DEFENSIVE STATS
Blocked shots can be reactionary because a player can’t stop a shot from happening in the first place, so that’s just one way to think about defense. Otherwise, it helps to look at on-ice metrics like shots and expected goals against.
It would be amazing if there was a way to measure good positioning. But for specific skills we can quantify: checks — whether with the stick or body) that lead to a change of possession, gap control, blocked passes, and puck retrievals all help. So do zone exits, especially in today’s game where a strong defense can be a puck-possession game.
BEST TEN STATS TO EVALUATE DEFENSEMEN
(March 4, 2010)
Defensive Zone Faceoffs
teams should (and generally do) use their best faceoff men when in their own or opposing ends,
Adjusted Plus/Minus
Goals-Against Average
Adjusted Plus-Minus looks at a player’s offense-relative-to-defense contributions, so the natural evolution would be to remove the offensive component (the “plus”) and look only at the defensive component (the “minus”).
Relative Corsi
replace the plus/minus statistic with the Corsi number, which looks at the attempted shot differential while a player is on the ice, instead of the goal differential.
Despite the attempt to remove the goaltending factor, Corsi remains a very contextual statistic, and players on teams with systems that allow a lot of shots from the outside will be shown in a very favorable light with Corsi.
To address some of these limitations, Gabe Desjardins introduced Relative Corsi to measure territorial dominance. Given the correlation between shots taken and in which zone the action is primarily taking place, he felt that the best application of Corsi is to study it relative to a player’s teammates, a concept he explored a little further in one of his earliest Puck Prospectus articles
Quality of Competition
One of the most significant remaining limitations of Adjusted Plus/Minus, Goals-Against Average and Relative Corsi is that a player can get a huge boost from playing with certain linemates, against certain opponents, and being asked to play certain roles in certain game situations.
Quality of Competition our best way to address the influence a player’s opponents have on his plus/minus, goals-against average or Corsi numbers, but it also tells us to whom coaches are turning to shut down the best lines.
Ice-Time
Point Allocation
Based on the ability to estimate how much ice-time a player should get based on his offensive contributions, any difference between a player’s actual ice-time and his expected ice-time is most likely a consequence of that player’s defensive contributions. Consequently, Point Allocation is measured in the proportion of ice-time that is explained by a player’s defensive contributions.
Goals Versus Threshold
team defense is measured by looking not at how many goals are allowed by a team, but rather how many shots were allowed (currently without regard to shot quality). The team defense is then assigned to players based on how many goals were allowed when they were on the ice. Position is taken into account, as defensemen are considered twice as responsible for preventing goals as forwards.
Player Contribution Defense (PCD)
PC looks at how many goals were prevented at a team level, then subtracts the goaltending contribution. Since shot quality is factored into the goaltending contribution, PC has the advantage of measuring more accurately how many goals were prevented by the skaters. The resulting marginal goals prevented by team defense is then allocated to individuals, which is based on goals scored against and whether they’re a defenseman or a forward (albeit to a slightly different ratio than GVT).
Delta and DeltaSOT
Awad melded the best of the defensive statistics already discussed above into a single offense-relative-to-defense measurement named DeltaSOT. In a nutshell, here’s how it’s built:
· Awad introduces shot quality to a Corsi-based plus-minus statistic to remove the goaltender’s influence, thus creating the Delta statistic.
· Next, situational information based on defensive zone faceoff research is introduced to remove the effect of being used in certain roles and playing primarily in certain zones, creating DeltaS (S for Situation).
· For the benefit of those players whose numbers have been significantly affected by playing against particularly weak or strong competition, QualComp is used to create DeltaSO (O for Opponent).
· Finally, to address the concern that the numbers are still being significantly influenced by the quality of ones linemates, Awad makes a simple adjustment to create the end product: DeltaSOT (T for Teammate).
DIFFERENCE BETWEEN 1ST & 3RD PAIR DEFENSEMEN
(Oct 27, 2010)
The difference between 1st tier and 3rd tier defensemen was only 4 minutes per game, from 13 to 17 minutes,
Generally speaking, defensemen don’t drive the results at even-strength; forwards do, especially, as we saw, top forwards.
Zone starts don't vary much by caliber of defenseman, while GPs tend to play against much stronger opponents, and with somewhat stronger teammates. This is what we expect.
EVALUATING DEFENSEMEN BASED BY RANK ON TEAM
You will notice that there is relatively little variability in CF% from D1 through D5. A slight drop off but not significant. The drop off in GF% is much more significant with D1 and D2 above 50% but the rest of the defense below 50%.
From D1 through D5 there is a slow drop off in save percentage with a total drop off of 0.45 percentage points and then save percentage spiked up 0.55 percentage points from D5 before dropping off again for depth defensemen.
Shooting percentages are fairly stable across all defensemen rising a bit for D5 and dropping a bit for D6.
CA/60 is fairly stable across all defensemen except for D6 where CA/60 is elevated. CF/60 shows a mostly steady decline from D1 through to depth defensemen except for D6 which is abnormally low.
D1 and D2 are on the ice for more goals for than against while D3 through depth defensemen are on the ice for more goals against than for.
There is a lot to digest here and I am not certain I have fully grasped everything that is going on but here are a few of my theories and thoughts:
· There do seem to be defensemen who can boost a goalies save percentage. In particular, D6 defensemen seem to have this ability, especially D6 defensemen on good Corsi teams.
· Good Corsi teams also seem to have higher save percentages than poor Corsi teams. Does playing a sound puck possession game make life easier on your goalie? It seems it may. It may also explain why Stanley Cup winners are often good Corsi teams and also often good Save % teams. Of course, it doesn’t explain Cam Ward’s terrible save percentage on a good Corsi Carolina team. There is probably more to the story than that simple explanation but there seems to be some connection between Corsi and Save percentage.
· Good Corsi teams have more balance throughout their defense, both in save percentage and Corsi Against rates. Is this a result of systems or a result of talent? I suspect a bit of both.
· Although I didn’t show offensive stats above, generally, there wasn’t much to see in them. The one observation I will pass on is while D6 defensemen seem to be defensive specialists (low GA/60) D5 defensemen may be more offensive oriented defensemen (higher GF/60) in part due to elevated shooting percentages. I’ll look into offensive stats more when I look at forwards but it could be that D5 defensemen get a bit more ice time with offensive forwards.
· Also not shown is any QoC stats but I did look at them. You can find some tiny differences in QoC among defensemen (and forwards) but they are so small it is difficult to imagine they have any measurable impact.
PDO & DEFENSEMEN
POSSESSION ISN’T EVERYTHING WITH THE EXCEPTION OF DEFENSEMEN
there is more to a forward’s on-ice shooting percentage, over extended periods, than just random variance.
Regardless of what a defenseman’s on-ice Sh% looked like from 08-11, you should basically expect it to be average from 12-14.
defensemen have a minimal impact on their on-ice shooting percentage.
Forwards have virtually no control over their on-ice save percentage.
Defensemen have no control over their on-ice save percentage as well.
The fact that a defenseman’s PDO is practically beyond their control implies that their ability to drive/anchor possession stands as an optimal tool for evaluation (at even strength) on its own.
FAILURE TO ACCURATELY EVALUATE DEFENSEMEN
The magnitude of a player’s impact is largely a product of opportunity. In hockey, opportunity comes in a few different forms – the most prominent one being ice time.
It is a coach’s job to deploy his roster in a way that optimizes his team’s Corsi differential and consequently improves their chances of success. In order to do so, better possession players need to be the beneficiaries of additional even strength ice time.
NHL coaches have historically done a really poor job allocating ice time to defensemen who tilt the ice in the right direction.
This is staggering considering that defensemen have virtually no bearing over on-ice percentages and therefore their possession rates are particularly illustrative of their effectiveness.
Regardless of his rate of production in year 1, a defensemen should be expected to finish within 0.60-0.85 points per / 60 at even strength in his subsequent season. That isn’t a large spread.
The disparity we see between defensemen who consistently finish with a large sum of points year over year compared to those who don’t is largely the result of opportunity in the form of ice time.
DEFENSIVE DEFENSEMEN
SDI – PART 1
The best defenders take the lion’s share of a team’s tough minutes, and prevent the best of the opposition from doing much damage offensively.
defenders facing the highest Corsi REL QoC on a team over a large span of games will generally be involved in the top 4 D on their team’s PK,
I feel that a defender’s key role is to limit goal scoring opportunities which closely correlates to Corsi, thus I stuck with Corsi measures largely.
A defenders job is immeasurably easier if the players they share the ice with regularly are more capable of driving play up ice
Therefore a player who plays against very difficult Corsi REL QoC, while toiling with inferior linemates from a Corsi REL QoT perspective is given a higher index rating.
In summary SDI Sit (Shutdown Defender Index Situational) will be the sum of values for Corsi REL Differential STD Ratio and OZ% STD Ratio, while SDI Res will be the sum of values for Corsi ON 60 STD Ratio and the 20% weighted value of Penalty Differential STD Ratio.
SDI – PART 2
to briefly summarize it compares a blue liner’s Corsi REL QoC, Corsi REL QoT, OZ%, Corsi ON/60, and Penalty Differential to their peers in a given season using the number of standard deviations above or below average in each category (with a 20% weighting on Penalty Differential).
SDI – PART 3
SDI Situational: Corsi REL QoC; Corsi REL QoT; OZ%
SDI Result: Corsi ON; Penalty Differential per 60.
In analyzing the numbers I decided that if one is looking for Shut Down Defenders then it makes more sense to look at their Defensive Zone percentage rather than their Offensive Zone percentage, so for this most recent iteration I switched OZ% for DZ%, and I think the numbers make a bit more sense.
players are rewarded for playing more difficult competition, with worse team-mates from a possession perspective, and from regularly beginning in a worse starting position. They are also rewarded for producing a higher corsi rate (shot attempt differential), and having a good penalty differential (taking fewer than they draw).
SDI – PART 4
Defenders that suppress shot attempts against consistently, without taking an inordinate amount of penalties, will do well by this measure.
It should be noted that NO offensive component is registered in the result score, thus there is no favouring to offensive defenders.
SDI – PART 5
Here is a graph displaying the correlation between the Expected Corsi Against (Based upon the input values of TeamCA20, OppCF20, and DZFO%) versus the output of a player’s actual Corsi Against for all NHL defenders to play 200+ minutes of 5v5 TOI over the past 6 years
As you can see, those 3 input values account for 51.66% of what we observe for a given defender in terms of Corsi Against. His usage, team-mates, and opposition all carry weight in his defensive outcomes – though the single most important factor would be the Corsi Against results of his team-mates
SDI – or Shut Down Index (although to be clear this is no longer an index value) is just the residual score from this correlation. In other words, SDI is the difference between a player’s Expected and Actual Corsi Against performance.
Players who have a lower than expected Corsi Against will have a positive SDI score, players with a higher than expected Corsi Against will have a negative SDI score.
NHL defenders are largely the victims of randomness and to an extent there is little in the way individual control possible in these results.
The flip side of this understanding though, is the corollary statement that players who exhibit extreme results in either the positive or negative case, over an extended period of time, are likely the most significant contributors to their results.
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