Fantasy Hockey

Frozen Tools Forensics: PIP Review – DobberHockey


Every now and then we turn our attention to certain categories of stats on the site, include a little rundown of what they are, and then see how we can use them to give some context to what’s going on with certain players. We featured Evgeni Malkin last season in our PPI review. Today it’s Jordan Kyrou.

PPI or individual point percentage is a measure of how often a player receives points on a goal that is scored while on the ice. It can also be more colloquially referred to as one-off participation. So if five goals are scored when a player is on the ice and he scores the goal, or an assist on four of them, his PPI would be 80%. The statistic alone is interesting, but it becomes really useful when we have something to compare it to. For example, we know that attackers tend to have a higher PPI than defenders. This makes sense as forwards tend to be able to score more often. We also know that elite attackers tend to have a higher PPI than the bottom six, with your elite players potentially hitting 75-85%. What is most useful, however, is comparing a player’s current sample size to a recent and larger sample size. For example, Alex Ovechkin’s current PPI is 76.9%. Ovechkin is an elite striker and it is certainly within the grasp of other elite forwards, so we might not think so. However, there is only one season where Ovechkin has participated in more than 70 percent of the goals scored while on the ice, and in his last four seasons he has come close to 63 for. hundred. In this case, the inflated PPI means that something has changed in his game / deployment, or that he is gaining more points than expected, and his PPI (and therefore his points rate) will likely drop somewhat.

On Frozen Tools, there are a number of places where you can find IPP. For individual players, it is listed under the Advanced Tab, and it’s included in a number of reports. For our purposes today, we will focus on IPP report, because well, we are talking about PPI.

To get started, let’s take a look at our best participants to date (all data in this article is taken from January 6, ahead of matches played that day).

Last name Team GP Please IPP
JACK HUGHES New Jersey 17 17 89.5
JORDAN KYROU LIST 30 33 89.2
BRAD MARCHAND BO 24 30 85.7
PIUS SUTER DET 34 18 85.7
JONATHAN HUBERDEAU FLORIDA 33 42 84

Since the PPI is a percentage, it covers all players and total points, so those who were on the ice for a goal and got a point will have a PPI of 100. Since I put a 15 point threshold, we are only looking for players who have a reasonable number of opportunities.

Topping our list here is Jack Hughes. It also has the smallest sample, both in terms of points and games played, so it’s probably the most variable. This list also gives us the opportunity to highlight a few other features of PPI. IPP really helps highlight who the game is about on the ice. Elite players – superstars – tend to have high PPIs and we can see that here with Brad Marchand and Jonathan Huberdeau. Your Connor McDavids and Leon Draisaitls usually end up here as well. The elite of the elite. And that makes sense, anyone watching the Edmonton game quickly realizes that the goals are coming from McDavid or Draisaitl.

Sometimes, however, we see other players (looking at your Pius Suter) who end up with high PPIs. With just 18 points, it’s hard to argue that Suter is part of the elite. The high PPI could be a function of luck and be very irreplaceable, could be a function of a role on a power play that pulls its other numbers, or could be the result of a driving play of Suter on, say, a third line. This line doesn’t score a lot of goals, but when they do, Suter is involved because he’s the best player on the line. In Suter’s case, that’s probably Option A. We don’t have a huge track record here, but in a sample of 55 games last season, his PPI was down about 55%. Granted, that context was largely different in Chicago, but these are the data we have to work with.

That leaves us with the player I really wanted to talk about in Jordan Kyrou. Kyrou’s previous speed record was 52, and he’s now at 90. He’s seeing an increase in ice time both overall and on the power play. That’s all good, but the reason it’s on our list is almost 90% PPI. McDavid himself only has one season with a 90% PPI, so it’s clearly a very difficult PPI to earn. Kyrou probably isn’t the next McDavid, so it shouldn’t come as a surprise to anyone that we should expect that percentage to drop. What’s good to see is that unlike Suter, Kyrou has reached 75% on several occasions during his career. These are pretty small samples, he did so in his 55-game sample last season. The implication is that Kyrou was involved in the game regardless of where he inserted himself into the roster, and that usually means he doesn’t stand a chance of scoring points with good teammates. , or the best player on a fourth row who can’t hang on with the big guns – that means he might actually be good.

In short, I love the Robert Thomas, Kyrou, Vladimir Tarasenko lineage for all involved, and I am excited about Kyrou’s breakthrough that has been happening for a few years now.

Another good use of IPP is to examine context-specific IPP information. The PPI report breaks down a player’s PPI into different states of strength – same strength, power play, and power penalty. This is especially useful when we are looking at players who do not meet our expectations (but can certainly highlight other players as well).

Let’s take a look at David Pastrnak. Pasta is currently on a 62-point pace – a far cry from his 100-point strides a few seasons ago. Now, with a 50 point drop, there are a lot of aggravating factors (his personal shooting percentage and secondary assist rate to name just two), but one number that stands out is his six point advantage. digital to date. That equates to 0.21 powerplay points per game versus his 0.54 and 0.5 powerplay points per game during his 100-point seasons. Again, there can be many causes for a drop in power-play production, but his power-play PPI is currently 42.9% instead of the 75-80% of the past three seasons. This means that goals are scored on the power play and he just doesn’t participate. Now we should take a little deeper dive to be really sure what’s going on, but it seems reasonable enough to bet that his PPIPP is too low. If we assume he only gets a few more points in the future, he hits something like a 75-point pace very quickly, even without any further bouncing.

To that end, here are the biggest changes from last season. Some of them (Tomas Hertl) had unsustainable PPIPPs last season, but others like Nikolaj Ehlers and Mark Stone clearly deserve better.

Last name Team GP PPP 20-21 RPPPP 21-22 RPPPP
TOMAS HERTL SJ 34 5 55.6 100 -44.4
NIKOLAJ EHLERS WPG 32 3 42.9 86.7 -43.8
STONE BRAND VGK 19 3 37.5 81 -43.5
TOM WILSON WSH 30 3 30 72.7 -42.7
ZACH HYMAN GED 31 5 33.3 71.4 -38.1
CAM ATKINSON RPS 33 2 33.3 69.2 -35.9
DAVID PASRNAK BO 29 6 42.9 77.8 -34.9
ANDRE BOURAKOVSKI COLLAR 27 6 42.9 77.8 -34.9
FILIP FORSBERG NSH 25 6 42.9 76.5 -33.6

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