MISSED SHOTS
I have heard it argued that goalies who are solid positionally force shooters to miss the net more often.
However, this position isn’t supported by the facts.
The correlation between save percentage and miss rate was slightly negative, meaning that as save percentage went up, shooters were less likely to miss the net, not more likely, which goes against the conventional wisdom.
Of all shots attempts at net that weren’t blocked (i.e. saves, goals, and misses), 29.1% directed at the starting goalies ended up missing the net. The stat for backups was 28.9%,
Missed shots therefore seem to be of little use in terms of evaluating goalies, at least based on this year’s results. The missed shot rate does not seem to be correlated with goalie ability at all. If anything, shooters seem to miss the net less often against good goalies
xSV% - ACCOUNTING FOR SHOTS
compares a goalie’s current even-strength save percentage to what we would expect an average goalie’s save percentage to be given the quality of competition faced by that goalie.
xSV% = ActualSV% - Expected SV%
xSV% is the difference between the goaltenders actual save percentage and what we would expect an average goaltender to achieve in similar circumstances.
A higher xSV% is good. A lower or negative one is not.
xSV% AND TEAM EFFECTS
What xSV% Is:
· Expected Save Percentage based on a 110 game moving average of the opposing shooter at the time of each shot faced by a goalie.
· Better players typically have a higher shooting percentage, therefore if a team limits their opponent’s best players from shooting the puck, they will raise their own ExpSV%.
· Forwards typically have a higher shooting percentage, if a team can limit the amount of shots taken by an opposing teams forwards and instead force them to rely on their defenceman to generate shots, they will raise their own ExpSV%
xSV% AND TEAM EFFECTS
Expected SV% comes from the same model I used in my latest ExpG model. Here is a quick breakdown of the different variables and a brief explanation of why they are included in the model:
· Adjusted Distance
o The farther a shot is taken from the lower likelihood it has of resulting in a goal
· Type of Shot
o Snap/Slap/Backhand/Wraparound/etc...
o Different types of shots have different probabilities of resulting in goals
· Rebound - Yes/No?
o A rebound is defined here as a shot taking place less than 4 seconds after a previous shot. Rebounds are more likely to result in a goal than non-rebounds
· Score Situation
o Up a goal/down a goal/tied/etc…
o It has been proven that Sh% rises when teams are trailing and vice versa
o This adjustment, while only slight, helps to account for a variety of other aspects that we are currently unable to quantify yet have an impact goal scoring
· Rush Shot - Yes/No?
o Shots coming off the rush are more likely to result in a goal than non-rush shots.
Now that we have the structure of our ExpSV% we need to add shooter talent into the mix since the model currently assumes league average shooting talent for each shot, which we know is not the case in reality.
So I wanted to make a multiplier for each player in each season to get a best estimation of their personal effect on each shot’s probability of resulting in a goal.
xSV% will show us more signal than noise sooner than 5v5 SV%.
xSV% BETTER PREDICTOR OF GOALTENDING
These results form the basis that goaltenders forcing opponents to miss is a repeatable skill.
Since goaltenders face varying workloads, an adjustment for shot quality is necessary.
Accounting for shot quality allows for a better comparison of goaltenders by revealing the value a goaltender provides relative to an average-level goaltender facing a similar workload.
xSV% consistently outperforms raw Fenwick Sv% starting at the 50-game mark.
To conclude, xSV% provides a better predictor of goaltender performance in comparison to Fenwick Sv%
SHOULD WE INCLUDE MISSES
Goalies who position themselves better could force shooters to try to aim for a corner or the side of the net and therefore shoot more pucks wide. And if goalies can force shots to go wide, we should then take notice of it.
goalies have the ability to force shots to miss the net and that team effects are minimal at best.
A FEW STUFF ABOUT MISSES
If a goalie can influence the amount of shots that miss the net that means a goalie can “force” shots wide. That means shots that would normally end up hitting the net are now missing the net. Is this not a positive thing?
With Sv% an average save was worth about .078 goals (since league average Sh% is about 7.8%).
So both a miss and a save are worth .056 goals (league average FSh% is about 5.6%) since they are in the same bucket.
The average Miss% is about 27.8% (so 72.2% of shots hit the net). So if a shot misses the net, that means a goalie stopped a SOG which carries a .078 value.
Goalie Miss% is more repeatable than Sv% and that the team effects affecting Miss% is minimal.
ADVANCED GOALIE METRICS
(July 2017)
The Expected Save Percentage (ES% Index) is a predictor of a goaltenders success based on a number of inputs that assigns the individual difficulty of each shot the goaltender faces. The inputs used in the model are shot location, puck visibility, and the rate at which the puck changes angle before or during the shot.
Then, through an array of formulas, the model determines the expected save percentage for each shot on goal given the inputs. Once these expected save percentages are aggregated over a game, or over a season, we can see how the goaltender’s actual save percentage compares with the expected save percentage and compare them to their peers. The best goaltenders will consistently exceed the predicted save percentage whether they are facing 20 high quality shots or 40 lower quality shots. The Expected Save Percentage Index—the difference between real save percentage and expected save percentage—will measure the proficiency of the goaltender.
The expected save percentage is a benchmark, and it is the discrepancy between the realized and expected save percentage that will be the true measure of individual performance.
GOALIES CAN’T INFLUENCE SHOT MISSES
Other than simple variance shot misses can come from 3 different parties:
· The offense: Shooters being bad at hitting the net, shooters taking shots in bad situations or even shooters shooting wide on purpose.
· The defense: Forcing shots from bad situations, defending shooting lanes so it’s more difficult to hit the net or putting pressure on the shooter by being close.
· The goaltender: Leaving very little open net to shoot at, thus making it more likely that the shooter misses the net.
Defenders generally miss the net more often than forwards, and secondly, defenders seem to be missing the net less and less.
Let’s start by looking at miss% for the different shot types:
Here we see that the miss% is relatively stabile over time, and that slap shots tend to miss the net more often than snap shots and wrist shots.
So, just to conclude the macro perspective. We have shown that shooter composition (forwards/defenders) and shot types are important factors, when we are discussing shot misses.
variance on shot misses against are almost the same as the variance on shot misses for. This is not what we would expect, if goaltending is the primary cause for shot misses.
Better goaltenders don’t face more shot misses.
· There doesn’t appear to be any connection between shot distance and shot misses.
· Goaltenders who has a lot shots blocked in front of them also see an increase in shot misses.
· There does appear to be some correlation between shot misses and the number of shots taken by defensemen.
· There is no correlation between slap shot% and Miss%.
Goaltenders have very little impact on shot misses, and that the quality of the goaltender certainly doesn’t matter.
I haven’t found any clear evidence that goaltenders can impact shot misses though.
Comments