clock menu more-arrow no yes

Filed under:

Power Play Save Percentage

Executive Summary: Goalies do not differ on the Power Play. Teams do not differ either.

Dilip Vishwanat

Now let's look at Power Play Save Percentage. I am going to look at 2009-10 through 2013-14. I have shown repeatedly that different goalies are different at Even Strength. As always, in order to do regression analysis I constructed logits (ln odds of a save). I threw out the goalies whose logits were zero or undefined. All the regressions are weighted by number of shots faced. Lastfirst is the name of the goalie variable.

Goalies are all the same, teams are all the same.

> LinearModel.7 = lm(PPLogit ~ lastfirst + Team, data=Goalies5, weights=PPSA)

> anova(LinearModel.7)

Analysis of Variance Table

Response: PPLogit

Df Sum Sq Mean Sq F value Pr(>F)

lastfirst 107 1198.1 11.197 1.0260 0.4374

Team 28 416.7 14.882 1.3637 0.1200

Residuals 162 1767.9 10.913

It looks like there is a small correlation between ESSP and PKSP. ESSP explains about 17% of the variability seen in PKSP (I.e., a little).

> cor.test(Goalies5$ESVPCT, Goalies5$PPSVPCT, alternative="two.sided",

+ method="pearson")

Pearson's product-moment correlation

data: Goalies5$ESVPCT and Goalies5$PPSVPCT

t = 7.8363, df = 296, p-value = 8.393e-14

alternative hypothesis: true correlation is not equal to 0

95 percent confidence interval:

0.3157524 0.5043694

sample estimates:



Graphically, the relationship looks like:


From the graph we can see that seven goalies at the low end are driving the apparent correlation. If we take out those seven, who faced at most 4 shots and a collective total of 18, we get

> cor.test(PPminus$ESVPCT, PPminus$PPSVPCT, alternative="two.sided",

+ method="pearson")

Pearson's product-moment correlation

data: PPminus$ESVPCT and PPminus$PPSVPCT

t = 0.8386, df = 289, p-value = 0.4024

alternative hypothesis: true correlation is not equal to 0

95 percent confidence interval:

-0.06608471 0.16332683

sample estimates:



So, in reality, nothing there. Total randomness.


There is no team effect. There is no goalie effect. In general, PP Save Percentage is not related to ES Save Percentage.

"Absence of proof is not proof of absence." From the ES data, we know that it takes several thousand shots to tell goalies apart. The busiest goalies in this data faced less than 300 shots over the 5 seasons. There may well be a goaltender effect. However, if it is far too small to see in 5 years of action and it certainly will not be visible in 1 year.

If a 0.920 goaltender sees 50 PP shots in a season, the 95% Confidence Interval for observed PP Save Percentage is 0.808 to 0.978.

Over the 5 years, goaltenders faced a total of 8862 shots and made 8063 saves for an overall PP Save Percentage of 0.90984.