The results were somewhat inconclusive. On the one hand, the inter-year correlation for even strength save percentage is no stronger for goalies remaining with the same team when compared to the value for goalies that changed teams. This suggests that team effects are negligible.

On the other hand, there is a statistically significant correlation between the even strength save percentage of starters and backups. Moreover, the magnitude of the correlation is moderate when viewed in light of the fact that even strength save percentage exhibits low reliability over the course of a single season. This suggests that team effects are important.

The purpose of this post is to look at whether -- and if so, to what extent -- team effects play a role with respect to penalty kill save percentage. The same methods used in yesterday's post will be applied here. If readers are interested in the specifics of each method, I'd encourage a reading of the original post, in which the calculation steps are set out in some detail.

Firstly, a comparison of goalies that changed teams to goalies that remained with the same team. Here's a summary of what this method entails:

Goalies that played for more than one team in a single season were excluded. No minimum shots faced cutoff was employed. However, because some of the goalies in the sample faced very few shots in a given season, I used a weighted correlation in which the weight assigned to each season pair was the lower number of shots faced in the two seasons used...[a]dditionally, because the league average [PK SV%] was not uniform over the period in question, I adjusted each goalie's raw [PK SV%] by dividing it by the league average [PK SV%] in that particular season.The results:

No evidence for team effects here. The correlation for goalies that changed teams is actually larger, although the difference is not statistically significant.

Next, determining the correlation between starters and backups. Again, a refresher as to the specifics of the method involved:

I separated starting goaltenders and backup goaltenders into two groups. A starting goaltender was defined as the goaltender that faced the most shots for his team in a particular season. All other goaltenders were defined as backups, except for goaltenders that played for more than one team in a season, who were excluded from the sample. Just like in the first method, the [PK SV%] for all goaltenders was adjusted by dividing same by the league average [PK SV%] in the particular season. I then determined the weighted correlation between the [PK SV%] of a team's starter with the collective [PK SV%] of his backups. The weight assigned to each data pair was the lower number of shots faced by either the starter or his backups. So, for example, if the starter faced 1000 shots, and his backups collectively faced 1400, the weight would be 1000.

The application of the above steps yields a correlation of 0.07 over 340 data pairs, a value which is not statistically significant - there's a roughly 19% chance that a correlation that large or larger could occur by chance alone. That said, given the low number of shots faced on the penalty kill by the average goaltender over the course of a single season, it is not possible to obtain a statistically significant correlation between starters and backups unless team effects accounted for a substantial percentage of the non-luck seasonal variation in PK SV%. For example, a correlation of 0.10 - which would barely be significant at the 5% level - would imply a very large role for team effects, given that PK SV% for individual goaltenders has a low seasonal reliability (see the next paragraph).

Proceeding on the assumption that the correlation between starters and backups is reflective of a true relationship, the next step is to compute the seasonal reliability co-efficients for each variable. I obtain approximate values of 0.28 for starters and 0.07 for backups. This implies a true correlation of 0.50.

Finally, I have goaltender data at the individual game level for the last three seasons against which the plausibility of the above results can be checked. The penalty kill data I have is inclusive of 4-on-5 situations only, but that shouldn't make a huge difference. The table below displays the split-half reliabilities for starter and backup PK SV%, as well as the split-half correlation between the two variables, both of which have been averaged over 1000 trials.

These values imply a true correlation of 0.54, which is consistent with the results of the second method.

So there you have it - comparing goalies that switched teams to goalies that remained with the same team suggests team effects are unimportant in relation to PK SV%. But there is a positive correlation between the PK performance of starters and backups, which indicates that team effects are relevant. Those who read yesterday's post will be aware that the data for even strength save percentage tells the same story.

Interestingly, the data suggests that team effects may be more important at even strength than on the penalty kill. This is unusual as penalty kill save percentage at the team level is somewhat more reliable than even strength save percentage, once you control for the disparity in sample size - that is, the fact that a team faces many more shots at even strength than it does on the penalty kill.

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## 2 comments:

So It doesn't make any difference from the panlty kill save percentage. So it is something that you won't include in the data.

تعتبر شركة بلاستك هوم من افضل مصانع عبوات بلاستيك pet حيث يتم تصنيع العبوات البلاستيكية من مادة البولي ايثيلين تيرفيثالات ستخدم على نطاق واسع في الصناعة، بما في ذلك صناعة عبوات بلاستيك. عبوات PET يمكن استخدامها لخزن مختلف أنواع المنتجات الغذائية والصحية، والمشروبات المعدنية والغازية، وغيرها. تعد عبوات PET شائعة في الاستخدام بسبب كفائتها في الحفاظ على الجودة والحفاظ على طعم المحتويات، وكذلك بسبب خواص الأمان والبيئة الخالية من الأضرار.

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