This week we are going to take a second look at the data set from last week and dive into a few more players and specific categories.

As a refresher, last week we used The Frozen Tool's report Home/Away and examine trends in categories that happen more often in home games than in away games. The upshot of it all was that most of the counted categories (goals, assists, points, power-play goals, power-play points, hits, and shots) happen more often at home than during away games, and only penalty minutes happens more at away games. These largely fell on a pretty standard line consistent with their size, meaning that there was a lot more variation (so big swings when comparing a player's home pace to their away pace) in categories that had lower totals (goals, power-play goals, etc.) but much less variation in categories with high volume totals (shots, hits). It essentially means that sample size was the biggest factor in determining how much variation there was, but there were a few categories that were unexpected – Power-play points, power-play goals, and penalty minutes. These categories fell outside of the line and so could actually be more or less likely based on where the game is being played.

Today our goal is to take a look at players who have performed consistently across a couple of years, and to see if largely the home/away trend is repeatable through multiple seasons.

To get started I exported another Home/Away report from Frozen Tools – the 2018-19 season. I ran it through the same analysis as the 2019-20 report to get per player numbers for each season for each category. I did a little r^2 to get an idea of how closely correlated the data sets were. The details aren't particularly important here, but I decided to focus on three categories, points, power-play points, and shots. If a player had a lot of control over their home vs away performance, I would expect the two data sets to be relatively strongly correlated (with likely a stronger correlation between shots than points since shots happen so much more frequently). Given that we learned that power-play points happen more frequently at home than we would have expected and that is likely due to officials calling more penalties for away teams, we might also expect that to be fairly well correlated between years.

Without going into the math, the general result is "not that correlated." Power-play points were the most consistent year to year, but it was still a pretty weak correlation. Next comes shots, and then points. This pattern at least makes sense given what we know about the frequency of those stats.

While the pattern isn't that consistent across the entire NHL, there are definitely some players who, over the course of the sample, were consistently productive at home, or at away games. Next I want to dive into what we see at the individual level.

For a second reminder, in this section we will be looking at players and their percentage in a given category. The full description is in last week's article, but the quick take-away is that we are looking at the player's home-per-game pace compared to their full season pace. Basically the higher the percentage the better their pace was at home games the lower (and more negative) the pace, the more productive they were at away games.

First up: Points. In the table below we can see a player's performance in 2019-20, 2018-19, and their average for the two seasons. The table is sorted by average so we can see who were the highest performers over the course of the two seasons.

Name	PTS 19-20	PTS 18-19	Average
JESPER BRATT	81.76%	58.49%	70.12%
CONOR SHEARY	70.55%	53.73%	62.14%
ANTHONY BEAUVILLIER	40.31%	83.26%	61.78%
NICK FOLIGNO	68.00%	44.08%	56.04%
ALEX KILLORN	50.82%	60.00%	55.41%
TAYLOR HALL	41.55%	65.58%	53.56%

One note of caution there. This table does not mean that Jesper Bratt is our league leader in points at home – but when we breakdown his point pace, his personal pace is skewed the most toward home games.

The next table does the same, but for away games.

Name	PTS 19-20	PTS 18-19	Average
J.T. COMPHER	-69.33%	-43.54%	-56.44%
CHARLIE COYLE	-16.22%	-49.54%	-32.88%
JOEL ARMIA	-27.26%	-36.71%	-31.99%
CARL SODERBERG	-17.20%	-44.90%	-31.05%
PIERRE-LUC DUBOIS	-42.48%	-16.39%	-29.44%

There isn't a lot to dive into yet, but I did want to give the flavor of some of the names that were popping up.

The table I was really interested in was the power-play points table. Since power-plays were a big cause of the home/away discrepancy I wanted to see if the stats were more repeatable, and if specific players were more likely to have consistently high numbers. The following table contains the top ten, rather than top five as we can see the average does not drop off as quickly as in points. That is consistent with the idea from above that power-play points are more repeatable than overall points.

Name	PPP 19-20	PPP 18-19	Average
JADEN SCHWARTZ	42.83%	117.13%	79.98%
MARK SCHEIFELE	71.68%	60.87%	66.27%
WILLIAM NYLANDER	64.76%	66.67%	65.71%
JOHNNY GAUDREAU	78.35%	51.85%	65.10%
MAX PACIORETTY	65.35%	56.11%	60.73%
VICTOR HEDMAN	84.93%	28.67%	56.80%
PATRICE BERGERON	70.01%	33.97%	51.99%
STEVEN STAMKOS	91.94%	10.00%	50.97%
BRAD MARCHAND	14.29%	79.83%	47.06%
JAMES NEAL	15.41%	76.36%	45.88%

The biggest takeaway on this list is that we see a lot of really solid top power-play producers. All of these players are averaging around three or more minutes of power-play time per game over the course of the 2019-20 season. There is definitely still fluctuation year to year (14 percent to almost 80 percent for Brad Marchand), but more players consistently rank high here than in points.

Name	PPP 19-20	PPP 18-19	Average
MATS ZUCCARELLO	-95.15%	40.00%	-27.57%
RYAN JOHANSEN	-72.61%	-50.00%	-61.31%
JOSH BAILEY	-117.09%	0.00%	-58.55%
DOUGIE HAMILTON	-29.09%	-85.71%	-57.40%
TOM WILSON	-79.90%	-30.55%	-55.22%
NINO NIEDERREITER	-45.68%	-61.83%	-53.75%

There are far fewer players in the negative percentages here, and that makes sense given everything we just discussed. We also have a wide range of usage here too. Dougie Hamilton is up over three minutes a night on average where Tom Wilson and Josh Bailey are well below two.

Finally a quick look at shots. Top home producers:

Name	SOG 19-20	SOG 18-19	Average
JAMIE BENN	43.40%	57.34%	50.37%
VICTOR HEDMAN	59.80%	1.76%	30.78%
DYLAN STROME	44.93%	10.05%	27.49%
MARK SCHEIFELE	36.32%	17.09%	26.70%
EVGENY KUZNETSOV	26.29%	22.73%	24.51%

Top away producers:

Name	SOG 19-20	SOG 18-19	Average
BROCK BOESER	-16.74%	-25.56%	-21.15%
ELIAS PETTERSSON	-5.89%	-27.74%	-16.82%
WILLIAM NYLANDER	-3.26%	-27.69%	-15.47%
JOHN TAVARES	-24.03%	-5.59%	-14.81%
KYLE PALMIERI	-19.86%	-8.89%	-14.37%

I wanted to take a moment and explore two players that came up above.

First up Mark Scheifele. He made the top ten for home power-play producers, and was very consistent between the two years. He also was in the top five for shots produced at home over the course of two seasons as well. He is as consistent a player as we have for producing at home given these categories. So what do we know about Scheifele? Well he gets over three minutes of power-play time and has a relatively high percentage of his overall points coming from power-play production. This is true of essentially everyone on that top ten list. He also is a moderate to low shooter, but a very high percentage of those shots come on the power play. He ranked fifth in the league if we sort by percentage of total shots that come on the power-play. Again the key here is power-play time. Scheifele's deployment and playing style seem to make him an ideal candidate for home production.  

Jesper Bratt is our top points producer at home, but he ranks just out of the top five in shot pace for away games. That means somehow Bratt takes a higher percentage of his shots during away games, but is scoring more points at home, and no it isn't because all of his points are assists. He actually had 16 goals and 16 assists in 2019-20. In this case Bratt appears to be an exception that proves the rule. If we dig into his numbers, he consistently gets low total ice time, low power-play time, low shot counts, and low point paces. It really looks like his low counts across the board make him much more the outlier because of those small sample sizes, where any random event has an outsized impact on his percentages.

That is all for this week. Thanks for reading.

Stay safe out there.

Want more tool talk? Check out these recent Frozen Tool Forensics Posts.

Frozen Tools Forensics: Home sweet home

Frozen Tool Forensics: Multi-Cat Defensemen

Frozen Tools Forensics: Bubble Keeper Week with Gaudette, Hyman, and more

Frozen Tool Forensics: Multi-Cat Forwards