Stats Made Simple Part 2: Score Close & Score Effects

In The Second Installment Of Our Series On Gaining A Basic Understanding Of Advanced Stats, We Explore Score Close, Score Effects And Relative Percentages.

In our first installment about Advanced Stats, we explored Corsi, Fenwick and how these are used to analyze hockey. In this installment, we will go deeper into the uses of these stats as well as Score Close and Score Effects.

As all hockey fans know, the game is normally played with 5 skaters and 1 goalie for each team. For the purpose of using Corsi and Fenwick, this can be referred to as 5 on 5, 5v5 or some other similar variation. When a team takes a penalty and thus goes on the Penalty Kill (PK) you may see 4 on 5, 4v5 or Shorthanded and occasionally 3 on 5, 3v5, etc. When a team’s opponent takes a penalty, then the team who drew the penalty is awarded a Power Play (PP), which may be called 5 on 4, 5v4, Man Advantage, etc. Even strength is any situation when both teams have the same number of players on the ice.

For the purposes of advanced stats, the best of the above situations to use is 5 on 5. PK, PP and 4 on 4 are not considered to be truly representative of a team’s strengths or weaknesses over time. The point of these metrics is to judge a team’s (or player’s) performance over time and get an idea of what to expect in the future. This cannot happen without being able to filter out the noise and focus on the game situations that give the clearest results. In order to filter out Score Effects (explained below), statisticians focus on 5 on 5 performance when the score is close. Score Close is defined as a score that is tied (including 0-0) or within 1 goal in the first or second period. In the third period, the score is only considered close when the game is tied.

For those of you who may be familiar with science or research, consider 5v5 Close (5 on 5 Score Close) to be the "control" in this study. All the other situations that arise during the game, PK, PP, etc., should be considered the experimental variants. In any statistical discussion, sample size is always a concern, so why make the already limited (5 on 5) pool of data even smaller? The answer is "Score Effects".

Score Effects takes over when a team has a lead greater than 1 goal, particularly late in the game. Often the team with the sizeable lead will go into a defensive mode instead of continuing to press their offensive attack. Football fans can think of this as being similar to a "prevent" defense. This defensively minded style of play often allows the trailing team to make a push offensively. This leads to more shots and thus higher possession and offensive zone time for the attacking (trailing) team. Further, teams trailing as the game gets closer to its conclusion tend to throw caution to the wind in an effort to score, contributing further to the disparity in shot attempts. In football, these would be likened to onside kicks, trick plays and Hail Mary passes.

When Score Effects are at work, at least in my observation of the Blackhawks, we tend to see the leading team taking defensive zone penalties. The third period of the Blackhawks game vs. the Dallas Stars on December 10, 2013, is an example of this. The Blackhawks had a 5 – 0 lead in the second period. The Stars scored a goal and suddenly their shot attempts quickly escalated until the end of the period bringing their 5v5 possession numbers up dramatically. In the third period, the Stars spent a substantial amount of time in the offensive zone which led to three penalties taken by Blackhawks forwards including one delay of game penalty (puck over the glass) and two hooking penalties. The Stars then enjoyed three power plays in the third period alone furthering driving up their shot attempts.

A practical example of Score Effects in a recent game comes from the Blackhawks meeting with the Florida Panthers on December 8, 2013. The Blackhawks dominated play early in the first period leading to 2 goals. An early goal for the Blackhawks in the second period led to the Panthers playing a more aggressive offensive style of play. The Panthers scored two goals in the second period, bringing the score back to being close. The Blackhawks scored again making the score 4-2 for the remainder of the second period. In the third period, the Blackhawks scored a power play goal early in the period making the score 5-2. Following that goal, the Panthers had 31 shot attempts to the Blackhawks 8. 11 of the Panthers shots came on two power plays from penalties against the Blackhawks (interference and holding). That still leaves 20 shot attempts at 5v5 which is far more than the leading team attempted. This is a standard example of Score Effects.


CF% 5v5 Close

FF% 5v5 Close

CF% 5v5

FF% 5v5











As you can see from the table above, when the score was close (sans Score Effects) the Blackhawks dominated possession. With the score filter removed, the Panthers had better possession numbers than the Blackhawks thus demonstrating the affect of Score Effects on shot attempts and style of play.

If you frequent social media during games, you have probably noticed sentiments such as "Play 60", "Keep your foot on the gas", "Don’t give up the 3rd period lead" and the like. Whenever the Blackhawks have the lead and I see these types of messages, I think of Score Effects.

Once the variants are removed, Corsi and Fenwick become much more reliable over time as analytical tools. CF% and FF% are useful in comparing teams and players across the league and even game by game. Many fans get an idea from watching the game as to which forward line or defensive pairing had the best performance of the night. Comparing players and line combinations on a team are aided by adjusting the CF% and FF% values to make them Relative. CF% Relative and FF% Relative allow us to see how a player stacks up against his teammates. Relative values tell us how the team performs when the player is on the ice. If the value is positive, the team performed better (more shot attempts/higher possession numbers) with that player on the ice than when he was off of it. If the value is negative, the team performed better when the player was off of the ice as opposed to on it.

Relative Percentages do not mean that certain players are good and others are not good. There are many factors that affect these numbers such as Quality of Competition and Zone Starts and so really what we are looking for is strength of performance. We can use this information to determine where the strengths and weaknesses of the team are located. If the team’s checking line consistently has better Relative numbers than the offensively gifted second line, perhaps the usage and deployment of the line needs to be revisited. Further, large disparities between a team’s lines may indicate a team with less forward depth or heavily front loaded lines.

Here’s a look at some of the teams around the league in terms of CF% Rel (5v5) just to get an idea of how the numbers look:


CF%Rel. First

CF%Rel. Second

CF%Rel. 2nd to Last

CF%Rel. Last


Saad 5.4

Toews 5.0

Kruger -7.6

Smith -8.3


Tarasenko 5.6

Shattenkirk 5.1

Reaves -7.4

Lapierre -8.1


Parise 10.6

Koivu 10.5

Fontaine -6.5

Brodin -6.9


Arcobello 6.7

Perron 6.3

Acton -8.8

Joensuu -10.5


Eriksson 11.7

Bergeron 10.3

Paille -9.1

Campbell -11.3


Niskanen 7.2

Crosby 6.4

Sutter -8.0

Glass -11.0

Distribution such as that seen from the Blackhawks and the Blues are representative of many teams in that the Relative numbers are fairly evenly spread out. The Bruins have a much wider distribution as do the Penguins. The Minnesota Wild are similar to these two teams as well. The distribution of a team's Relative possession numbers is heavily dependent upon not only depth of talent but also the Usage and Deployment of the players. The next installment in our series on beginning to understand advanced stats will cover Usage and Deployment including Zone Starts, Quality of Competition and Quality of Teammates so that we may better appreciate what they mean.

*All statistical data gathered via