So I'm going to be using numbers from the past six years (2010-2016). I'm just going to do a simple year over year analysis like Eric did, but I'll split up between forwards and defensemen (I tried splitting up forwards between center and wingers but there was virtually no difference). Also the minimum amount players needed to have qualified for the analysis was 400 minutes for both years.
Repeatability
n
|
Position
|
A160
|
A260
|
A60
|
1402
|
Forwards
|
0.36
|
0.19
|
0.43
|
836
|
Defensemen
|
0.20
|
0.03
|
0.24
|
Here are the correlation coefficients for each metric. And I would say this all makes sense. First assists for both are more reliable than secondary assists. More so for forwards than for defensemen, for whom it's almost virtually random. And this is reflected in the total assists numbers. For defensemen it's only slightly higher than first assists. For forwards we see a a little bit more there.
Of course this isn't it. Let's split up between players who stayed on the same team versus those who switched teams. As Eric T. noted, teammates play a role in assists, so numbers on players who change teams might be closer to the truth. So let's see:
n
|
Repeatability
|
A160
|
A260
|
A60
|
1020
|
Forwards-same
|
0.36
|
0.21
|
0.44
|
382
|
Forwards-Diff.
|
0.32
|
0.10
|
0.35
|
Difference
|
-0.04
|
-0.11
|
-0.09
| |
604
|
Defensemen-same
|
0.24
|
0.12
|
0.26
|
232
|
Defensemen-Diff.
|
0.06
|
0.02
|
0.11
|
Difference
|
-0.18
|
-0.10
|
-0.15
|
n
|
Predictivity
|
A160
|
A60
|
1020
|
Forwards-same
|
0.40
|
0.44
|
382
|
Forwards-Diff.
|
0.33
|
0.35
|
1402
|
All Forwards
|
0.39
|
0.43
|
604
|
Defensemen-same
|
0.27
|
0.26
|
232
|
Defensemen-Diff.
|
0.08
|
0.11
|
836
|
All Defensemen
|
0.23
|
0.24
|
As you would imagine total assists edges out first assists in all cases except, oddly, defensemen who stay on the same team. I won't put much thought into that, it's nothing. Also, the edge in each case is really small. I would imagine that this being only one year worth of data contributes to this. As you would imagine, after getting a few years of playing time we can make a better estimate of secondary assists.
Conclusion
The numbers here are, overall, close to the one's shown by Eric T. five years back. And I think the best bet when looking at assists is focusing primarily on primary assists. But secondary assists still matter a little. One year of assists tells us more than primary assists. Not by much, but there is something. And given a few years, we'll get a better judge of a players secondary assist "talent". Just looking at the leaderboards for secondary assist over the past few years will tell you that it means something. Secondary assists may contain a lot of randomness, but they still matter. The gain over primary assists is minimal, but they shouldn't just be discarded as noise (just mostly noise).
**All data courtesy of Corsica.hockey
***Update:
This is a good article on secondary assist-http://fivethirtyeight.com/features/some-nhl-stars-get-more-assists-at-home-than-they-deserve/. The next step would probably be adjusting for rink bias. Also this
is a good chart showing how small the spread of talent is in secondary assists. Even though it would have been better to do a separate chart for forwards and defensemen.@tangotiger SD of z-scores is 1.02, histogram suggests moderate skew/shift right pic.twitter.com/E2bf1jQYCH— Michael Lopez (@StatsbyLopez) April 22, 2016
I'd love to get my hands on this data. My issue is that this argument always looks at 2As in isolation, and then uses the result to claim that they shouldn't be used in aggregation; that's just a bad conclusion.
ReplyDeleteThe fact that total assists (A1 and A2) is less noisy then primaries (based on the tables above) seems to confirm that. If you want a measure that is repeatable, i.e., with high year-over-year correlation, then the best of the three is total points.
So while A2s might not be a highly repeatable talent, neither is A1, and actually the aggregate (A1 and A2) is. There might be factors that are beyond a player's control that influence that, but it doesn't matter: If you're goal is to have a stat with high repeatability, then removing 2A is a mistake.
As an analogy: Suppose I buy the same amount of fruit every week. I buy apples every week, bananas some weeks, and coconuts some weeks.
If you do a week-over-week analysis for total fruit, we get perfect correlation. If we do it for apples, we get high correlation, but it would be low for bananas and coconuts.
Based on that evidence, do we remove bananas from our sample set just because they are noisy? No! Not if we are trying to measure total fruit-buying.
So if assists are recorded as a proxy measure for playmaking; and if we think repeatability indicates a talent; then removing 2As from our assessment actually makes things worse.
Yeah, it really comes down to weighting primary and secondary assists by their value (however one defines that).
ReplyDeleteFor data check out - corsica.hockey