WHAT IS IPP
individual point percentage - a calculation of the number of times an individual player gets a point (either a goal or an assist) relative to the number of total goals scored while he’s on the ice
hopefully give us a better idea of which guys are handling the puck a lot in the offensive zone and helping to create goals. When we look at the results over a long period of time, we can see that the guys who are doing this usually rise to the top. But that’s not to say it gives a perfect reflection of reality with just one year of data. The league average for forwards is somewhere between 69.5% and 70.0%, and most players will regress toward that number over time even though, in an individual season, you might see a player do much better or much worse (a lot like shooting percentage)
INDIVIDUAL POINT PERCENT
(Oct 2012)
Individual point percentage is a calculation of the number of times an individual player gets a point (either a goal or an assist) relative to the number of total goals scored while he’s on the ice
A lot of the really good offensive players in the league should find a home near the top of the chart (which is exactly what you’d expect from a statistic that’s trying to tell you who’s driving the play in the offensive zone). But as with most things based on sample size of less than 100 (no one was on the ice for more goals-for at five-on-five than Evgeni Malkin at 81), the year-to-year variation is quite high. Over a long period of time, the cream mostly rises to the top, but in any one season, you can get some funky results, similar to the kind of thing we see with shooting percentage.
The average individual point percentage for a forward who was on the ice for at least 25 goals-for (there are 258 players) is 69.1% and the median is 69.5%, while the standard deviation is 8.3%, so that should provide some idea of whether or not a player is doing well (i.e. those players above 77.1% or below 60.8%).
This obviously isn’t the be-all end-all for analyzing which players are driving the play (or being impacted by luck), but I do think as one tool among many, it can be very helpful.
Essentially what IPP is doing is identifying which players are better offensively than their line mates, not who the best offensive players in the league.
A big part of this exercise – for me, at least – is to identify which players are riding an unsustainable IPP rate. A player’s IPP over several seasons gives you an idea of their capability
The sheer amount of regression we see from year to year suggests that luck is a big part of what drives IPP in any given season.
I’d say that a very high IPP (something over 85%) does automatically suggest that the player has been somewhat lucky since no one can really sustain that. The same would be true of an established NHLer with a very low IPP (something under 50%).
IPP 2008-2012
(Oct 2012)
The idea is that this statistic will tell us which players were driving play in the offensive zone. One of the problems is that, because of the small sample size at the level of the individual season (no player was on the ice for more than 81 goals-for), the results are swamped by luck
In order to move the conversation forward, I think we need to have a better sense of how players do over several seasons, which should help to deal with the sample size problem, and give us a sense of what a reasonable range of looks like.
A normal range for players is between 60 and 80 with only a handful outside those bounds.
Players on lower lines making their way up this list by virtue of being clearly better than their linemates but still not all that good.
Perhaps it’s because IPP measures production relative to linemates, meaning it can’t distinguish between players that are actually good offensive players and players who are just a little better than the dregs they are playing with.
Linemates are clearly going to have an impact on IPP, but even if the value here is knowing which guys are driving offense on their line, I think that’s really significant. It’s certainly a lot better than not telling us anything.
Knowing which players are integral to a lines offense and which ones aren’t can be valuable information from both a coaching and a general managing perspective.
IPP ON THE POWER PLAY
(Oct 2012)
Today I’ll be looking at the individual point percentages for forwards at five-on-four.
The average individual point percentage in that group is 61.6% and the median is 62.6%, and the standard deviation is 8.1%. You’ll notice that those first two numbers are significantly lower than what we saw for five-on-five play (70.0% average and 69.5% median). This makes complete sense: defensemen play a much bigger offensive role when teams are on the power play, which leaves fewer points available for the forwards.
You’ll also notice that the standard deviation is higher, which also comes as no surprise (we’re dealing with a smaller sample, both in terms of total qualifying players and in the number of goals).
BREAKING APART IPP
At first I was skeptical of the value of IPP because essentially it only tells you how important the player is to the teams offense when the player is on the ice, and not really anything about the actual skill level of the player. A good player with really weak line mates can put up a pretty good IPP even if he isn’t a great offensive player. Or, a good third liner could have a similar IPP as a good first liner, but not be anywhere close to each other in terms of overall talent level. But, upon further thought I figured there would be some value in determining who is leading the offense and who might be deserving of a line promotion (i.e. might be too good for his current line mates) or a demotion (might be holding their line mates back). So, I decided I would look into IPP a bit further.
The list above tells us who the guys that control the offense are, but there is a mix of goal scorers and play makers in the list. I wanted to split the list up further because for team building purposes I think it makes sense to want to have a mix of playmakers and goal scorers and not be too heavily weighted towards one over the other. I decided to calculate Individual Goals Percentage (IGP) and Individual Assist Percentage (IAP). Let’s start by looking at the top 30 players in terms of IGP.
One of my comments in Scott’s articles was that it might be interesting to reduce the importance, or even eliminate, the second assist as it may not be as important as the first assist. To see the impact of the second assists, let’s take a look at Individual Second Assist Percentage (ISAP).
Looking at the above list, I can’t say the second assist offers anything in terms of determining who the top play makers are. There really is a huge mix of players in that list, very few one would consider quality playmakers. It seems to me that the second assist is probably largely a useless point somewhat randomly distributed among players.
everything I understand about hockey tells me that the second assist is almost certainly of lesser importance.
CROSBY AND IPP
To recap the concept, individual point percentage is a calculation of the number of times an individual player gets a point (either a goal or an assist) relative to the number of total goals scored while he’s on the ice. So, for example, if a player is on the ice for fifty goals-for during five-on-five play over the course of the season and he gets a point on forty of them, his individual point percentage at five-on-five would be 80%.Most forwards end up at about 70% over the long haul, but there are some that buck the trend.
Sidney Crosby led the league over the last four seasons with an IPP of 84%. One of the things discussed in the comments to those posts was what kind of impact playing with a guy like Crosby might have. Points are assigned on a zero-sum basis, so if he’s getting more, who’s getting less?
As you might expect, the answer is everyone. If we look at the numbers of Crosby’s most common linemates (both forwards and defensemen) over the last two seasons, we notice a distinct trend for them with Crosby on the ice
Just one player has a better IPP with Crosby on the ice that with Crosby on the bench
The trend is clear: players who are on the ice with Crosby are less involved in the offense and handle the puck less frequently. This makes good sense. Sidney Crosby is an elite player, and when he’s on the ice, you want him handling the puck as much as possible, something that will turn his linemates into more complementary players.
This gives more weight to the idea that IPP is most helpful for telling us which players are driving the offense on their line, and not necessarily which players are the best at driving offense full-stop.
In the end, I think that, when we have enough data, IPP tells us quite a lot about how individual players work together, and about which players are able to drive offense in specified circumstances, but is less effective at identifying the best players in the league in any kind of systematic fashion.
IPP – WHAT IS IT
(Aug 2015)
There are three critical components of forecasting at the player level.
· The first is being able to properly identify a player’s peaks and valleys as a performer.
· The second is knowing when the laws of regression will come into play, and to what extent regression will shape future outcomes.
· The third is applying these concepts on the player acquisition front – knowing when to consider selling on overvalued assets, and knowing when to consider buying on undervalued assets.
One common example is that of a defenceman who rides a very high on-ice save percentage to impressively low goals against totals. On-ice save percentage is not a particularly repeatable skill, which brings into question what kind of talent impact – if any – the defenceman can actually have on his goaltender’s ability to stop pucks. If the player’s perceived value is affected by his on-ice save percentage, it offers real opportunity for a savvy trader to enter the market and instantly benefit.
Another metric I like to look at is ‘Individual Point Percentage’ (“IPP”), which shows how frequently a player was awarded a point in an event his team (a) scored; and (b) the player was on the ice. Much like our on-ice save percentage example for defencemen, IPP regresses substantially towards league averages.
On average, forwards usually receive a point on about 68 per cent of goals scored when they are on the ice. That number sits at about 30 per cent for defencemen.
If we want to identify outliers, we must first observe strong deviations from the league norms, and then observe strong deviations from a player’s career norms.
Individual Point Percentage (“IPP”) is a great way to identify and explain potential outlier performances in a given season. Teams who are cognizant of the effects of IPP and the principles of regression analysis can use IPP to steal talent as it becomes cheaply available, and send away talent that reaches a peak price by virtue of favourable random variance.