After
reading Robert Vollman’s post at The Leafs
Nation about projected Leafs Scoring I thought that it was finally time to revisit
my attempts at forecasting what the Leafs could reasonably expect for scoring
in the upcoming season (assuming of course there is one.)
While I
don’t think there is anything wrong with the current metrics that are currently
out there, I am interested in seeing how my own attempt holds up and would like
to apply my own methodology to the projection game. Where Snepsts67 and VUKOTA
both rely on comparables, my own attempt (for consistency I’ll call it MIRONOV)
looks at each players recent history in conjunction with trends associated with
players aging and overall offensive output changes.
Like
Snepsts67 I chose to go with a range. The high value is based off the best
season the player has had in the past three seasons, the low value is based on
the worst season in the past three seasons, and the average is the average over
the past three seasons. If the younger players do not have three seasons with
20 more or games played in the NHL than CHL, AHL, or NCAA equivalencies from Behind the Net were
used.
The next
piece that is incorporated is the effect of aging, experience, etc. It’s an
attempt to quantify if whether a player should be trending upwards, plateau, or
be on the decline, and to what extent.
So I took
the top 180 scoring forwards for each season since the lockout (180 players
divided by 30 teams gives you the average top six forwards) and looked at
sorted them by age to determine the total number of games played by each age of
player and the average goals or points per game for each age.
Age
|
GP
Total
|
Average
PPG
|
18
|
464
|
0.765
|
19
|
1143
|
0.751333333
|
20
|
2793
|
0.72027027
|
21
|
3662
|
0.750851064
|
22
|
5161
|
0.73641791
|
23
|
7160
|
0.708645833
|
24
|
7061
|
0.732
|
25
|
7853
|
0.736407767
|
26
|
7070
|
0.742795699
|
27
|
7758
|
0.739417476
|
28
|
6659
|
0.732873563
|
29
|
6114
|
0.754390244
|
30
|
5257
|
0.746666667
|
31
|
5377
|
0.717183099
|
32
|
4730
|
0.727580645
|
33
|
4314
|
0.756034483
|
34
|
3368
|
0.779555556
|
35
|
3159
|
0.714761905
|
36
|
2178
|
0.738275862
|
37
|
1640
|
0.678181818
|
38
|
1042
|
0.688
|
39
|
630
|
0.754444444
|
40
|
291
|
0.7475
|
41
|
163
|
0.665
|
42
|
81
|
0.59
|
From there
I took the age with the highest number of games played (Age 25 with 7853 games
played) and divided each of the other age totals from the max number to create
a ratio of how often this age group will fill a scoring role on the team.
The next
step was to multiply that age ratio by the points per game. From there it’s
taking the higher aged number and dividing it by the previous age value (Age 19
value divided by Age 18 value, Age 20 value divided by Age 19 value and so on.)
This finally gives you the value that you can multiply against the players
point per game values.
The last
step is looking at the change in scoring around the league. Since the year
after the lockout the number of points per season from the top six has been on
a modest decline. While not a huge impact the players point per game value and
the age value and multiplied by the change in scoring value which is 98.6%.
All of the
values are listed as point per game, but for the purpose of showing predicted
seasons I have multiplied out what the totals would be over 82 games.
Points over 82 games
|
Points Per Game
|
|||||
High
|
Low
|
Avg
|
High
|
Low
|
Avg
|
|
Kessel
|
90
|
70
|
77
|
1.10
|
0.86
|
0.94
|
Lupul
|
78
|
43
|
56
|
0.95
|
0.53
|
0.68
|
Grabovski
|
55
|
45
|
51
|
0.67
|
0.55
|
0.62
|
Kulemin
|
62
|
35
|
46
|
0.75
|
0.43
|
0.56
|
MacArthur
|
52
|
29
|
41
|
0.64
|
0.36
|
0.50
|
Bozak
|
53
|
29
|
43
|
0.65
|
0.35
|
0.52
|
Connolly
|
70
|
40
|
53
|
0.86
|
0.49
|
0.65
|
van
Riemsdyk
|
60
|
48
|
55
|
0.73
|
0.59
|
0.67
|
Frattin
|
50
|
24
|
35
|
0.62
|
0.30
|
0.42
|
Kadri
|
54
|
37
|
45
|
0.65
|
0.45
|
0.55
|
Goals over 82 Games
|
Goals per Game
|
|||||
High
|
Low
|
Avg
|
High
|
Low
|
Avg
|
|
Kessel
|
45
|
36
|
41
|
0.55
|
0.44
|
0.50
|
Lupul
|
33
|
20
|
27
|
0.40
|
0.24
|
0.33
|
Grabovski
|
27
|
13
|
21
|
0.33
|
0.16
|
0.26
|
Kulemin
|
32
|
9
|
19
|
0.38
|
0.10
|
0.24
|
MacArthur
|
18
|
13
|
16
|
0.22
|
0.16
|
0.20
|
Bozak
|
18
|
13
|
16
|
0.22
|
0.16
|
0.19
|
Connolly
|
18
|
15
|
16
|
0.22
|
0.18
|
0.20
|
van
Riemsdyk
|
32
|
22
|
28
|
0.39
|
0.26
|
0.34
|
Frattin
|
30
|
13
|
20
|
0.37
|
0.16
|
0.24
|
Kadri
|
26
|
11
|
19
|
0.32
|
0.13
|
0.23
|
(what I consider the most likely result in bold.)
I’m sure
I’m overstating Tim Connolly’s numbers to some extent, but in a healthy season
with him playing in the top six it may be a plausible range. The fact is that
given that the numbers are based off of top six forwards it seems that any of
these forwards who do not receive top six ice time will not likely make their
average numbers, and possibly not their low numbers either.
Below is a
comparison to the other predictions for some of the Leafs:
VUKOTA
|
Snepsts67 Average
|
MIRONOV Average
|
||||||||||||
Player
|
G
|
Pt
|
G
|
Pt
|
G
|
Pt
|
||||||||
Kessel
|
33.5
|
72.5
|
30.4
|
63.2
|
40.6
|
77.2
|
||||||||
Lupul
|
19.6
|
50.1
|
20.1
|
54.6
|
27.3
|
55.9
|
||||||||
Grabovski
|
21.4
|
47.5
|
25.5
|
53.8
|
21.3
|
50.8
|
||||||||
MacArthur
|
18.3
|
44
|
18
|
49.3
|
16.4
|
40.7
|
||||||||
van
Riemsdyk
|
16.6
|
17.1
|
23
|
48
|
27.8
|
55.2
|
||||||||
Bozak
|
14.7
|
37.2
|
12.6
|
36.6
|
15.8
|
42.9
|
||||||||
Connolly
|
11.2
|
30.8
|
12.3
|
33.3
|
16.1
|
53.3
|
||||||||
*Last
Year Numbers include actual games played. VUKOTA numbers are based on predicted
number of games played and Snepts67 and MIRONOV are based on 82 game schedules.
Certainly I
seem to have inflated numbers for Connolly in comparison, but if he returns to
a top six role I think these numbers are plausible. My Bozak, van Riemsdyk, and
Kessel numbers are also well above the other metrics, which I can pretty much
guarantee I’ll either be right on Bozak or right on Connolly, not both. Lupul
and Grabovski are right in the same range, although if the VUKOTA metric was
applied to 82 games for Lupul it would be significantly higher than Snepsts67
or MIRONOV. Only on Clarke MacArthur was there a significantly lower number
than the other two metrics.
My approach
is based on the following assumptions:
- The three most recent seasons in a player’s career provide a more baseline for projections than looking at their entire career.
- Players should generally see their career numbers improve until the age of 27 and decline after that with an accelerated decline by the age of 34.
- A steep downward slope is not accurately captured in this forecast as experienced with the Tim Connolly projections. Based on the historical decline/incline, role of the player (top six/powerplay) and total ice time it’s likely that a player will be closer to the high or low rather than the average.
- Since the previous numbers from some these players like Kadri (OHL), Frattin (NCAA), and Connolly (Buffalo) reflect them getting top six ice time in their previous role their numbers will likely not be achievable in bottom six minutes.
I am aware that when you total the average number of goals for all ten predicted players you‘ll get 223 goals, which we would be happy to have as a team total. When you factor in the range of projections and injury impact it makes the numbers more plausible.
Now that I’ve floated my predictions I’m looking forward to seeing how out to lunch these may be. Any feedback on my first attempt at this would be appreciated.
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