NIELSON NUMBERS
It was Neilson as a young coach of the Peterborough Petes in major junior hockey from 1967 to 1976, who first developed the individual scoring chances plus/minus stat
Neilson came to believe that going by shots on net wasn’t a very good way to judge a game. A team might be outshot in a game, yet still have been the superior team, getting far more scoring chances than the opposition
For example, King says, a team might be outshot 34 to 30, but when you took a closer look, most of those 34 shots had come from the outside and the outshot team had actually outchanced the opposition 18 to 12.
Neilson developed a systematic way to keep track of scoring chances and also to keep track of which players helped create those chances and which players made mistakes on chances against.
Neilson defined a scoring chance as a dangerous shot from a zone in front of the net. For his boundary, he drew a line back from the side of the goalie crease to the faceoff dot, then straight back to the blueline. But any shot that came from the point in this scoring chance zone either had to be screened or tipped to count as a scoring chance.
Almost always, a shot had to be on net to be considered a chance. If a team had a chance to score, but made an extra pass and no shot came out of it, Neilson still considered that a chance, as the opposition had still exploited his team to create a dangerous moment
Tracking individual scoring chances also helps coaches recognize the real contributions of players who aren’t so flashy, such as players who go hard to the net. In this system, they get a plus mark if they help create a chance by screening the goalie
You see value in players other people don’t see. Because some people only see goals and assists and flashy plays. They don’t understand what a guy can do for you in a certain role. So it does give you a little clearer picture, for the coaches and the manager of the team to know what you’ve got.
One thing I found is that wingers are much more likely to get plus marks than minus marks, because they have more offensive than defensive responsibility. To be doing their job, I came to believe, they should have at least 2.5 chances for compared to every one chance they give up.Centres have more defensive responsibility than wingers so they have more minus marks than wingers do, though generally the same number of plus marks. For centres in the NHL, a two-to-one ratio is acceptable. Defencemen, with the least offensive and most defensive responsibility, should be around one-to-one chances for to chances against.
DEFINING SCORING CHANCES
Scoring Chances (SC):4
All shot attempts that have danger 2 or greater. As originally described here.
Take the above quantities and replace “G” with “SC” like so:
SCF: Scoring chances produced by a player or their teammates when the focal player is on the ice. SCFoff: Scoring chances produced scored by a player’s teammates when the player is off the ice.
iSC: Individual scoring chances.
SCP60: Scoring Chance Pace (per 60 minutes), equal to SCF60 + SCA60.
High-Danger Scoring Chances (HSC):5
All shot attempts that have danger 3 or greater. Take the above quantities and replace “G” with “HSC” like so:
HSCF: Scoring chances produced by a player or their teammates when the focal player is on the ice. HSCFoff: Scoring chances produced scored by a player’s teammates when the player is off the ice.
iHSC: Individual scoring chances.
HSCP60: Scoring Chance Pace (per 60 minutes), equal to HSCF60 + HSCA60.
(Dec 2014)
As any hockey fan knows, there are low quality shots, high quality shots, and many different “categories” in between. However, it is hard to measure these over time and effectively measure which teams or players are getting the best shots or the highest amount of shots in “quality” areas.
We need two features to make this work. First, recall our definition of “danger zones” as broken into probability areas. Second, we’ve empirically tested for higher probabilities within these zones for two types of shots:
· Rebounds: Any shot that follows within 3 seconds of a blocked, missed or saved shot. All have measurably higher probabilities of success in each of the three zones.
· Rush shots: Any shot that follows within 4 seconds of any event in the shooting team’s neutral or offensive zones. This is based on David Johnson‘s definition, but the four second threshold gave general and statistically significant increases in probability.
So based on these measures, the average probability of a goal given the type and locations, and the consideration of team defense, we have these conditions for a “scoring chance”:
· In the low danger zone, unblocked rebounds and rush shots only.
· In the medium danger zone, all unblocked shots.
· In the high danger zone, all shot attempts (since blocked shots taken here may be more representative of more “wide-open nets”, though we don’t know this for sure.)
Teams need to be able to generate scoring chances in order to score (you can score on low quality, “non-scoring chance” shots, but it is highly unlikely and impossible on a consistent basis).
These numbers are simply one way to evaluate a team or a player, and many different statistics and viewpoints are needed to generate conclusions. For now, scoring chances are what we need to use to evaluate a team’s shot quality and how it is related to team success.
What I want to look at in the future is a weighted measure for scoring chances, that takes into account scoring chances for and against in all different situations (EV, PP, SH, etc.) and determines which teams are putting together a good overall system in terms of shot quality. Finding the right weights for PP, SH, and EV situations will lead to an accurate representation and will properly represent team shot quality for and against. This type of measure could explain a lot of different aspects of a team and could eventually lead to solving the shot quality issue
HDSC & QUANTIFYING SHOT QUALITY
The premise underlying this analysis is a simple one. The higher the percentage of overall shots (or shot attempts) that are scoring chances or high danger scoring chances the higher the likelihood the player will post a higher shooting percentage. So, I will evaluate the following “on-ice” relationships:
· HSCF/CF vs CSh%
· HSCF/SF vs Sh%
· SCF/CF vs CSh%
· SCF/SF vs Sh%
There are two key takeaways from these charts. First, these metrics do positively correlate with shooting percentage so to some degree these metrics are capturing shot quality though the correlations aren’t particularly great. The second takeaway is that considering all scoring chances is better at measuring average shot quality than restricting to just high danger scoring chances.
There is another metric that is known to correlate well with shooting percentage for forwards though. Ice time. To see how these scoring chance metrics stack up against ice time I looked at the relationship between TOI% (or the percentage of the teams ice time the player gets) and shooting percentage.
TOI% is quite a bit better at estimating shooting percentages than our shot quality metrics. This tells me two things. First, while scoring chance data is telling us something about shot quality there is still a lot that isn’t accounted for. Second, scoring chance data hasn’t caught up to the coaches “eye-test” ability yet – coaches are better at evaluating talent than our scoring chance data
In conclusion, shot quality is still something we are having a terribly difficult time understanding. It clearly exists and is a significant factor in driving on-ice results but out ability to measure and quantify what leads to higher shot quality is still clearly lacking. Scoring chances as defined by War on Ice might be a step in the right direction but our attempts to quantify it are still a step (or two) behind the coaches.
WHY SCORING CHANCES IS A FLAWED METRIC
No publicly available statistic predicts future goals better than shots.
I would argue that the appeal of scoring chances as a statistic is largely due to its intuitive significance and its name.
Obviously, creating chances to score is important to scoring goals and therefore winning games.
The unfortunate part of the widespread acceptance of scoring chances is that it has several flaws that are rarely discussed. The first, and most significant, is that the term does not have an actual definition meaning.
All of them are based on counting shots that meet certain characteristics such as location and shot type to determine how dangerous a given shot is. But determining those characteristics is left to each organization to define.
The second problem is one of binning. Scoring chances represent “binning” of data.
Binning is almost always a bad idea because it draws arbitrary boundaries around an otherwise continuous set of data. To use our scoring chance example, all shots have a given danger level. The most logical way to measure that is in expected shooting percentage.
Shooting percentage is impacted by the passing sequence that precedes it.
Shots of various types could be expected to go in the net anywhere from nearly 0% of the time (a shot from the opposite end of the ice) to nearly 100% of the time (an empty net shot where the shooter is standing in the blue paint).
So what makes something a scoring chance? A shot with an 8% chance of being a goal? A shot with a 10% chance? 12%? I’m guessing you get the point.
The solution is to start thinking in terms of expected goals.
This line of thinking removes the need for a term like scoring chance because we can define the danger level of shots based on their expected shooting percentage instead of a collection of characteristics.
Thinking of each shot in terms of the likelihood that it becomes a goal is the correct way assess that danger of the shot.
Talking in those terms allows for assessing the danger of each chance without resorting to binning.
Don’t walk away from this post with the message that using scoring chances is bad. It isn’t. My only point is to say that if the goal is to understand which team is creating more dangerous chances, expected Fenwick Sh% (xFSh%) is a better approach.
HIGH DANGER SCORING CHANCES
Team Level HDCF:
When we remove events (blocked shots, missed shots) we lose both repeatability and predictivity.
Scoring chances actually end up being slightly more predictive than raw attempts because we are choosing which observations to exclude. It still has a pretty high sample size, but successfully sifts out a lot of noise. By the time it gets to high-danger chances, however, too much information is ignored, and the value decreases. That’s not to say it has no value, it just doesn’t have as much value as CF%, and is only slightly better than GF%.
NHL teams record one high-danger scoring chance in every 5.24 shot attempts.
The Devils record a high-danger chance in every 4.82 attempts, which is 2nd only to the Rangers (4.15!). Also, the Devils only allow one high-danger chance in every 6.21 attempts which is, once again, 2nd in the NHL, this time to the Wild (6.43). All said, 20.8% of the Devils attempts are high-danger, and only 16.1% of the attempts against them are. That differential is right behind Minnesota for NHL-tops.
But can we keep that up? Well, actually, the ratio of attempts that are high danger is definitely significantly repeatable.
This could indicate that the ratio of shot attempts that come from dangerous zones is, at least in part, a product of scheme.
A good team will limit an oppositions overall attempts, but their tendencies will remain constant.
(27 shots/7HDC/25% vs 44/11/25)
Same team, same %, different raw totals. Tendencies remain the same
Player Level HDCF:
HDCF%Rel is Repeatable/Predictive for Forwards, but not Defenders.
This means that HDCF%Rel is not a particularly good statistic to use for projecting Defenders forward in the year. There’s not enough guarantee that it will a) stay consistent, or b) correlate to goals.
High Danger Chances per Shot Attempt is Repeatable
This mirrors what was found at the team-level. It is actually also somewhat intuitive. Defenders have more repeatable HDCA/CA (ratio of attempts against that are high danger) and forwards are roughly even.
So to recap what we know so far, the ratio of high-danger chances that a team produces while a player is on the ice is not particularly repeatable for defenders, but is for forwards. Also, the percentage of attempts that are high danger (both for and against) is repeatable across positions.
So if HDCF is less reliable and predictive than CF, why would we use/trust it?
Well, that’s where it get’s interesting. One such justifications could be it’s inertia relative with regards to zone starts.
HDCF% is not affected by Zone Start Ratio in Forwards
The stats that we use exist on a spectrum. That spectrum goes from possession to goals. The closer we are to the possession end of the spectrum — something like CF%, for instance – the more it matters where the puck starts. This is because if you win a faceoff after getting an OZS (offensive zone start), you are very likely to get at least 1 shot attempt (CF) out of it.
However, the more you move towards the goal end of the spectrum, the less given it is that you would record such an event, because more skill-related events need to fall the right way. After winning the faceoff, you need to move the puck, open up lanes, move without the puck into the lanes, make successful passes, and get the shot off, and score.
Only the last aspect of that is taken away for a high-danger chance. You still need to do so much right, that the fact that you started in the offensive zone ends up being of negligible importance. We already know the zone starts are only relevant for about the first 20 seconds of the shift, and high-danger chances take a while to create, so it likely runs up against the limits of beneficial impact.
dCF% was significantly impacted by dOZS% to the tune of about 1 percentage point per 10% change in OZS%. So, if a player with a 48 CF% increased his OZS% from 30% to 40%, his CF% would be projected to rise to around 49%.
However, that significance drops off quite a bit in dHDCF%, and drops off completely for forwards.
I do find it interesting that defenders, who had a lower p-value for CF%, seemed significantly more resistant to the OZS% change.
And finally, by the time we get to GF%, so much more has to go right other than the zone the play starts in, that OZS% becomes completely irrelevant.
Although HDCF% is not very repeatable or predictive, it IS still descriptive. HDCF/CF correlates to shooting percentage (r=0.21), HDCA/CA correlates to save percentage (r=-0.15) and the difference correlates to the sum of Sv% and Sh% -- PDO (r=0.17).
So when you hear people call PDO the “luck” statistic, that’s not entirely true – it is partially a product of a team having disproportionately more higher danger chances than their opponents which are more likely to lead to goals.
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