Toews

 

 

 

Last week, Brett Lemon wrote an article regarding Bloom’s simple hierarchical system of categorizing cognitive learning and how it related to the process we all need to go through in order to experience success in fantasy hockey. By surfing TSN.ca, the Dobber website, Yahoo! player blurbs or Rotoworld, you’re being exposed to the “remembering” level. By consistently following DobberHockey and reading the mid-season guide you’re introduced to the “understanding” level. (Still love the “Ma’s Laws” by the way)

 

By making changes to your fantasy squad based on the above information you’re “applying”. As Lemon previously touched on in his article, “analyzing” and “evaluating” is the toughest part of the gig. This week I’m going to make your life a whole lot easier by introducing tools to help you better analyze and evaluate your team.

 

First, let's clear up some general misconceptions about projections:

 

 

1) First of all we need to place a realistic value on overall point production.



Year

Number of point-per-game players

Number of players > than 80 points

2007-08

23

19

2008-09

20

17

2009-10

21

17

2010-11

19

17



  • So, just by looking at the table above, realistically there’s only 20 or so players that will finish at a point-per-game pace, and probably around 17-18 that will tally more than 80 points. If you break it down to a standard 12-team league, that’s roughly one, maybe two, per team. If you’re expecting three or four, you either have one hell of a team or you’re dreaming.

  • What you do need to pay attention to is the group that is a smidge below the mark: Slightly below the point-per-game mark are players like Ryan Kesler, Patrick Kane, Ryan Getzlaf and Nicklas Backstrom who all garner enough responsibility on their respective clubs to get them over the point-per-game hump, but players like Ryane Clowe, Clarke MacArthur, Mikhail Grabovski, Brandon Dubinsky probably won’t get there.

2) Ice-time plays a huge factor in point production.

  • Of the 17 players that hit the 80-point plateau last season, all of them are essentially top-line and top-PP “household” names. As a group they averaged 20:37 overall and 3:59 with the man advantage per contest.
  • So what can you draw from this? Unless players are actually getting “quality” ice-time there’s a big chance that they won’t get the production that you’re after. The tables below will give you a better idea as to what to expect.

3) Team depth is vitally important; a player that’s bouncing around between the second and third line isn’t going to be a candidate for 70 points, or even 60 according to the numbers below.


Player’s rank on respective team

Average # of points tallied

Median

Range

Top

74.3

70

61

2nd

63.7

61

60

3rd

54.8

53

47

4th

46.4

47

34

5th

40.5

38.5

21

6th

35

34

31


  • This is the table from last season, looking at average player scoring position for their respective teams. The leading scorer of each team averaged out to tally around 74 points, 63 for second, 54 for third and so on... Of course you have your variants like Henrik Sedin leading the Canucks with 112 points, contrasted to Patrick Hornqvist at 51 for the Preds, but it still paints a general picture.

4) Salary plays a large role in ice-time distribution, and generally speaking if a team is forking out six million or higher in salary for a player, they are going to receive optimal ice-time. In the salary cap era of the NHL, teams will be very hard-pressed to stick a player making bucket-loads of cash on a checking line (unless maybe you’re the Leafs).

 


5) The Western Conference teams are the slightly more offensive of the two conferences.

  • The Western Conference teams average 2.80 goals per contest, while their Eastern counterparts average 2.71.
  • If you have a comparison between two players in a similar point range, choosing the player in the Western Conference might give you a slight point advantage over the Eastern Conference player.

6) Beware of lofty expectations from rookies.


Year

Number of Rookies > 45 points

Number of Rookies > 50 points

Number of Rookies > 60 points

2007-08

6

4

2

2008-09

7

2

0

2009-10

3

2

0

  • It’s essentially been three years since we’ve seen a 60+ point rookie (Patrick Kane and Nicklas Backstrom), so the “new NHL” is trending towards a lowered rookie production than the days of Sidney Crosby and Alex Ovechkin.
  • With that said, the high-end rookies could still see 55 points, which isn’t all too bad for a debut season. Just don’t expect 70+.

7)    Be wary of the second-half of the “magical fourth year”

() current point pace.

  • Nicklas Backstrom: 69, 88, 101, (74)
  • Claude Giroux: 0, 27, 47, (71)
  • Brandon Dubinsky: 40, 41, 44, (68)
  • Tobias Enstrom: 38, 32, 50, (66)
  • Jonathan Toews: 54, 69, 68, (71)
  • Patrick Kane: 72, 70, 88, (74)
  • Kris Versteeg: 4, 53, 44, (58)
  • Bobby Ryan: 10, 57, 64, (53)
  • Sam Gagner: 49, 41, 41, (54)
  • Derick Brassard: 2, 25, 36, (53)
  • Sergei Kostitsyn: 27, 23, 18, (50)

 

Second half numbers during the “magical fourth year” of recent fourth year breakouts: Mike Richards 1.03, Ryan Getzlaf 1.06, Pavel Datsyuk 1.17, Zach Parise 1.11, Ilya Kovalchuk 1.08, Jeff Carter 0.92, Mike Cammalleri 1.19, Anze Kopitar 1.02, and Paul Stastny 1.02.

 

Now, onto the real mathematical stuff! Keep in mind these are general average numbers, there may be certain exceptions to each scenario.

 

* Last season the top 10 numbers were the stats from all of the top 10 skaters in each category. This season I broke it down into two sections (point-per-game and top 10). I basically used the five players that were closest to the top 10 to determine their values, so if there are large discrepancies between the top 10 of this year and last year, that’s probably why.

 

Centers

Games Played

Goals

Assists

SOG

Ice-time

PP Ice-time

Point-Per-Game

41.7

20.8

32.8

142.2

21:00

4:33

Top 10*

39.6

13.0

25.2

89.0

19:53

3:23

60-point

40.2

15.0

15.0

120.7

18:44

3:12

50-point

40.1

9.7

15.1

90.8

17:39

2:54

35-point

35.4

7.2

8.1

70.3

17:14

1:49

 

* includes Pavel Datsyuk and Ryan Getzlaf, so numbers maybe a bit lower than expected.

 

Last Season

Games Played

Goals

Assists

SOG

Ice-time

Top 10

44.4

17.2

32.5

123.3

20:48

60-point

42.1

11.1

21.9

106.6

19:51

50-point

40.2

10.5

16.1

95.4

18:26

35-point

40.0

7.7

12.6

81.5

17:10

 

 

Left Wing

Games Played

Goals

Assists

SOG

Ice-time

PP Ice-time

Point-Per-Game

42.2

19.2

27.8

153.6

20:03

3:53

Top 10

41.2

14.4

18.0

116.4

17:24

3:14

60-point

41.7

13.2

16.2

105.0

18:14

3:09

50-point

40.0

11.1

12.3

96.9

17:06

2:34

35-point

41.0

8.3

9.3

67.2

14:41

1:42

Last Season

Games Played

Goals

Assists

SOG

Ice-time

Top 10

41.5

20.2

21.9

141.9

19:54

60-point

42.2

15.2

17.8

120.8

18:11

50-point

39.6

12.1

14.6

96.3

17:15

35-point

40.5

16.1

9.5

70.0

14:23

Right Wing

Games Played

Goals

Assists

SOG

Ice-time

PP Ice-time

Point-Per-Game*

44.0

19.5

29.5

147.5

21:22

4:03

Top 10

39.1

16.0

17.8

115.6

17:53

3:10

60-point

39.0

13.6

16.4

126.0

19:50**

3:32

50-point

36.8

9.2

15.3

92.5

17:52

2:59

35-point

36.4

7.4

9.6

77.7

15:35

1:41

 

 

 

 

 

 

 

 

 

 

 

*small sample size only Martin St. Louis and Corey Perry

 


Last Season

Games Played

Goals

Assists

SOG

Ice-time

Top 10

44.2

18.4

24.9

138.6

20:17

60-point

41.7

11.7

18.1

108.3

18:14

50-point

43.7

11.4

14.7

105.1

16:00

35-point

36.8

9.1

9.6

78.6

16:43

 

 

The RW cohort is a very interesting one, as the numbers don’t really fall into line. The “top 10” cohort had guys like Milan Hejduk, Justin Williams, Rick Nash, Dustin Brown and Daniel Briere who are all kind of second line RWers on “good” high scoring teams. The “60-point” cohort had guys like Phil Kessel, Shane Doan and Dustin Penner who were top-line guys but on less offensive teams. As you can see the 19:50 ice-time average is very high compared to the cohort group that averaged just 18:14 at the same time last season. Expect the two groups to actually switch places by season’s end.

 

 

Defenseman

Games Played

Goals

Assists

SOG

Ice-time

PP Ice-time

Top 5

43.8

9.6

27.8

109.2

23:47

4:20

Top 10

38.6

3.6

25

86.6

23:08

4:19

40-point

36

4.2

15.8

60.6

22:55

3:16

30-point

36.2

3.2

11.8

59.3

22:45

2:47

25-point

39.1

2.9

9.6

57.3

21:13

1:58

Last Season

Games Played

Goals

Assists

SOG

Ice-time

Top 10

44.5

6.7

25.4

91.5

24:36

40-point

43.5

5.9

16.1

75.8

22:05

30-point

40.1

3.3

12.7

62.8

21:18

25-point

39.2

3.2

10.8

50.0

20:45

 

 

Goalies

I didn’t know how to tier the goalies without getting 50 billion complaints about how I did it, so I guess I’ll just state some quick points.

 

  • Only three goalies (Jimmy Howard, Carey Price and Jonas Hiller) are on pace to finish with 40 or more wins compared to five last season.
  • Seven more are on pace for 35 wins or more.
  • Only two goalies are on pace for 70+ starts.
  • Fantasy Impact: more teams are using split-time/tandem situations than a number one/number two option. For example, the once sure fire 70-start goalie Roberto Luongo is only on pace to start 62 contests this season.
  • The top 50 fantasy-worthy goalies have averaged 24.2 starts and 12.0 wins.
  • Of those 50, only eight have a win percentage of greater than 60 percent with a minimum of 30 games played.

Goalies

Starts

Win Percentage

GAA

Save Percentage

Top 5

33.6

60.7

2.37

0.921

15 win

28.8

51.7

2.52

0.916

10 win

19.5

51.3

2.89

0.902

5 win

14.6

35.1

3.08

0.896

 

 

 

Last Season

Starts

Win Percentage

GAA

Save Percentage

Top 5

N/A

64.0

2.18

0.926

20 win

36.8

54.0

2.46

0.912

15 win*

29.3

52.3

2.51

0.919

10 win

21.8

45.8

2.36

0.910

 

* small sample size.

 

So you’re probably sitting there wondering, there’s a whole bunch of numbers, what does it all mean? Here are three examples of how I would use the numbers to determine the second-half.

 

For the first example I’m going to examine someone who I think is overachieving. Loui Eriksson has 46 points in 43 contests, while averaging 20:42 overall and 3:21 on the PP for the Stars. He also has 97 SOG and is currently ranked eighth in overall scoring in the NHL. Eriksson has spent 83.16 percent of his overall shifts alongside Brad Richards (another point-per-game player), which does show that he’s an integral part of the Stars offense. His ice-time seems to fall in line with his peers, but it’s the SOG that’s nearly 20 off the pace. Is he a certified top 10 LWer? His numbers certainly prove so, but is he good enough for point-per-game? I don’t think so, so look for a dip in numbers during the second-half to align itself.

 

 

Let’s try a second example to make sure that we’re all on the same page. Alex Kovalev is averaging 16:06 per contest amongst and has recorded just 18 points on the season. His power-play ice-time isn’t all too flashy at just 2:09 per game and he’s also picked up 98 SOG. The numbers project him to be more of a 45-point scorer by season’s end, which makes him borderline ww material. If you are a Kovalev-owner and is expecting a turnaround, it might be a smart play to move on and look for other alternatives.

 

 

For a third example we can take a backwards approach. Let’s take a look at Sergei Kostitsyn’s stats, so far he’s played in 37 contests, while averaging 13:25 per contest and has tallied 23 points. If you break it down a bit further, in the last month he has boosted that average to 17:36 and 2:25 on the PP in 14 contests, so really it was the start-of-year stats that are dragging his overall totals down. If you use his current numbers, he’s more of a 55-60 point player than a 46-point pace that’s he’s currently on. So expecting somewhere around 30-35 points in the final 41 contest is fairly reasonable if he maintains this recent trend. At just 13 percent Yahoo!-owned, he’s a bargain bin pickup to help provide an offensive boost to your fantasy squad.

 

 

So hopefully you can use the guidelines and numbers above to help you gain a better grasp of what to expect for player X moving forward. As the last tier of Bloom’s simple hierarchical system of categorizing cognitive learning, you need to go out and “create”. Now use this information and create changes to win a fantasy championship. Of course if you are desperately seeking different opinions, hop onto the DobberHockey Forums where there are plenty of fantasy fanatics who are ready and willing to give you their opinions. Questions or comments? Like always I’ll be ready and willing to discuss them with you in the comments section below.

 

 


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Comments (12)add comment

Ryan Ma said:

Maaaasquito
... Sorry post got cut off

A more realistic approach is to look at SOG, as I think that has more of a direct correlation to production than SH%....

If you analyze the top 50 scorers, you only have Henrik, JT, Lidstrom, and Enstrom who are under the 200 SOG pace... and even then there's probably good reasons for all 4 of them.

If you look at it as well, someone who has 1 goal from 10 SOG, has the same shooting percentage as a player who has 10 G from 100 SOG. But I'd much rather own the 10G shooter. To me SOG is a much better indicator for point production than SH%.
January 14, 2011
Votes: +0

Ryan Ma said:

Maaaasquito
... RE: Shooting g;e

The thing for me is that shooting g;e has a random element to it which is similar to babip. SOG is much like AB (sorry been awhile since I've followed baseball), but I think it fits. The more AB a hitter gets, well there's more chance that he gets a hit. Same kind of deal with SOG, more SOG a shooter gets, well more chance that he'll get a point or a goal.

SH% is more random, I mean 3 more goals with Malkin's current shots and he'd be at the same pace that he was shooting at last season... Stats are arbitrary. Just because he has a career 12.7 sh% doesn't mean that the numbers are always going to be there. If you look at the trends for all players they all vary probably within 5% careerwise. So there's a lot of peaks and valleys especially with SH%...
January 14, 2011
Votes: +0

gregory churchill said:

spitball
malkin/ ov, second halfs prior to this season, malkin scored on approx 13.3% of his shots on goal.
ov scored on approx 12.4% of his shots.

this season, after 173 shots, malkin has 15 goals, scoring on only 8.7%, and ov, 15 goals on 202 shots, only 7.4%.

in the second half, if malkin takes another 170 shots, would we assume he continues at a rate of his career low 8.7, another 15 goals, or improve to his career avarage of 13.3, which would result in 23 goals, or improve to the point where his season average matches his career average...and finish the season with 45 goals, so another 30 in the second half.

and ov, if he takes another 200 shots in the second half, would we expect 15 goals, 25 goals, or 35 goals?

is shots on goal a randon determinate, something akin to baseball's babip, or can we make confident predictions of expected production based on large small-sample fluxuation.

thoughts?
January 13, 2011
Votes: +0

Ryan Ma said:

Maaaasquito
... Weekly Approach

I'll tell you a secret, if you go into FrozenPool on the side, and go under report generator, then filter through whatever stat you want, for this article I used Ice Time, PP Ice time and SOG, you can sort it by week, 2 weeks, 3 weeks and last month... so you can catch the trends before it even hits the Dobber front pages...

My pick this week Mike Santorelli, keep an eye on him, top unit PP with Weiss, Booth and Stillman in FLA.
January 12, 2011
Votes: +0

mike hess said:

SharkMeat
great stats approach Great article....While this static approach to applying the stats is great...what you would really like to catch is those movements where Sergio goes from 13 mintues to 17 mintues, on the cusp before everone else recognizes it. We need a stat measure of velocity on a weekly basis to easily find the ones we need to analyze and put throught your approach above...If I wait for you or Dobber to point it out it is too late especially in deep leagues..smilies/grin.gif
January 12, 2011
Votes: +0

Ryan Ma said:

Maaaasquito
... RE: Giroux

I dunno kinda hard to tell with him just cause they're so deep.

The thing with Philly is that they're up there with the goals for, so there's a bit more room for leeway in terms of production.

Also things are starting to level out too, where the offense is spread out across the three lines. So its not like a top-6/bottom-6 setup like most teams run...

The thing is the numbers are there for him to succeed.

I think he's very similar to Sharp, kinda buried behind a few "big names" but will still produce at a decent pace, probably not good enough for point per game, but settling around the 70-75 point mark by the end of the season.
January 12, 2011
Votes: +0

Tony Bendiktsen said:

ynotzz
Giroux You agree on my Giroux analysis then?
January 11, 2011
Votes: +0

Ryan Ma said:

Maaaasquito
... RE: Tony

Perfect that's the way I'd go about it.

With Ryan I think it can go either way, the thing is Getzlaf is out, so he's gonna get that "go to" time, so I could see around a point-per-game pace. But once Getzlaf comes back he becomes that third/fourth (Selanne) option, where it'll drop his production. I'd probably expect around a 70-75 point pace so around 30-35 in the remaining 40 games...

Similar situation with Pavelski. He has good skills but buried by depth. The PP time will help boost some of the totals. So expect somewhere around 55-60 by season's end so around 30-35 in the final 40...

I'm differing on Stewart, but I kinda of have a Avs bias... The thing is Stastny is a great 2nd half guy. The two had awesome chemistry at the end of last season and even the start of this season before he was injured. If they unite Stastny, Stewart and Hejduk and keep Duchene, Flash and Jones together that could be a very dangerous team... I'd keep him cause I think he could be on a blistering pace down the stretch...

But perfect for the way you've used the stats. That's how I would use them. smilies/wink.gif
January 11, 2011
Votes: +0

Tony Bendiktsen said:

ynotzz
My team so far So if I apply this to my team: (Would appreciate some feedback on how I'm using this.)

M Richards: 41 GP - (13,24-37), 98SOG, ATOI 19:15, PPTOI 3:19
fits with: Centers top 10
Conclusion: Expected pace so far

Savard: difficult because of injury etc, but rough estimates based on play so far and lately, stats weighed up to 41 games:
GP 41, (6,12-1smilies/cool.gif, 90 SOG, ATOI: 18 PPTOI 3:00
Fits best with the 60-point players.
Conclusion: Slight increase in production but no PPG numbers.

Ryan: GP 45, (18,13-31), 163 SOG, ATOI 20:44, PPTOI 2:45
Conflicting stats. SOG, ATOI fits with PPG, PPTOI is too low. Goals fit, Assists way behind. Only 4 PPP so far.
Conclusion: Should have a much better second half and increase in production, especially on the PP.

Marleau: GP 43, (15,15-30), 145 SOG, ATOI 20:41, PPTOI 3:46
SOG, ATOI, PPTOI fits the PPG player.
Conclusion: Expecting BIG increase in production.

Sharp: GP 43, (25,19-44), 177 SOG, ATOI 19:33, PPTOI 3:38
Numbers fit PPG wingers/centers.
Conclusion: Will continue at about the same pace, but maybe more assist heavy.

Cammalleri: GP 41, (12,16-2smilies/cool.gif, 118 SOG, ATOI 18:29, PPTOI 3:16
SOG, ATOI and PPOI fits the top 10 wingers, but prouction is the 60 point pace.
Conclusion: Slight increase in production.

Pavelski: GP 36, (9,18-27), SOG 136, ATOI 19:13, PPTOI 3:43
SOG, ATOI and PPTOI puts him somewhere in the PPG/TOP10 area. Production might be lowered because of so much ice time on the 2nd/3rd line.
Conclusion: Expecting slight increase in production.

Giroux: GP 41, (16,20-36), SOG 82, ATOI 18:46, PPTOI 3:16
Conflicting stats. ATOI, PPTOI and his production indicates a top 10 player, but the SOG doesnt match.
Conclusion: Expecting a similar/slightly lower pace, but more assist heavy.

Havlat: GP 42, (10,28-3smilies/cool.gif, SOG 126, ATOI 18:24, PPTOI: 2:56
Stats fit the top10 RW pretty well.
Conclusion: Expecting a similar pace, but slightly more goal heavy.

Iginla: GP 42, (17,20-37), SOG 155, ATOI 20:35, PPTOI 3:59
SOG, ATOI, PPTOI fits the PPG players.
Conclusion: Iginla started off slow but has turned it around. Will have a solid 2nd half with a slight increase in production.

Stewart: GP 23, (11,14-25), SOG 66, ATOI 17:08, PPTOI: 3:05
SOG, ATOI, PPTOI fits the 50-60 point player better than the PPG he kept up to his injury.
Conclusion: Wont keep up the PPG pace. Sell high if he gets a good start?

I might do my defenders and goalies later as well. Does this make sense? Am I using the numbers correctly?
January 11, 2011
Votes: +0

Ryan Ma said:

Maaaasquito
... RE: Numbers

Yeah I can't do all the work for you... I just churned out all the numbers so you guys can analyze them yourselves... It wouldn't be hard really. Just pick any player, say Umberger, 108 SOG, 18:30 and 3:19 the numbers fall in line for a 55-60 point season, which is pretty much on pace for what he's doing right now... So if you are happy with that production then stay status quo, but if you were expecting say 75, he won't get there based on the numbers above.

Eriksson... I think he's close, but I think he's just gonna fall short. Generally you need someone to really get pucks to the net in order to put up the points... Unless you're a great passer like Thornton or Sedin... I dunno we'll know at the end of the season.

Those questions are deep, and if you can find the answers to that you'd be winning all of your pools. Might be a great forum question to see if you can get some help answering those...
January 11, 2011
Votes: +0

derek said:

buck0198
Crazy You guys here are amazing but at the same time to put an article like this together you must be certifiably nuts...congrats...great piece.
January 11, 2011
Votes: -1

Pengwin7 said:

Pengwin7
A for Effort Excellent collection of statistics here! Super duper awesome!
I'm a numbers geek myself and love to see this stuff.

Unfortunately, a lot of these numbers are gathered & presented without actually going anywhere or presenting a trend that can help pick out an overachiever compared to an underachiever.

Example #1: Loui Eriksson. IMO, all signs point to him being able to maintain the ppg pace. Eriksson tallied 59 even-strength points last year and only 12 on the PP. This year he's maintained his even-strength pace (30pts) but his contributions on the PP are much higher (16pts)... in-line where a #1PP player should be. His SOG/game is actually below last year - so if that increases back to his average, numbers could even increase.

Example #3. S-Kot. Now this is perfect, and a few more of these would have made the article as valuable as its potential. Players who are trending towards 2nd half increases.

Topics I would have liked to see covered:
1. Players who are outshooting (Stamkos 20%, Lidstrom 12%) or undershooting (Ovechkin 7.7%!!!) their average shooting percentage and will likely see a correction in their point totals.
2. How rookies perform in their 2nd halfs vs. 1st halfs. Do they wear out? Or do they adjust to the speed of the NHL and increase scoring?
3. Scheduling impacts. It was noted that the Western Conference has been scoring more. Is this the good Western conf players, or weak Eastern goalies... or something else. Teams usual play their cross-conference games (East vs. West) in the first half of the season. In the 2nd half of the season, the West will play more of the West and the East will play more of the East. So what would we expect to see when the conferences settle into their rivalries? Which division's players will prosper or fail?

Overall, great write-up. smilies/wink.gif
January 11, 2011
Votes: +2
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