As Micah
published his posts over the past couple of weeks, I was obviously irked that
his projections had me squarely in second place. There were a couple of other
things that didn’t quite sit right with his model, and so I set out to improve
on it, but more importantly settle on something that would crown me the
preseason favourite. After toiling with Micah’s points above replacement
allotment for draft picks, however, I realized that I couldn’t really do much
better—either in translating draft position to predicted points from the draft
OR in predicting an overall first place finish for the Patrik Stefans. You’ll
see I inevitably lean heavily on Micah’s approach; I think he’s done well to
quantify each team’s relative strength to one another, and as you’ll also see,
he’s done pretty well in building a pretty damn good team.
As a first
point of departure, I simply used Left Wing Lock’s projections instead of
Dobber’s for each team’s projected keeper roster. There have also already been
four trades that have affected Micah’s rankings, and I have incorporated those
into my analysis. Below is a ranking of KL teams by their projected keeper
roster points.
|
Patrik Stefans
|
673
|
|
Milan Micahleks
|
671
|
|
Los Amjawors Kings
|
647
|
|
Mackhawks
|
629
|
|
Valeri Nickrooshkins
|
628
|
|
Quebec Rordiques
|
616
|
|
Phillipsadelphia Flyers
|
611
|
|
Teeyotes
|
594
|
|
Winter Claassics
|
591
|
|
Fylanders
|
576
|
|
Powder Rangers
|
571
|
|
Joshfrey Krupuls
|
560
|
|
WBS Parkers
|
545
|
|
Toilers
|
533
|
|
Schizzarks
|
528
|
|
Dicklas Lidstroms
|
481
|
That’s a
much better start. But now the tricky part. How can we meaningful look at a
team’s complement of draft picks and assign it some value that captures its
absolute strength for the purposes of our format?
It
initially struck me as incorrect that Micah focussed on points above
replacement for his entire analysis. For starters, there didn’t seem to be much
reason to obfuscate season ending totals, when you could simply add replacement
value of 560 points (9*40 + 4*30 + 80) to his totals and put things in more
intelligible, apples to apples, terms for GMs. But I see the need for his
approach when we turn our minds to the draft and the uncertainty of a forward,
defenseman, or goalie getting nabbed at each draft pick.
The second
thing that seemed wrong about Micah’s approach to the draft, though, was that
he totalled all 10-12 picks’ points above replacement. Our scoring rosters cap
out at 14. What does my 6th round pick matter if I can fill out my
scoring roster with my earlier picks? But then, of course, every year we see
GMs hitting homeruns in late rounds, and so those picks actually do matter
#allpicksmatter.
I was
inspired by Micah’s attempt and also this paper by some oyster lover to come up
with a truer way of capturing pick value, based on probability. http://myslu.stlawu.edu/~msch/sports/Schuckers_NHL_Draft.pdf
Taking
draft data from 2013-2016, and using Micah’s fungible thresholds of 30, 40, and
80, I assigned probabilities* to each draft pick 1 through 160 of picking a
player to surpass those thresholds (table below). I then multiplied those
probabilities by the average points* above replacement for each draft position.
The result is the graph below, after and about which I provide further comment.
|
Draft
Pick
|
P(scoring
roster)
|
|
1
|
0.667
|
|
2
|
0.563
|
|
3
|
0.55
|
|
4
|
0.536
|
|
5
|
0.5
|
|
6
|
0.429
|
|
7
|
0.464
|
|
8
|
0.464
|
|
9
|
0.536
|
|
10
|
0.536
|
|
11
|
0.536
|
|
12
|
0.536
|
|
13
|
0.571
|
|
14
|
0.607
|
|
15
|
0.607
|
|
16
|
0.5
|
|
17
|
0.5
|
|
18
|
0.464
|
|
19
|
0.5
|
|
20
|
0.464
|
|
21
|
0.464
|
|
22
|
0.464
|
|
23
|
0.536
|
|
24
|
0.536
|
|
25
|
0.571
|
|
26
|
0.5
|
|
27
|
0.536
|
|
28
|
0.571
|
|
29
|
0.571
|
|
30
|
0.536
|
|
31
|
0.607
|
|
32
|
0.571
|
|
33
|
0.643
|
|
34
|
0.607
|
|
35
|
0.571
|
|
36
|
0.643
|
|
37
|
0.607
|
|
38
|
0.536
|
|
39
|
0.607
|
|
40
|
0.5
|
|
41
|
0.464
|
|
42
|
0.429
|
|
43
|
0.393
|
|
44
|
0.393
|
|
45
|
0.357
|
|
46
|
0.357
|
|
47
|
0.464
|
|
48
|
0.464
|
|
49
|
0.464
|
|
50
|
0.464
|
|
51
|
0.5
|
|
52
|
0.571
|
|
53
|
0.464
|
|
54
|
0.357
|
|
55
|
0.429
|
|
56
|
0.393
|
|
57
|
0.393
|
|
58
|
0.393
|
|
59
|
0.357
|
|
60
|
0.464
|
|
61
|
0.5
|
|
62
|
0.5
|
|
63
|
0.536
|
|
64
|
0.5
|
|
65
|
0.536
|
|
66
|
0.5
|
|
67
|
0.464
|
|
68
|
0.5
|
|
69
|
0.464
|
|
70
|
0.429
|
|
71
|
0.429
|
|
72
|
0.357
|
|
73
|
0.357
|
|
74
|
0.286
|
|
75
|
0.214
|
|
76
|
0.143
|
|
77
|
0.214
|
|
78
|
0.25
|
|
79
|
0.286
|
|
80
|
0.321
|
|
81
|
0.357
|
|
82
|
0.429
|
|
83
|
0.5
|
|
84
|
0.464
|
|
85
|
0.393
|
|
86
|
0.321
|
|
87
|
0.25
|
|
88
|
0.25
|
|
89
|
0.214
|
|
90
|
0.179
|
|
91
|
0.107
|
|
92
|
0.214
|
|
93
|
0.214
|
|
94
|
0.25
|
|
95
|
0.25
|
|
96
|
0.214
|
|
97
|
0.179
|
|
98
|
0.179
|
|
99
|
0.107
|
|
100
|
0.143
|
|
101
|
0.179
|
|
102
|
0.179
|
|
103
|
0.214
|
|
104
|
0.286
|
|
105
|
0.321
|
|
106
|
0.393
|
|
107
|
0.429
|
|
108
|
0.357
|
|
109
|
0.393
|
|
110
|
0.393
|
|
111
|
0.321
|
|
112
|
0.321
|
|
113
|
0.214
|
|
114
|
0.143
|
|
115
|
0.179
|
|
116
|
0.143
|
|
117
|
0.179
|
|
118
|
0.179
|
|
119
|
0.143
|
|
120
|
0.179
|
|
121
|
0.179
|
|
122
|
0.143
|
|
123
|
0.143
|
|
124
|
0.071
|
|
125
|
0.071
|
|
126
|
0.107
|
|
127
|
0.071
|
|
128
|
0.143
|
|
129
|
0.143
|
|
130
|
0.143
|
|
131
|
0.179
|
|
132
|
0.214
|
|
133
|
0.179
|
|
134
|
0.179
|
|
135
|
0.107
|
|
136
|
0.143
|
|
137
|
0.107
|
|
138
|
0.107
|
|
139
|
0.071
|
|
140
|
0.143
|
|
141
|
0.179
|
|
142
|
0.214
|
|
143
|
0.25
|
|
144
|
0.25
|
|
145
|
0.25
|
|
146
|
0.286
|
|
147
|
0.25
|
|
148
|
0.214
|
|
149
|
0.214
|
|
150
|
0.179
|
|
151
|
0.214
|
|
152
|
0.214
|
|
153
|
0.214
|
|
154
|
0.214
|
|
155
|
0.214
|
|
156
|
0.214
|
|
157
|
0.214
|
|
158
|
0.179
|
|
159
|
0.179
|
|
160
|
0.179
|
*probabilities
and averages were calculated using picks at or within 3 of each given draft
pick for the last 4 drafts (2013-2016). For example, to calculate the
probability of selecting a player that would make your scoring roster with the
7th overall pick, I looked at picks 4 through 10 for each season.
Likewise, in calculating the average, I considered only those picks at or within
three draft picks that made a scoring
roster.
I think the
graph tells a compelling and somewhat accurate story on the one hand, but
doesn’t help all that much in forecasting points that GMs will get out of the
draft, on the other. It captures that very real steep drop off of talent
available at the draft over the course of the first round. Things seem to level
off as GMs mine for solid scoring roster depth over the next three rounds or
so, and then there’s a steady decline that approaches complacency in the middle
to late rounds, before some GMs feel inspired to hit those late round jacks;
or, you know, that tail could be all a function of luck.
To the
“doesn’t help all that much in forecasting points” remark, clearly there are
going to be players drafted between the 10th and 70th
pick that score more points than the 48 or so predicted by the graph. And
clearly, you would want the 10th pick over the 70th pick,
right?
I have some
possible explanations. I think the graph is revealing that draft “strength” may
not actually count for as much as we’d like to think. At least that’s how the
data bears out. I believe it’s a combination of this all being a clusterfuck of
chance as well as some GMs just preparing a lot better than others, and showing
up on draft day with a clear plan and sticking to it a lot better than others
(me). Further, I’d think that GM performance on draft day bears no correlation
with draft strength going in, or if anything may even be negatively correlated;
I can see a GM with poor draft position thinking that he needs to prepare
better for some late round steals.
Where does
that leave us with getting back to this pre-season forecasting? I have one
other observation. Back to the point about only needing to fill out scoring
rosters, and the implication that only #4picksmatter, guess how many KL teams
had all of their keepers on their scoring roster at season end in 2016-17 (or
would have had them but-for trades)? Just two: the Joshfrey Krupuls and the
Dicklas Lidstroms. Allow me to go off on this tangent further, if you haven’t
already concluded from the mention of those teams that having all 10 of your
keepers make your KL is no great achievement.
24 keepers
didn’t make scoring rosters last year, either due to injury or shitty
performance. I included Loui Eriksson in this, because even though he would
have made the Parkers’ SR, all that took was 17 points!! (it’s like Rome
actually tried to build his team in a day). It’s further interesting to note
that the list of failed keepers included 5 goalies but only 1 defenseman,
Hampus Lindholm (who would have made Rome’s SR as a forward). I point all this
out for GM consideration as we approach the keeper roster submission deadline
tomorrow.
Back to the
task at hand. 24 failed keepers means an average of 1.5 not making a team’s
scoring roster, which means that #5.5picksmatter. In recognition of that, and
completely ignoring what the data says from past drafts (!), I’ve used Micah’s
model, but just factored in the top SIX draft picks for each team in
forecasting their points from the draft.
What I also
sought to capture was the positional mix of keepers and the differing needs of
GMs to fill out their positional scoring rosters at the draft. I have the
(Edenton?) Toilers as the only team not keeping a goalie, so they have the
highest draft points “base” of 250 on which to build. I have my team and the
‘sics keeping only one dman each, and so we have the lowest base of 200 on
which to build. After whatever mix you needed to fill out your SR, I just added
70 pts to your base, representing the replacement value of an extra d and an
extra forward (and again, completely ignoring that it is much rarer for a kept
defenseman to miss an SR than it is for a kept forward). After adding pts above
replacement to the base, I’ve multiplied everything by 5.5/6 to predict the
contribution that the draft will make to each team’s scoring roster.
|
Team
|
Pts above Rep
|
Base
|
SR Draft Pts
|
|
Toilers
|
78
|
250
|
301
|
|
Powder Rangers
|
91
|
220
|
285
|
|
Schizzarks
|
78
|
230
|
282
|
|
Dicklas Lidstroms
|
87
|
220
|
281
|
|
Joshfrey Krupuls
|
69
|
230
|
274
|
|
Fylanders
|
79
|
210
|
265
|
|
Valeri Nickrooshkins
|
75
|
210
|
261
|
|
Milan Micahleks
|
61
|
220
|
258
|
|
Teeyotes
|
70
|
210
|
257
|
|
Quebec Rordiques
|
69
|
210
|
256
|
|
Phillipsadelphia Flyers
|
67
|
210
|
254
|
|
Mackhawks
|
67
|
210
|
254
|
|
WBS Parkers
|
64
|
210
|
251
|
|
Winter Claassics
|
70
|
200
|
248
|
|
Patrik Stefans
|
68
|
200
|
246
|
|
Los Amjawors Kings
|
49
|
210
|
237
|
Lastly,
putting it all together, I’ve multiplied each team’s projected keeper points by
0.85 (1.5 out of 10 keepers don’t make a scoring roster) and added that to the
draft points above. Below are my predicted standings at the end of the 2017-18
KL season!
|
Team
|
Pts
|
|
Milan Micahleks
|
828
|
|
Patrik Stefans
|
818
|
|
Valeri Nickrooshkins
|
795
|
|
Mackhawks
|
789
|
|
Los Amjawors Kings
|
787
|
|
Quebec Rordiques
|
780
|
|
Phillipsadelphia Flyers
|
773
|
|
Powder Rangers
|
770
|
|
Teeyotes
|
762
|
|
Fylanders
|
755
|
|
Toilers
|
754
|
|
Winter Claassics
|
750
|
|
Joshfrey Krupuls
|
750
|
|
Schizzarks
|
731
|
|
WBS Parkers
|
714
|
|
Dicklas Lidstroms
|
690
|
For a whole
host of reasons, the standings won’t look like this. Some teams are going for
it, while some aren’t. Some GMs will intentionally draft players early, who
they know won’t play a single game this season. Prospects are not factored in whatsoever.
And trades and free agent signings will shake things up. While I think the
above standings are reflective of where we sit going into our 8th
campaign, nothing is predetermined; that’s why we play the games! Wait…
