1. Why is it so hard to evaluate individual defensive play?
At the individual level there’s only so much a player can do to influence play on their own
Players have to act as a unit -> Results are heavily influenced by teammates
Player matchups can be more varies -> More influence of opponents
Defensive systems can have a large impact on results -> Hard to compare across teams
Granular data doesn’t exist for defence
For offence we know the shooter, who had the assist on goals
Goalies are voodoo and hate statistical analysts

2. What types of passes/shots are we interested in at the individual level?
Metric
Sh%
Why do we care?
Skill(s) Measured
Entry Assists Against/60
7.2%
Zone entry work has shown how important the type of entry is
Zone Entry Prevention, Gap Control, etc.
OZ Center Lane SAA/60
13.9%
Expect this to be a skill – how well players control the middle of the ice
Forcing play to the outside
Behind the Net SAA/60
11.7%
Higher danger shots against – expect that defencemen should be able to influence this by winning battles down low
Winning puck battles in corners, maintaining position
Royal Road SAA/60
27.6%
Very high shooting percentage – could be influenced by ability to remain structured defensively or prevent rush attempts
Preventing cross-ice passes
Point Shots/60
2.6%
Relatively low danger shots – could be influenced by keeping players to the outside
Covering the point, keeping play to the outside

3. Defencemen - What’s repeatable?
Split-Half Correlations
(Does data in one half predict data in the other?)
Which metrics seem to be most repeatable?
Metric
Split-Half R^2
Shots Assists Against/60
0.54
Entry Assists Against/60
0.53
OZ Center Lane SAA/60
0.32
Behind the Net SAA/60
0.39
Royal Road SAA/60
0.01
Point Shots/60
0.17
Entry Assists Against/60
In line with Eric Tulsky’s findings
OZ Center Lane SAA/60
D need to control middle of ice
Behind the Net SAA/60
D need to control area behind net
Players with >= 250 minutes in each half (N=48)

4. Defencemen – Are these skills related?
Correlations Between Defensive Metrics
A fair amount of correlation exists within defensive metrics
Entry
OZ Centre
BTN 1
0.66
0.55
0.78
Behind-the-net and OZ Centre- Line Passes are most correlated
Both measure play in D-Zone
Correlation isn’t perfect
There can still be players who excel at one area but not the others
Useful for deployment within a team
Players with >= 500 total minutes (N=51)

5. Example: Randomly Selected Defencemen
Metric
P.K. Subban
Shea Weber
TOI
338.6
352.1
Rate Stats
Entry Assists Against/60
15.4
16.7
OZ Center Lane SAA/60
9.6
10.2
Behind the Net SAA/60
4.1
2.9
Relative Stats
Entry Assists Against/60 Rel
3.0
2.1
OZ Center Lane SAA/60 Rel
1.0
Behind the Net SAA/60 Rel
0.4
0.5

6. Forwards - Do they even matter on D?
Split-Half Correlations
(Does data in one half predict data in the other?)
Forwards appear to have less control over defensive results
Metric
Split-Half R^2
Shots Assists Against/60
0.36
Entry Assists Against/60
0.48
OZ Center Lane SAA/60
0.31
Behind the Net SAA/60
0.19
Royal Road SAA/60
0.04
Point Shots/60
0.20
Lower repeatability across each metric
Biggest influence on Entry Assists Against/60
Unfortunately, you actually do need to backcheck
Some impact on Center Lane Shots
Players with >= 250 minutes in each half (N=47)

7. Example: Buffalo Sabres Forwards – 2015/16
Player Name GP
TOI
OZ Center Lane SAA/60
Entry Assists Against/60
O'REILLY, RYAN 18
275.8
11.1
15.0
GIRGENSONS, ZEMGUS 19
253.6
9.0
14.9
LARSSON, JOHAN 17
221.8
7.8
14.6
REINHART, SAM
252.3
9.8
14.3
MOULSON, MATT 20
208.5
9.5
13.5
EICHEL, JACK
299.3
7.2
13.0
GIONTA, BRIAN
265.0
6.3
12.9
MCGINN, JAMIE 22
252.1
6.4
KANE, EVANDER 16
251.8
8.8
11.9
Noted Two-Way Center Ryan O’Reilly had the most Entry Assists Against/60
Noted Defensive Liability Evander Kane had the fewest Entry Assists Against/60