HTH pod ep 52 – Lovely groomed ginger beards

Bob and Dan are joined by Carolyn and Merrin of the Deep in the Heart of Hockey podcast. They talk about the Stars surprisingly bad season, the Wild’s surprisingly good season, and why the Blackhawks are actually bad. They delve into the details of why zone exits are so important, why defense doesn’t really help win games, and the design aspects of fancy stats charts. Plus, who’s got the best beard in the NHL?

HTH pod ep 51 – Indiana Jones and the Lost Episode

Bob and Dan discuss the Wild’s current hot streak, including several players who have been pleasant surprises, such as Matt Dumba, Mikael Granlund, and YES! even Zach Parise! Then they discuss the World Junior Tournament gold medal game, and the Wild prospects that shined during the tournament.

The guys need your help identifying and collecting data on Twin Cities outdoor rinks! As described in this episode, here is the link to the spreadsheet: https://docs.google.com/spreadsheets/d/1OOS7mK4gMv406bX4H5-5oQhzb8gTm6qFDXTfiX-SIHg/edit#gid=0

Finally, there is a blogger/podcaster/HTH fan skate happening January 15th probably in Roseville. Check the blog or our twitters for more details. Hope to see you there!

Don’t forget to leave a 5-star review on iTunes if you liked the show!

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.