Advanced Stats Archives

Tag: Advanced Stats

(Photo Credit: SinBin.vegas Photographer Brandon Andreasen)

Variance is a huge part of the game of hockey. It’s common for a team to have a week’s worth of games where they can’t buy a goal and then follow it up with a week where everything goes in.

One such place where variance can be extremely volatile from one game to the next is when examining rebounds. This season the Golden Knights lead the NHL in rebound shots with 59. They’ve allowed the 9th most in the league with 51. However, Vegas has scored just three goals on their 59 rebound shots while allowing six on the 51 they’ve given up. These numbers currently have the Golden Knights in the bottom five in the league for the fewest percentage of goals scored by rebound and the highest percentage of goals allowed.

It could be a good example of something that is likely to regress to the mean as the season goes on.

That being said, these numbers aren’t new for the Golden Knights. In their six completed seasons, Vegas has finished in the top five in rebound shots four times, but have only finished in the top five in rebound goals once. Also, Vegas has been in the bottom five for the highest percentage of goals allowed from rebounds in three of the six years.

Here are all the rebound shot numbers including ranks for that particular season.

Do NHL Players Resent Analytics?

By Jason Pothier

On August 5, 2019

In Team Ops

(Photo Credit: SinBin.vegas Photographer Brandon Andreasen)

Newly signed Canadien Ben Chiarot was forced to defend himself this offseason, as some analysts in Montreal weren’t impressed by the acquisition. Enhanced analytics have exposed some of the limitations to Chiarot’s game. Some expressed their opinions and it didn’t sit well with the former Winnipeg Jet.

“A lot of those analytics guys sometimes I wonder if they played a game of hockey. There’s more to it than what the analytics show with a lot of players. It doesn’t always give you the full picture. That’s something new that’s kind of come up over the last couple of years is the whole analytics. And then you get all of these new hockey experts that come up when they get all of these numbers. That’s just one part of the game and one to evaluate a player.”-Ben Chiarot, Canadiens Defenseman, TSN Montreal

Colleague and friend of the site, Sheng Peng from Fear of the Fin, views the game differently than most. He’s heavily influenced by deep statistics that predict or highlight a player’s ability to create, or limit time and space. Peng never played the game but understands it well, sometimes that isn’t enough. He’s had a few confrontations with coaches and players when asking analytically focused questions. Defensive pushback can make it difficult to get the responses Peng would like.

“The challenge is figuring out which players are open to the subject. There are players who are completely resistant, players that need it framed the right way, and players who are open.”-Sheng Peng, Fear of the Fin

Former three-time Stanley Cup winner Aaron Ward is involved with the next generation of NHL analysis, advanced player tracking. Ward genuinely believes player tracking will accurately evaluate strengths and weaknesses.

“Active players get lost in what the analytics say about them. You’re trying to dissect certain circumstances and understand how they react in that situation. It’s based on tendencies. As the science evolves… you’ll find new ways to break down players that maybe a benefit or a relevance for players in that role.”-Aaron Ward, TSN 690 Montreal

As much as Ward sees future improvement with chip tracking, he understands why players are sensitive to negative statistics of any kind. It’s tough for a professional athlete to recognize their inabilities.

“I think what happens is a player will come out and get an idea of how they played. When the numbers don’t fit it, and they don’t match the way you play, they immediately dismiss it.”-Aaron Ward

Advanced Stats Say Hot Start Unsustainable

By Ken Boehlke

On October 13, 2017

In Game Analysis

Here at SinBin.vegas we spent a good portion of the summer teaching you about stats like PDO, GVAA, Corsi For, and others. Now, it’s time we use those stats to crush your hopes and dreams.

It’s no secret the Golden Knights aren’t going 82-0, despite the fact that I’m planning on tweeting it and #CupIn1 until it’s not possible anymore.

With losses by STL and CHI, there are now two undefeated teams in the NHL. The two most likely candidates… New Jersey and Vegas. #CupIn1
— SinBin.vegas (@SinBinVegas) October 13, 2017

However, the question, is this team a lot better than we thought, is a fair one.

So let’s dive into some stats to try and give us a sneak peak into what might be coming up in the near future for the undefeated Vegas Golden Knights.

Starting with the most glaring, PDO. In short, the idea of PDO is that most players and teams will end up at 100 over the course of a season. Currently, the Golden Knights have ONE player with a PDO under 100. One effing player after three games, that’s madness.

Raise your stick if your PDO is over 104. (Photo Credit: SinBin.vegas Photographer Brandon Andreasen)

Of course, that means the team PDO is out of this world as well. The Golden Knights have a PDO of 104.5. Insane. Here’s the rest of the team…

Advanced Stats For VGK Dummies: 5 On 5 Close

By Jason Pothier

On August 29, 2017

In Team Ops

It’s time for article six of Advanced Stats for VGK Dummies. Today we are taking on a situational stat category called “5 on 5 Close.”

Unlike the last five articles, this one is not actually a stat, instead, it’s a qualifier for other stats. Normal qualifiers for stats are “even strength,” “power play,” or “penalty kill,” and understandably players stats differ based on each situation.

5 on 5 Close is a qualifier to show how guys play when the game is on the line, disregarding stats when a team is well ahead or well behind in any particular game. So what exactly makes a game “close?”

Within 1 goal (1st and 2nd periods) or tied (3rd period or overtime)

Example time! Let’s take a look at James Neal’s 2016-17 season with the Nashville Predators. Neal scored 22 goals, had a CF% of 58.3%, and a PDO of 99.9 last year. But when we drill it down to 5 on 5 Close, Neal’s numbers drop off a bit. 7 goals, CF% of 50.9% and a PDO of 98.6.

This is usually the part where we put Golden Knights stats and who is good at the stat and who is not. But, that makes absolutely no sense for this one, especially since none of these players have ever played together… so no soup for you.

Where this does become interesting will be when the Golden Knights finally start playing. Vegas is probably going to get beat a lot this year, and they’ll probably be out of 5 on 5 Close range quite often. But when they are in close games, we will be tracking time on ice, CF%, PDO, and many other stats which will translate in the future, when the Golden Knights are good.

5 on 5 Close, a stat that matters, a lot, but not for us yet.

Advanced Stats For VGK Dummies: Goals Saved Above Average

By Jason Pothier

On August 10, 2017

In Team Ops

After four advanced stats articles breaking down the prowess of skaters, it’s time to head to the crease and help you further examine goaltenders. The next stat up in our series of Advanced Stats for VGK Dummies is GSAA or Goals Saved Above Average.

For those who are baseball fans, this is essentially WAR (wins above replacement) for goalies. For those who aren’t baseball fans, smart thinking, baseball is boring.

Usually, we first try to explain what the stat is before we get into the formula of how it’s calculated, but this one is a bit convoluted when explained that way that we’ll start by simply saying, GSAA measures individual goalies against the league average goalie. That’s all you need to know, now follow through how it’s calculated.

First, we need to calculate the league average goalie. To do so, we take every save made by every goalie in the NHL and divide it by every single shot on goal over the course of a season.

To simply the numbers let’s use a hypothetical using just one game. Say the first game of the season there are 50 total shots on net, 25 by each team. A total of five goals are scored, so 45 shots are saved. Thus, the league average is 45/50=0.900.

Now, we take an individual goalies stats. Let’s say he gave up one goal on 25 shots, meaning he saved 24.

GSAA = [Shots against x (1 – league-average save percentage)] – goals allowed

So, our goalie faced 25 shots, he allowed 1 goal, and the league average is 0.900.

[25 x (1-0.900)] – 1
[25 x 0.1] – 1
2.5-1
GSAA = 1.5

Advanced Stats For VGK Dummies: PDO

By Jason Pothier

On July 27, 2017

In Personnel

We’ve already taught you about Corsi, Zone Starts, and First Assist, now it’s time for the fourth installment of the ever popular segment. Today we’re taking on a slightly more complicated one… PDO. The NHL refers to this stat as SPSV%.

Tim Barnes ran the now defunct website Irreverent Oilers Fans. While doing so he used the pseudonym Vic Ferrari. Brian King was another writer on the site and used the nickname PDO. Barnes began delving into stats and came up with the terms Corsi and Fenwick, and eventually with the help of King came up a stat they named after King, calling it PDO. Or something like that, the story gets twisted a lot.

Yeah, but who gives a damn about the name, just tell me what the hell it is. Fine, calm the F down. But first, remember that PDO and SPSV% are the same thing. For the remainder of this article, we are calling it PDO, cause it’s cooler and the NHL is lame for changing it.

PDO is the sum of shooting percentage and save percentage while a player is on the ice. Shooting percentage is number of goals scored divided by total number of shots of all players on that players team while on the ice (oiSH%). Save percentage is number of saved shots divided by total number of shots by the opponent while a player is on the ice (oiSV%).

(Similar to Corsi, it doesn’t matter if an individual player actually takes a shot, all that matters is that he’s on the ice when a shot is taken or a save is made.)

Example time! Erik Haula is on the ice for a total of 10 minutes during a game. During those 10 minutes, the Golden Knights have 20 shots and 1 goal. In those same 10 minutes the Kings have 10 shots and also have 1 goal. Therefore, nine saves were made by the Golden Knights while Haula was on the ice.

On Ice Shooting Percentage (oiSH%) – 1/20 = 5%
On Ice Save Percentage (oiSV%) – 9/10 – 90%
PDO – 5% + 90% = 95

PDO is usually measured against the number 100 because a shot is either scored or saved. Think of it like flipping a coin. It’s either heads or tails, so over the course of all flips, the percentage of heads plus tails will be 100%. Under the same mathematic principle, the average of all shots made plus all shots saved will be 100%.

The working theory in hockey is about 8% of shots are made meaning 92% are saved. So, over time, player’s PDO numbers should regress toward 100 (8+92). Of course, that’s ridiculous because some players are better than others and luck is heavily involved in hockey, but a player’s PDO can often be an indicator of the future of a player’s season or career.

Let’s look at some Golden Knights PDO numbers. Forwards first…

Advanced Stats For VGK Dummies: First Assist

By Jason Pothier

On July 21, 2017

In Personnel

Time for episode three of Advanced Stats for Dummies (see Corsi For Percentage and Zone Starts). Today we are diving deeper into the word assist to explain the meaning and importance of the stat “First Assist.”

Quite simply, the First Assist (FirstA) is awarded to the player who last touched the puck before the player who scored. In other sports, like basketball or soccer, this is the only player to record an assist, but in hockey, multiple assists are awarded for every goal. Only one First Assist is awarded per goal.

Example time! The Golden Knights have the puck in their defensive zone, Nate Schmidt zips the puck up the boards to Reilly Smith, Smith takes it and passes it to Vadim Shipachyov, Shipachyov shoots and scores.

Goal (G) – Shipachyov
Assists (A) – Smith, Schmidt
First Assist (FirstA) – Smith

The reason First Assist is measured is because it’s often an indicator of actual impact on the play. Often times in hockey, a player makes a simple pass and ends up getting an assist out of it. Goalies accounted for 35 assists last season, only five of them were First Assists (14.3%). On the flip side, Connor McDavid recorded 70 assists and 44 of them were First Assists (62.9%).

First Assist is a good measure of playmaking impact on the ice, ability to generate offense, and puck focus. Not every time, but in most cases, the final pass before the goal was more important than the pass that led to the pass before the goal. In other words, First Assist is a validation of the total assist number.

Let’s take a look at how the Golden Knights roster fares in the First Assist category.

Advanced Stats For VGK Dummies: Zone Starts

By Jason Pothier

On July 17, 2017

In Personnel

Next up in our series of Advanced Stats for Dummies (last was Corsi For Percentage) we are taking on a stat that helps to understand what a player does best. It’s called “Zone Starts” and it measures the number of faceoffs a player is on the ice for in the offensive or defensive zone.

Zone Starts are calculated using a very simple procedure. Any time a player is on the ice for a faceoff in the offensive or defensive zone he is scored with a Zone Start. (Neutral zone faceoffs are ignored). If the player is on the ice for a faceoff in the offensive zone, it goes for an offensive Zone Start, if his team is in the defensive zone, he gets a defensive zone start. Zone Starts are then measured by percentage.

Offensive Zone Start Percentage (oZS%) = Offensive Zone Starts / Total Zone Starts
Defensive Zone Start Percentage (dZS%) = Defensive Zone Starts / Total Zone Starts

Example time! Let’s use David Perron because he tweeted at us one time and it made us blush. Perron starts the game on the ice, the opening faceoff is taken (no Zone Start scored). The puck is iced and the Golden Knights get a faceoff in the offensive zone (+1 oZS). Shot goes into the netting, another offensive zone faceoff (+1 oZS). Play continues, the puck goes out of play in the Golden Knights zone, Perron stays on the ice for the faceoff in the defensive zone (+1 dZS).

Offensive Zone Starts (oZS) – 2
Defensive Zone Starts (dZS) – 1
Offensive Zone Start Percentage (oZS%) – 2/(2+1) = 2/3 = 66.6%
Defensive Zone Start Percentage (dZS%) – 1/(2+1) = 1/3 = 33.3%

Perron had an oZS% of 43.9% last season and a dZS% of 56.1. The two years he played in Pittsburgh though, his oZS% was 60% and his dZS% was 40%.

As you can see, you really only need to focus on one side or the other as oZS% and dZS% offset. (43.9+56.1=100 / 60+40=100)

Here’s a look at the Golden Knights top oZS% players. First forwards.

Player	Offensive Zone Start Percentage (oZS%)
James Neal	59.6%
Jonathan Marchessault	53.1%
David Perron	53.1%
Oscar Lindberg	52.3%
William Carrier	51.5%

Now defensemen.

Advanced Stats For VGK Dummies: Corsi For Percentage

By Jason Pothier

On July 12, 2017

In Personnel

Now that the Golden Knights have players, and will soon begin playing actual games and accruing statistics, we felt like now was a good time to learn the roster, by dumbing down different advanced statistics in the NHL.

Everyone understands goals, assists, points, penalty minutes, and time on ice, but for many hockey fans words like Corsi, Fenwick, PDO, and zone starts send off the instant “too much info, ignore and move on” signal. Since the Golden Knights are probably going to be bad, and the standard stats are likely to all have asterisks next to them with people saying “well someone has to score the goals,” we think it’s best if we use the downtime of July and August to take the stigma away from advanced stats, and get a feel for who the Golden Knights actual have on the roster.

Up first, Corsi and Corsi For Percentage.

Corsi is a statistic that measures the true number of shots a player takes during a game. It was created by an Edmonton Oilers blogger when he heard a GM mention the term “shot differential” but not have concrete numbers to back it up. So, like any good Internet user, he put in the research, came up with the numbers, and created a stat that’s now widely used in the NHL. So what is it exactly?

Corsi = Shots on Goal + Missed Shots + Blocked Shots Against

It’s fairly simple, it’s just all shot attempts. Corsi can be measured for a team or for an individual. For a team, it’s simply all shot attempts. For an individual, it’s all shot attempts by the team while that player is on the ice. So, for every shot, the team gets +1 on their Corsi rating, and five players get +1 on their individual Corsi rating.

Obviously that stat means nothing if not put in context with what the other team is doing. So, each player has a “Corsi For” and a “Corsi Against” rating. Corsi Against is simply a Corsi rating for the other team while a player is on the ice.

SinBin.vegas

Praise Be To Foley, Vegas Golden Knights Hockey Website

Tag: Advanced Stats

Golden Knights Have Been Unlucky In Finishing Rebound Chances

Do NHL Players Resent Analytics?

Advanced Stats Say Hot Start Unsustainable

Advanced Stats For VGK Dummies: 5 On 5 Close

Advanced Stats For VGK Dummies: Goals Saved Above Average

Advanced Stats For VGK Dummies: PDO

Advanced Stats For VGK Dummies: First Assist

Advanced Stats For VGK Dummies: Zone Starts

Advanced Stats For VGK Dummies: Corsi For Percentage

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