and we can see a player’s scoring chance proportions for the last eight years.

Anyway, this is all just a long introduction to my weekend project where I looked at an older stat called the Disciplined Aggression Proxy (DAP). This was created by one of the godfathers of the fancy stats movement, Ian Fyffe, and basically looks at the physical aspects of the game (hits and takeaways) compared to the number of minor penalties a player takes. So, which players are being aggressive but disciplined, are getting hits and takeaways, and separating players from pucks without getting called for slashing, interference, etc.

Another #fancystats pioneer, Neil Greenberg, wrote about DAP a couple of years ago. It’s actually a rather simple formula: (Hits + Takeaways) / # of minor penalties. I like stats like this because they are easy for people to wrap their heads around, it’s basically just a fraction. The more hits and takeaways a player has, the higher his DAP will be, and the more minors he takes, that will start to cut down the number. So, I like to bring back these stats once in a while to show that there is a lot of information that we could be gaining, but since it has to be gathered by hand, it’s not in the ‘mainstream’ of advanced stats if you will.

Just to give you an idea of how I went about putting together my dataset, I downloaded all NHL players’ individual data from corsica.hockey for the 15-16 and 16-17 seasons (this was before Saturday’s games), but that site doesn’t split out minor penalties from major penalties. So, I had to search around for that data (shout out to @stateofstats, who is definitely worth a follow)…and wouldn’t you know it, NHL.com actually had it. Who would have guessed!? So, I scraped the data from that site (NHL doesn’t have a download feature, so I copied and pasted from 18 pages of stat tables into excel) before merging the data sets. Oh, and I had to do some data cleaning, too…for example, corsica tends to shorten players’ names in its database, but NHL does not…so we have ‘Alex Ovechkin’ vs ‘Alexander Ovechkin’ and my VLOOKUP function did not work until I reconciled the different names. Finally, I ran a the simple =(hits+takeaways)/minors function to get the DAP. Anyway, this is not to get pats on the back for the work I did, but to pull back the curtain and show how inefficient it can be to pull data that ends up going into a very simple equation.

So, I decided to break the rules and start by showing the 16-17 season only, which I freely admit is too small of a sample size to rely too heavily on, but it’s a good starting point. Here are your top ten skaters for DAP this year:

Name Hits Takeaways Minors DAP
Nic Dowd (LAK) 63 3 1 66.00
Scott Wilson (PIT) 53 5 1 58.00
Micheal Haley (SJS) 52 6 1 58.00
Matt Read (PHI) 44 10 1 54.00
Ryan Hartman (CHI) 41 7 1 48.00
Elias Lindholm (CAR) 29 17 1 46.00
Bryan Rust (PIT) 40 5 1 45.00
Brandon Tanev (WPG) 73 15 2 44.00
Pierre-Edouard Bellemare (PHI) 34 9 1 43.00
Aleksander Barkov (FLA) 20 21 1 41.00

Obviously, at this point in the season, this list is influenced heavily by penalties. If Nic Dowd takes another minor, his DAP is cut in half, to 33.00–still impressive but not in the top-10. Also, as we’ve seen from Greenberg’s and others’ work, these numbers are quite inflated, as a DAP of around 20-25 over the course of a season is considered quite good.

Also, an obvious problem is that there are 100 players who have not taken a penalty this year. The formula does not work with them, because no matter how many hits and takeaways they have, we’re dividing by zero, and we don’t get a DAP at all. I thought it would be informative to show some of those players, because they should get credit for being aggressive and disciplined as well:

Name Hits Takeaways Minors H + T
Micheal Ferland (CGY) 62 15 0 77
Kevin Klein (NYR) 48 9 0 57
Tanner Pearson(LAK) 43 9 0 52
Colton Sissons (NSH) 38 5 0 43
Joseph Cramarossa (ANA) 38 5 0 40
Adam Cracknell (DAL) 33 3 0 36
John Carlson (WSH) 24 11 0 35
Anton Stralman (TBL) 26 7 0 33
Frans Nielsen (DET) 24 9 0 33
Nathan MacKinnon 17 14 0 31

I think that while there are obvious flaws in this stat, we should give credit where credit is due–Nic Dowd is playing well as a grinder on the Kings’ fourth line. He’s throwing hits without taking penalties (and oh by the way he’s chipped in 2G and 9A for 11 pts) so he deserves kudos for that. Also, he’s a St. Cloud State Husky so you gotta love that. Also, shout out to Brandon Tanev for accumulating 73 hits and 15 takeaways while only receiving two minor penalties. But it’s obvious that we need to look at a larger sample size before we can really draw conclusions about who the best disciplined aggressive players are.

Since I haven’t mentioned any Minnesota players, here are the top five Wild skaters who are doing well in this metric this year.

Name Hits Takeaways Minors DAP
Mikael Granlund 18 10 1 28.00
Nino Niederreiter 42 10 2 26.00
Jason Pominville 18 8 1 26.00
Tyler Graovac 20 6 1 26.00
Charlie Coyle 30 13 4 10.75

Defense is always harder to measure than offense, and while this stat isn’t a comprehensive defensive metric, it still provides an interesting glimpse into the contributions of certain players that don’t always get the spotlight. I have data going back to the start of 2015-16 that I will post on Wednesday, which will allow us to draw more conclusions. In the mean time, I’d love to hear your thoughts about the overall concept of the ‘data science’ side of fancy stats, as well as the DAP stat shown here. Leave a comment on this post or hit me up on Twitter @BobaFenwick. Thanks for reading!

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HTH pod ep. 49 – If the women don’t find you handsome…

Bob and Dan discuss some cool spots in Northeast Minneapolis, including Fair State Brewing Co-op. Then, the hockey birthdays and a special tribute to a television legend. This week’s special guest is Ben Remington from Wild Xtra and the Giles and the Goalies Podcast. The guys discuss fighting in hockey and players who need to step up for the Minnesota Wild this season.

HTH pod ep. 48 – Life After Death Star

Bob and Dan are back after a month-long hiatus! As the 2016 NHL season passes the one-quarter mark, the guys run down everything they can about the Minnesota Wild. Who has impressed, who has depressed, who needs to step up, who have been surprising, and what does the team need to do to be competitive in the Central division.

Predicting the NHL is a fool’s errand…or is it?

As I’ve said a lot recently, there are a lot of stats that don’t tell us anything in the early part of the year–basically any individual player’s on-ice stats, even most his counting stats–you can look at them and try to squeeze some meaning out of them, but at the end of the day, the sample size is too small and to predict anything from them would be foolish. Also, we talked on a recent podcast about how PDO is only a backward-facing stat and it has zero predictive power. More on this in a second.

One stat that I do watch closely and do put stock in from the start of the season is goal differential. The shot-based side of analytics has gotten a major boom, as people have wrapped their heads around using the larger sample of all shot attempts to measure possession than just shots on goal. However, I feel like a lot of times, the goal-based metrics are ignored or sort of taken for granted. One thing that I think most people don’t know is that despite all the time and energy we put into shot-based metrics, goals are a better predictor of future success than shots. (See: here, and here, and here.) Continue reading Predicting the NHL is a fool’s errand…or is it?

Snap Shots: Joel Erikkson Ek’s cup of coffee at the NHL level

I thought I’d try this new thing called “writing more.” So here we go! I have a lot of thoughts about the election, and normally I try to keep my political stuff to my twitter. I am still deciding on whether I want to write or talk on the podcast…but I will be posting something soon. I have tended to try to keep the hockey blog and pod as a space of escapism, where political stuff and real-world stuff are purposely kept out. But, that can no longer be the case.

Back to hockey for now. The Wild are at a tipping point early in the season for what to do with young Joel Erikkson Ek. I think most of us assumed he would get a few games at the pro level and then head back to Sweden, where he could arguably develop as much if nor more than the AHL. But EE surprised us all by scoring on his first shot. He had a great first couple of games and has been pretty much invisible over the last few games. The Wild have to decide whether to burn a year off his Entry Level Contract. Meaning, they have to weigh whether his contributions this year against his contributions in 2018-19, essentially. With nine games, we certainly have a small sample to analyze–but the problem is how much weight we put into that sample. My hunch is that they will burn his ELC year…they showed last year with Dumba that they are willing to take a chance, and if it pays off, it could really help them out in a time where their window may not be open as much as it is now.

So I won’t draw too many conclusions from the stats I’ll show, but I wanted to at least take a snap shot (get it!?) of what he’s done in his initial nine games.

  1. He has had insane puck luck. Erikkson Ek’s personal PDO is sitting at 108.97 right now, so let’s just call it 109. As we discussed on a recent podcast, personal PDO is really not a stat you want to use to evaluate a player. But, when we break it apart, we see that his on-ice Sh% is a hefty 13.9%, while his on-ice Sv% is .951. So, he’s been very fortunate with the puck bounces. This confounds an already small sample size, because with such extreme numbers, we basically have even less reason to trust the data.
  2. His deployment has been a bit more even than I would have thought. EE’s zone deployment (OZ-NZ-DZ) so far have been  36.9%, 29.8%, 33.3%. He’s got a reputation so far as a 200-foot player, so it seems Boudreau has mixed up his deployment a fair bit. The Wild have a different team composition than in recent years, so I’ll be watching all year to see who garners the DZ starts.
  3. He has not driven possession at all. Despite slightly favorable zone starts, his CF% is at an even 40% right now, which is really bad. Yes, a player’s individual corsi is not very telling, but I only include it here to show that it’s not been a strength of his. I thought about doing some WOWY analysis, but that’s bad because you take an impossibly small sample and break it down into even impossibly smaller samples. So *shrug.*
  4. He hasn’t had success in the face-off circle. 20 wins and 34 losses for a 37% win pct. You wouldn’t necessarily expect a 19-year old to excel in this area in his first NHL action, but again, I include it here for posterity. As a fourth-line center this year and a potential mantle of “no. 1 center of the future,” face-offs will be a key measurement…although there is research out there that shows that face-offs aren’t as important as people think, I tend to feel that they are “never not good,” so winning face-offs could be a pathway to icetime for EE.
  5. He has been more physical than you might expect. Alright, I’m totally grasping at straws here. I was set to include a stat about how he’s an extreme pass-first player, but couldn’t find the data that I really needed to back that up. Plus, I didn’t want to have a list of all negative things. Erikkson Ek is tied for sixth on the team with 14 hits at even strength. He has 10 hits against, so he’s doling out more punishment than he receives. As a European player, he will undoubtedly have a reputation as a finesse player, so if he can play a bit rougher than people expect, that might lead to some increased opportunity for him. Hits are generally a terrible stat because each arena counts them differently. So, take all this with many grains of salt, but it’s at least something to watch for.

Overall, from what we’ve seen in this early season, it might make more sense for the club to send EE back to Sweden, even though it could hurt this year. He has shown great positioning and some really good instincts, but clearly he needs more seasoning. Whether that’s in the AHL or in Sweden is beyond my pay grade, but overall my gut tells me that based on the flashes we’ve seen, it may pay better dividends to have an extra year of control over a player that could be a main contributor in the next couple of years.

What do you think? Should the Wild keep Easy-E on the NHL roster, or send him overseas for more training? Let me know in the comments or on Twitter, @BobaFenwick. Thanks for reading!