Apr 052013
 

Yesterday HabsEyesOnThePrize.com had a post on the importance of fenwick come playoff time over the past 5 seasons. It is definitely worth a look so go check it out. In the post they look at FF% in 5v5close situations and see how well it translates into post season success. I wanted to take this a step further and take a look at PDO and GF% in 5v5close situations to see of they translate into post season success as well.  Here is what I found:

Group N Avg Playoff Avg Cup Winners Lost Cup Finals Lost Third Round Lost Second Round Lost First Round Missed Playoffs
GF% > 55 19 2.68 2.83 5 1 2 6 4 1
GF% 50-55 59 1.22 1.64 0 2 6 10 26 15
GF% 45-50 52 0.62 1.78 0 2 2 4 10 34
GF% <45 20 0.00 - 0 0 0 0 0 20
FF% > 53 23 2.35 2.35 3 2 4 5 9 0
FF% 50-53 55 1.15 1.70 2 2 1 10 22 18
FF% 47-50 46 0.52 1.85 0 0 4 3 6 33
FF% <47 26 0.54 2.00 0 1 1 2 3 19
PDO >1010 27 1.63 2.20 2 2 2 6 8 7
PDO 1000-1010 42 1.17 1.75 1 0 5 7 15 14
PDO 990-1000 47 0.91 1.95 2 1 3 4 12 25
PDO <990 34 0.56 1.90 0 2 0 3 5 24

I have grouped GF%, FF% and PDO into four categories each, the very good, the good, the mediocre and the bad and I have looked at how many teams made it to each round of the playoffs from each group. If we say that winning the cup is worth 5 points, getting to the finals is worth 4, getting to the 3rd round is worth 3, getting to the second round is worth 2, and making the playoffs is worth 1, then the Avg column is the average point total for the teams in that grouping.  The Playoff Avg is the average point total for teams that made the playoffs.

As HabsEyesOnThePrize.com found, 5v5close FF% is definitely an important factor in making the playoffs and enjoying success in the playoffs. That said, GF% seems to be slightly more significant. All 5 Stanley Cup winners came from the GF%>55 group while only 3 cup winners came from the FF%>53 group and both Avg and PlayoffAvg are higher in the GF%>55 group than the FF%>53 group. PDO only seems marginally important, though teams that have a very good PDO do have a slightly better chance to go deeper into the playoffs. Generally speaking though, if you are trying to predict a Stanley Cup winner, looking at 5v5close GF% is probably a better metric than looking at 5v5close FF% and certainly better than PDO. Now, considering this is a significantly shorter season than usual, this may not be the case as luck may be a bit more of a factor in GF% than usual but historically this has been the case.

So, who should we look at for playoff success this season?  Well, there are currently 9 teams with a 5v5close GF% > 55.  Those are Anaheim, Boston, Pittsburgh, Los Angeles, Montreal, Chicago, San Jose, Toronto and Vancouver. No other teams are above 52.3% so that is a list unlikely to get any new additions to it before seasons end though some could certainly fall out of the above 55% list. Now if we also only consider teams that have a 5v5close FF% >50% then Toronto and Anaheim drop off the list leaving you with Boston, Pittsburgh, Los Angeles, Montreal, Chicago, San Jose and Vancouver as your Stanley Cup favourites, but we all pretty much knew that already didn’t we?

 

Apr 152011
 

Before I get into the main subject of this post let me first point out that I have updated stats.hockeyanalysis.com to include all 1, 2, 3 and 4 year player ratings that can be calculated using the last 4 years of NHL data.  For more information on my player ratings read this.

I generate offense, defense and overall ratings for each and every player in the NHL and I wanted to get an idea of how much each position contributes to the performance of the team.  To accomplish this I multiplied each players offensive and defensive ratings (HARO+, HARD+) by their ice time (5v5 ratings and ice time used) and summed them up by position and then compared the positions total to the overall total.  I did this using the ratings calculated for the past 4 seasons combined as well as for each of the past 4 individual seasons.  This is the result I came up with :

Offense:

Season(s) Center RW LW D D
2007-11 24.64% 18.04% 17.14% 20.09% 20.09%
2007-08 26.91% 16.22% 16.47% 20.20% 20.20%
2008-09 25.23% 18.01% 16.66% 20.05% 20.05%
2009-10 23.93% 18.47% 17.49% 20.06% 20.06%
2010-11 25.13% 18.02% 16.76% 20.04% 20.04%

Defense:

Season(s) Center RW LW D D G
2007-11 20.67% 15.08% 14.27% 16.72% 16.72% 16.55%
2007-08 22.46% 13.83% 13.81% 16.75% 16.75% 16.39%
2008-09 21.06% 15.49% 13.76% 16.67% 16.67% 16.35%
2009-10 19.98% 15.46% 14.79% 16.73% 16.73% 16.30%
2010-11 21.35% 15.08% 14.21% 16.51% 16.51% 16.35%

Average of Offense + Defense:

Season(s) Center RW LW D D G
2007-11 22.65% 16.56% 15.71% 18.40% 18.40% 8.28%
2007-08 24.69% 15.03% 15.14% 18.48% 18.48% 8.19%
2008-09 23.14% 16.75% 15.21% 18.36% 18.36% 8.17%
2009-10 21.95% 16.96% 16.14% 18.39% 18.39% 8.15%
2010-11 23.24% 16.55% 15.48% 18.27% 18.27% 8.17%

Note:  I split the defense contribution over 2 positions.

Now, the first thing I noticed with these numbers is how surprisingly consistent they are from season to season, especially for defense and goaltending.  Up front players frequently shift from center to wing and from left wing to right wing so that may account for some of the (still relatively small) seasonal fluctuations.  Maybe I shouldn’t be surprised at this consistency but it does give me some confidence in my rating system that it is consistent across seasons as well as with multiple season ratings.

The second thing that caught my attention was the importance of defensive contribution to the offense.  Approximately 40% of offensive production can be attributed to the two defensemen on the ice and the defensemen are more important than the wingers. Part of this is simply that defensemen get more ice time than forwards since there are only 3 defense pairs versus 4 forward lines.  The other part is probably that they play an integral part of collecting rebounds and transitioning the team from defense to offense so they may have greater influence in the percentage of time played in the offensive zone.

Of the three forward positions, the center position is clearly the most important but we probably figured that.  Face offs might be a contributing factor but also we might just find that the most talented players end up playing center.  Right wings are slightly more important than left wings but the difference is not substantial.

Next I wondered what this data would mean to what teams should allocate for salaries.  For a 60 million payroll the average salary for position should work out to the following:

Pos Salary (Million$)
Center 13.6
RW 9.9
LW 9.4
D 11.0
D 11.0
G 5.0

Of course elite players skew the team payroll structure a fair bit.  As a LW earning over $9.5M Alexander Ovechkin is eating up the entire Capitals allotment for LWs and Crosby, Malkin and Staal are way over budget for the Penguins but you have to work around the talent you have.  A couple months ago Behind the Net Hockey Blog had a post outlining the salary allocated to players by position (split between forwards, defense, and goaltending).  Forwards were allocated 59.1% of a teams payroll, defense 32.2% and goaltending 8.7% over the past 4 seasons which compares to 54.9%, 36.8% and 8.3% for my ratings.  That would mean that forwards are overpaid (relative to their contribution) by about 4.1%, defense under paid by 4.6% and goalies over paid by about 0.4%.

For interest sake I decided to take a look at the Vancouver Canucks performance distribution since they have a fairly well balanced team and are a serious cup contender.  Here is what I found:

2007-11 2010-11
Position Offense Defense Average Offense Defense Average
Center 23.44% 19.96% 21.70% 21.04% 17.15% 19.10%
RW 11.44% 9.88% 10.66% 9.97% 10.34% 10.15%
LW 25.14% 21.88% 23.51% 31.12% 25.11% 28.11%
D 19.99% 17.21% 18.60% 18.94% 15.92% 17.43%
D 19.99% 17.21% 18.60% 18.94% 15.92% 17.43%
G 0.00% 13.86% 6.93% 0.00% 15.55% 7.77%

(Note:  The above is calculated using the current roster using the ratings and ice time over the past season or four seasons regardless of whether that ice time was with the Canucks.  This is an evaluation of the team ending the 2010-11 season with the Canucks, not the Canucks team performance over past seasons.  Also four season ratings should give a better player evaluation than single season ratings due to the larger sample size so I would consider them closer to true value.)

The Canucks are definitely a team driven by a group of quality left wingers or at least players listed as playing LW such as D. Sedin, Burrows, Raymond, Torres but I suspect some get shifted to RW from time to time.  Also, as good as Luongo is the quality and depth of the team in front of him reduces his relative contribution to his team to below average levels.  In the future I’ll take a look at some other teams as it’ll be interesting to see how goalie contribution changes from good teams with subpar  goalies (Detroit maybe) to bad teams with good goalies (Florida – Vokoun!! Though my ratings don’t value him as highly as many others do).

Dec 112007
 

The big movers up this week are Colorado (16th to 11th) and Calgary (21st to 13th) while the biggest drop has been Buffalo who fell from 13th to 23.

Rank 7 Days
Ago
Team AdjWinP SchedStr Power
Rank
1 2 Detroit 0.717 0.521 0.721
2 3 Ottawa 0.679 0.478 0.643
3 1 St. Louis 0.574 0.534 0.612
4 6 Vancouver 0.583 0.514 0.590
5 5 Minnesota 0.586 0.518 0.587
6 4 Chicago 0.483 0.559 0.571
7 8 Columbus 0.500 0.545 0.564
8 11 San Jose 0.589 0.496 0.563
9 7 Nashville 0.500 0.542 0.562
10 10 Montreal 0.500 0.502 0.531
11 16 Colorado 0.517 0.520 0.525
12 9 Boston 0.552 0.479 0.521
13 21 Calgary 0.433 0.555 0.506
14 15 Philadelphia 0.558 0.464 0.505
15 14 NY Rangers 0.517 0.472 0.498
16 20 Toronto 0.468 0.507 0.493
17 19 Dallas 0.500 0.497 0.486
18 12 NY Islanders 0.500 0.470 0.478
19 17 New Jersey 0.533 0.453 0.474
20 18 Carolina 0.550 0.450 0.473
21 22 Pittsburgh 0.466 0.490 0.464
22 23 Phoenix 0.446 0.515 0.464
23 13 Buffalo 0.464 0.489 0.462
24 24 Anaheim 0.453 0.506 0.438
25 26 Los Angeles 0.400 0.517 0.419
26 25 Atlanta 0.448 0.452 0.394
27 28 Tampa Bay 0.417 0.462 0.385
28 29 Florida 0.397 0.463 0.370
29 27 Edmonton 0.323 0.541 0.363
30 30 Washington 0.383 0.455 0.341

AdjWinP is a teams winning percentage when shootouts are considered ties and there are no points awarded for overtime losses
SchedStr is an indication of a teams relative difficulty of schedule
Power Rank is the teams expected winning percentage if team played all .500 teams