Time On-Ice

The Pressures of Parity

Benjamin Wendorf Exploring Context, NHL League-Wide Analysis, Theory Tags: , , , , , , , , 2 Comments

File:Balanced scale of Justice (blue).svg

Two nights ago, when no one was looking, I tweeted out a telling statistic to understand how teams have reacted to the salary cap post-lockout.

Boulerice wasn’t the only one scraping the bottom of the barrel in 2005-06; Colton Orr was nearby with his 2:49 per game, and you didn’t have to look much further to see Andrew Peters (3:15) and Eric Godard (3:27). In fact, 19 skaters played over 20 games that season and recorded even-strength TOI/G lower than Peluso’s from this year. Teams have realized that, in a salary-capped league, even league-minimum dollars can’t justify players who cannot be trusted with regular minutes.

This was a fairly stark evolution of player usage, but it led me to wonder if there were any other things we could see by looking at finer-grained data from 2005-06 to the present. The salary cap was a game-changer because it pushed teams at the top and bottom closer together, and that compelled teams to stop employing players they couldn’t trust at evens; what are some other areas we see the pressure of parity?

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NHL Player Physical Peak Estimation

Benjamin Wendorf NHL League-Wide Analysis, Resources Tags: , , , , , 5 Comments
File:Hockey. Canadiens. Mosdell . Getliffe . Filion BAnQ P48S1P12151.jpg

Probably want to keep that middle guy out for the next shift. Photo by Conrad Poirier, via Wikimedia Commons

Determining NHL player peaks has frequently focused on production and, occasionally, wrinkles are added to account for the steeper fall-off for goal-scoring as opposed to playmaking. Generally, the peak appears to be around the ages 23-25, with some skills like shooting exhibiting fairly early peaks and others a bit later.

Poking around some spreadsheets, I came across data that I’ve always meant to get to: time per shift. The NHL has been keeping a measure of average time per shift for players going back to 1997-98, so I licked my chops over the robust data set. The “Why?” for looking at it, I think, takes us to an interesting place. To some degree, time per shift can allude to a player’s stamina and overall physical fitness; it can also allude to the coaching staff’s assessment of their performance — though there are plenty of shifts ended on the fly in a hockey game. What’s more, we simply haven’t had a lot of player peak estimations using time on-ice, and when done carefully, I think we can capture something like a total physical peak for players.

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More on “Corsi & Context”, with some added predictive modelling

Garret Hohl NHL League-Wide Analysis Tags: , , , , , 2 Comments

Corsi

INTRODUCTION

I have always been of the opinion that Corsi is part of the larger puzzle in trying to gain greater understanding of the game and how a player can affect their team’s chance to win.  Like all statistics though, it needs appropriate sample size and context, and will never tell you everything. Teammates, opponents, luck, system, strategy and what moments a coach deploys a player will always effect results… although, there can also be times where context is overly stressed. While Corsi does tend to need less context than many other hockey statistics, there are some things that need to be kept in mind in how two players with the same Corsi% are not always created equally.

Tyler Dellow wrote a piece on context that is definitely worth a read. In the article Dellow used two tables showing how Corsi changes dependent on ice time for the 2011-12 season.

We will revisit this article using a larger sample and look at both forwards and defensemen.
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Friday Quick Graph: The Evolution of an NHL Forward’s Time On-Ice

Benjamin Wendorf Friday Quick Graphs, Total Player Chart Tags: , , , , , , , , 1 Comment

Friday Quick Graphs are (initially) intended to revisit some of the better, potentially more-significant work I’ve posted over the past year on my Tumblr page (if you want to beat me to some of them, take a look at benwendorf.tumblr.com).

I did a similar GIF one week ago, using defensemen, in an effort to understand how a player’s playing time evolves over their career. Taking NHL player data from 2007-08 through 2011-12 and identifying year-t0-year change, I’m able to create a hypothetical forward that plays from age 18 to age 40, and how that player’s ice time would change.

For frame of reference, the hypothetical player is the dark blue triangle, the light, dotted triangle is the league average across the player population, and the light blue triangle is the league high in each situation.

There are some similarities to the defensemen GIF, primarily that player’s are given powerplay minutes early, but grow into penalty kill minutes. Unlike defensemen, though, forward TOI decreases uniformly at all strengths, whereas defensemen tend to retain some of their penalty kill time.

As with the previous post, it’s worth pointing out that a player playing from age 18 to age 40 would be a pretty unique, talented player, so this model is really just to demonstrate change.

Friday Quick Graph: The Evolution of an NHL Defenseman’s Time On-Ice

Benjamin Wendorf Friday Quick Graphs, Total Player Chart Tags: , , , , , , , , 1 Comment

Age progression TPCs for a hypothetical defenseman who has played from age 18 through 40. The progression is built on year-to-year age trends across the entire NHL defenseman population from 2007-08 through 2011-12.

Friday Quick Graphs are (initially) intended to revisit some of the better, potentially more-significant work I’ve posted over the past year on my Tumblr page (if you want to beat me to some of them, take a look at benwendorf.tumblr.com).

What you see above is a “Total Player Chart,” or TPC, a chart I developed about a year ago to visualize a player’s time on-ice (TOI) deployment. Using that chart, I took the NHL player population from 2007-08 through 2011-12 and recorded the year-to-year change in player’s TOI relative to their age and age +1 seasons. I took those trends and placed them upon an average 18-year old defenseman’s ice time, and tracked how that hypothetical player’s TOI would evolve if they played to the age of 40. The result is the GIF above.

For frame of reference, the hypothetical player is the dark blue triangle, the light, dotted triangle is the league average across the player population, and the light blue triangle is the league high in each situation.

As you can see, the trend is that young player’s tend to receive 5v4 minutes, and as they age they become more trusted with 4v5; as they get older, the 4v5 minutes stick around, but the 5v4 minutes fade.

It’s worth pointing out that this hypothetical defenseman, overall, is likely to be a decent player, by virtue of the fact that they would be getting NHL minutes at age 18 in the first place (and playing until 40).