Tag Archives: fancy stats

What’s up with Marco Scandella pt 2 – Defensive Pairings

Earlier in the week, I looked at Marco Scandella’s underlying stats. If you haven’t read it, I encourage you to go check it out! It lays down a foundation of where Scandella is, and leads into the discussion today of who he’s playing with.

Today, I wanted to dive deeper into some analysis of Scandella when paired with other players on the Wild blue line. I haven’t done a lot of this type of analysis, and I’m also using Corsica’s ‘Pairing’ feature to begin, so bear with me if I’m sort of feeling my way through this post. First off, let’s look at pairings over Scandella’s whole career. Here’s who he has been paired with most frequently:

  • Jared Spurgeon: 1,687′
  • Matt Dumba: 886′
  • Nate Prosser: 865′
  • Jonas Brodin: 811′
  • Christian Folin: 395′
  • Ryan Suter: 212′

Nick Schultz, Brent Burns (yep!), Jonathan Blum, Justin Falk, Tom Gilbert, and Cam Barker all skated with Scandella for 125 minutes or less, and are excluded from this analysis.

The above chart shows expected goals for and against for each combo, along with Corsi%. The first thing to notice (which I mentioned in the earlier post,) is that Scandella is not over 50% corsi with any D partner–he’s closest with Spurgeon, 49.6%, and around 45-46% with most others. I’d bet that the Spurgeon numbers are heavily driven by Jared, who is simply a beast…and the rest of them are just…yuck. In terms of xGF and xGA, Scandella/Suter have a wide disparity, but recall that there’s just about 120 minutes over the last seven years there. Scandella/Spurgeon have a very slight edge in goals scored (0.08 goals per sixty, so basically no difference,) while Scandella/Brodin have a noticeable edge in preventing goals. Scandella/Dumba have a fairly pronounced tendency to allow more goals, which is troubling considering how many minutes they play together, and also they are the third pair under Coach Boudreau. Taken together, this data suggests that there isn’t really a pairing where Scandella has thrived–his numbers are the best with Spurgeon, but again…I think that’s driven by #46 moreso than #6.

Now, let’s examine the Scandella/Dumba pair, which is the most common third pair this year. Here’s the same chart as above, but only for these two, and shows their production over time. Real quick, these two skated 136′ in 14-15, 420′ in 15-16 (nice), and 331′ so far in 16-17.

These two had a great year in 14-15, but I really think it’s a factor of sample size, with just 130 minutes and change. For the last two years, their CF% has been around 45%, which is just no bueno. There was a pretty wide gap in their expected goals in 15-16, which has shrunk a bit in 16-17, but the trend is still not great.

Finally, a quick look at Scandella’s WOWY numbers. This type of analysis has a lot of data points, so it’s tough to put on a chart, so this table will have to suffice–click through to enlarge the image.

Looking specifically at Scandella/Dumba, they do not appear to have significantly different corsi production when apart from each other, about 46-47%. One thing we do see is that their GF/GA numbers are stark, but that’s a factor of the team showing a great goal differential this year. It can be confusing to show expected goals and actual goals in the same post, so I hope that’s not misleading, but the data comes from different sites. It’s interesting to note that Scandella/Brodin have about a 55% corsi percentage together, and separately they both have sub-50%, but again, that’s probably a product of sample size, just 85 minutes together this year.

Ultimately, there’s not a lot of optimism coming from these underlying numbers. At 27, this is probably who Scandella is as a player, and at $4 million AAV, he’s likely to be exposed to the expansion draft, but there’s no guarantee that Vegas will take him. So, we’re left hoping he gets his groove back or continues to develop. With Suter, Spurgeon, and Dumba looking like core components of the Wild blue line, Scandella may end up as an expensive role player on the third pair. Which is too bad, because I still like him…and I sure hope he breaks out of this funk he’s been in over the last two years.

What’s up with Marco Scandella?

I am finally getting around to doing some analysis on one of the Wild players that I’ve liked for a while, but who is struggling this year–Marco Scandella. He’s just 27 years old, despite playing in his seventh NHL season…so we pretty much know what to expect from him, but they also say defensemen take a little longer to develop, so perhaps he could still show us something new. He’s got a booming slap shot, but is not known much as a goal scorer–he had a fantastic 14-15 season where he contributed 11 goals, but with a career line of (26+60=86; 0.24 pts/gm), we can expect that the offensive side of his game has developed about as much as it’s going to. Scandella still seems like a solid two-way defender who can contribute in all three zones, and could be a fine second- or third-pair defenseman. I’m going to highlight Scandella’s individual stats over his career today, and then look at some pairing stats and WOWY information later in the week. Let’s get down to it! Continue reading What’s up with Marco Scandella?

Martin Hanzal: Price is what you pay, Value is what you get

“Price is what you pay, value is what you get.” –Warren Buffet

Last night, news broke that the Minnesota Wild had traded for Martin Hanzal from the Arizona Coyotes in exchange for a boat load of draft picks. The full trade is as follows:

ARI sends Martin Hanzal, Ryan White, and a 2017 fourth-round pick. MIN sends Grayson Downing, a 2017 first-round pick, 2018 second-round pick, and a conditional 2019 fourth-round pick, which if my research is correct, could become a second-round pick if the Wild win two playoff series this year, or could disappear if Hanzal plays <50% of playoff games (so think of it as injury insurance.) Continue reading Martin Hanzal: Price is what you pay, Value is what you get

Damned Lies & Statistics: Mikael Granlund is not getting enough credit

Today, I want to talk about one of my favorite subjects: Mikael Granlund. He’s having an outstanding year, and yet I think he’s not getting enough credit for just how good he’s been playing. If you’ve been paying attention, you’ll know that we recently added game scores to the blog. Granlund leads all Wild skaters with an average GS of 0.83, which tells you that he is going out and contributing each night. Let’s dig deeper into his season, shall we? Continue reading Damned Lies & Statistics: Mikael Granlund is not getting enough credit

New Feature on the HTH blog: Wild Game Scores!

A new development in the NHL stats community this year is a stat called Game Score. They have existed in baseball and basketball in previous years, and now we have them for hockey. The idea is that a bunch of factors get put together with different weights to end up with a single number that assesses how good or bad of a game a player had.

Additionally, game scores tend to be adjusted so that they more or less equate with points, to make them interpretable. Meaning, game scores for hockey usually tend to be from 0-3, so you can easily get a sense of just how good or bad a guy’s night was.

Here is the original game score post, so you can read all about the stat. The author does a much better job than me of explaining how it was created and why, Measuring Single Game Productivity: An Introduction to Game Score

I was recently contacted by a guy named Derek who has been pulling the Wild game scores from Corsica, and he offered to let me put them on the HTH blog, which I thought was a great idea. Each week, I will be creating a single post that includes the game score breakdowns for each game during that week, and then updating it as the games happen. I will put up this week’s scores later this morning.

Also, Derek was a guest on the HTH podcast this week, so look forward to hearing him discuss the stat with me! The pod will be posted late tonight (Friday) or else Saturday morning.

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

#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!







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?