Blackhawks News

Scoring Potential – Chicago Blackhawks’ Performance in TOI

By Melissa Peterson
facebooktwitterreddit

In this article, I discuss the Chicago Blackhawks’ skaters, their use over the season as seen through their time-on-ice (TOI) and how that impacts their scoring potential. 

Table compiled by Melissa Peterson from data courtesy war-on-ice

INTRODUCTION TO DATA CALCULATIONS AND ABBREVIATIONS

(Please see abbreviations and definitions at end of this section for the table above) I started by compiling all the per-game time data available on war-on-ice and multiplying that by the number of games a player had started in I then calculated the players total time on ice they had been eligible for. What this means is that each game a player could technically spend the entire time on the ice in a single game, and was meant to look at what percentage of a game a player was played for. How this differs from the TTOI second column is that the second column represents the Blackhawks’ total time on ice for the their 94 games played.

More from Blackhawks News

Unlike my previous post in which I used a 60.9 minutes per game season average to estimate time for the 82 games to determine eligibility, I used exact figures to calculate the TTOIE in the above table. This basically meant scraping individual game total times, cross referencing them with when players were in lineup, and adding them together.

This differentiation between the percentage of TTOI versus the percentage of TTOIE helps us to look at players who may have been received in a trade (ex: Kimmo Timonen, Antoine Vermette, Andrew Desjardins), players who may have been traded mid-season (ex: Tim Erixon, Jeremy Morin, Ben Smith, Adam Clendening, Klas Dahlbeck), players who may have two-way contracts with Rockford (ex: Teuvo Teravainen and Joakim Nordstrom are prime examples here) and players who may have been out for some time with some injuries (ex: Trevor Van Riemsdyk, Kris Versteeg, Patrick Kane)  and how they are being used.

It also gives us insight as to what level of productivity could potentially be achieved from each player by taking their percentage of TOI into consideration. How? The comparison between an individual player’s percentage of that total time Chicago spent on ice and the percentage of the total amount of goals attributed to them helps us to gain insight into productivity by allowing us to see based on TOI how much productivity a player could, at maximum, be projected to contribute.

So, to review:

  • TTOI refers to the total amount of time a player spent on the ice.
  • TTOIE refers to the total amount of game time a player was not scratched and in line-up, therefore eligible to play.
  • %IE refers to the percentage of eligible time (TTOIE) a player played for and is calculated by taking TTOI and dividing it by TTOIE.
  • CTTOI refers to the total amount of time Chicago has had this season.
  • %CTTOI refers to the percentage of time a player appeared on the ice as a whole and is calculated by taking TTOI and dividing it by CTTOI.
  • GF refers to the player’s goals for
  • %GF refers to the percentage of Chicago’s total goals a player is responsible for out of the 256 CHI has scored from 01 October 2014 up until 20 May 2015.
  • PoGF refers to the potential goals a player could score based on their percentage of total ice time and is calculated by taking their %CTTOI and multiplying it by CHI’s total GF for the season (256). It takes no account for position or shooting percentage or Corsi; it is merely a flat calculation based on the total percentage of TOI a player was put on the ice for. 
  • %PoGF refers to the percentage their actual GF is of their potential goals for (PoGF)

SO WHAT’S IT ALL MEAN?

Well, obviously an entire line of skaters are out on the ice at a time (minimum of 3 skaters, maximum of 6) so these times on the ice would be overlapping with other skaters and make it impossible for them to achieve full potential productivity as measured by goals since only one skater is capable of being credited a goal. However, a skater could be expected to perform between 16.67% and 33.33% of their goal potential (16.67% assumes 6 skaters on the ice; 33.33% assumes 3 at a time), with the average contribution at 20.0% (5 players), assuming equal distribution between all skaters. However, a distinction between forwards and defensemen should probably be made (and was in the following calculations below). But how much of an impact should position have?

According to QuantHockey, defensemen account for approximately 15-20% of a team’s total goals (x). Assuming this to be the case for Chicago, that means of the 256 goals, CHI defensemen (as a whole) could be expected to contribute between 39 and 52 of them, rounding up (They actually scored below that at 36 goals between them).

The other cut of that is obviously that forwards are responsible for less than the total amount of goals for a team (between 80 and 85% typically; in CHI’s case, 85.9%). If we follow suit with the max amount forwards are responsible for, we set the amount at 217 goals. These figures then needed to be adjusted to account for defensive pairings and line combination forwards.

Table compiled by Melissa Peterson sourced from war-on-ice

From here we can see that based on TOI, Brent Seabrook is actually the most productive (as defined by goals) defensemen, followed by Duncan Keith (Clendening and Dahlbeck are skewed heavily by low time samples). The top performing forwards are, unsurprisingly, Patrick Kane and Jonathan Toews, followed by Brandon Saad, Patrick Sharp and Marian Hossa.

When you take into consideration that the average forward is performing at 69.5% of their potential goals and the average defensemen at 36.5%, their accomplishments seem even more impressive (You know, on the off chance you weren’t already impressed by Kane’s nearly doubling expectations). Obviously some take-aways are that not everyone is on the ice functioning as a scorer, but it’s interesting to look at just who is filling the role and how.


Resources

  • Corsi: For those that are unsure of what Corsi even is, it is measured as Corsi For and Corsi Against (CA). Corsi is the total number of on-ice shot attempts (on goal, missed, or blocked) taken during a game/series/season. Corsi For is the amount of the total Corsi taken by one team or player on said team. Corsi Against is the amount of the total Corsi taken against one team or player on said team.
  • CA% : Corsi Against Percentage (of total) What this means is they’ve totaled up the Corsi Events that took place for both teams, and divided the individual team’s total by that number and multiplied it by 100 to get a percentage.
  • CP60 : Corsi Per 60. What this means is they’ve totaled up the Corsi events that took place for both teams and divided it by 60 to get an average Corsi Events per 60 minutes.
  • G+/- : Goal Differential. The total number of Goals For (GF) minus the total number of Goals Against (GA). If it is a positive number, the team is outscoring their opponents.
  • FO%: The percentage of Face-offs won.
  • OFOn%: On-Ice Unblocked Shot Attempts on Goal
  • OSh%: On-Ice Shooting percentage
  • OSv%: On-Ice Save percentage
  • PDO: On-Ice Save percentage + On-Ice Shooting percentage
  • ZSO%: The amount of Offensive Zone starts. The larger the number, the more often a team starts (with a Face-off) in their Offensive Zone

Sources

Stats and charts courtesy of war-on-ice.com

Quanthockey.com

More from Blackhawk Up

facebooktwitterreddit