Frozen Tools Forensics: The Plan to Play
Chris Kane
2020-05-29
So the NHL has a plan. A plan to play. And a plan for the playoffs. If you want some perspective on what it might mean for your fantasy league, make sure to check out Ian's article here. For me it got me started thinking about playoff hockey again. It looks like we are having a 24-team playoff pool, which certainly broadens our pool of prospective players if we are thinking about fantasy. This week I wanted to take a look back at playoff performers and see what sorts of information is easily accessible through Frozen Tools.
In short, the answer to the above wondering is: a lot. Each and every report that you can run in the Reports section can be run based on regular season performance and playoff performance. That makes it easy to pull data and analyze playoff specific information. For example, our basic Big Board Report for 2018-19 playoffs looks like this.
Name | GP | G | A | PTS | PTS/GP | Pace | PPP | PPTOI | %PP | TOI | SOG |
BRAD MARCHAND | 24 | 9 | 14 | 23 | 0.96 | 79 | 12 | 3:30 | 73.7 | 20:15 | 72 |
RYAN O’REILLY | 26 | 8 | 15 | 23 | 0.88 | 73 | 5 | 3:00 | 60.1 | 21:00 | 54 |
JADEN SCHWARTZ | 26 | 12 | 8 | 20 | 0.77 | 63 | 2 | 2:03 | 41 | 17:28 | 63 |
LOGAN COUTURE | 20 | 14 | 6 | 20 | 1 | 82 | 5 | 3:20 | 61.5 | 20:09 | 67 |
DAVID PASTRNAK | 24 | 9 | 10 | 19 | 0.79 | 65 | 9 | 3:03 | 64.3 | 17:34 | 82 |
ALEX PIETRANGELO | 26 | 3 | 16 | 19 | 0.73 | 60 | 4 | 2:44 | 54.8 | 25:45 | 77 |
TOREY KRUG | 24 | 2 | 16 | 18 | 0.75 | 62 | 12 | 3:29 | 73.3 | 22:21 | 55 |
VLADIMIR TARASENKO | 26 | 11 | 6 | 17 | 0.65 | 54 | 7 | 3:09 | 63 | 18:19 | 90 |
PATRICE BERGERON | 24 | 9 | 8 | 17 | 0.71 | 58 | 10 | 3:15 | 68.3 | 18:42 | 87 |
BRENT BURNS | 20 | 5 | 11 | 16 | 0.8 | 66 | 5 | 2:56 | 54.3 | 28:25 | 57 |
I have cut out a couple of columns (sorry contract info) to give the basic idea here. For the purposes of this article we are mostly going to be focusing on points and points per game. Here we see the table sorted by total points and it is no surprise that eight of the top ten-point producers are from Boston or St. Louis. They had the most games played, so they should end up with the most points.
If we sort by points per game (and filter for at least five games played) we get a very different list.
Name | Pos | Age | Team | GP | G | A | PTS | PTS/GP |
MARK STONE | R | 28 | VGK | 7 | 6 | 6 | 12 | 1.71 |
MAX PACIORETTY | L | 31 | VGK | 7 | 5 | 6 | 11 | 1.57 |
DUSTIN BYFUGLIEN | D | 35 | WPG | 6 | 2 | 6 | 8 | 1.33 |
ALEX OVECHKIN | L | 34 | WSH | 7 | 4 | 5 | 9 | 1.29 |
MIKKO RANTANEN | R | 23 | COL | 12 | 6 | 8 | 14 | 1.17 |
NICKLAS BACKSTROM | C | 32 | WSH | 7 | 5 | 3 | 8 | 1.14 |
PAUL STASTNY | C | 34 | VGK | 7 | 2 | 6 | 8 | 1.14 |
SHEA THEODORE | D | 24 | VGK | 7 | 1 | 7 | 8 | 1.14 |
JORDAN EBERLE | R | 30 | NYI | 8 | 4 | 5 | 9 | 1.13 |
ARTEMI PANARIN | L | 28 | NYR | 10 | 5 | 6 | 11 | 1.1 |
This illustrates two things: 1) How streaky playoff performances can be (Mark Stone and Max Pacioretty were amazing owns during their run), and 2) At the end of the day, picking playoff players is really all about picking playoff teams.
I want to expand on the first notion here a little bit. We cannot predict what a player will do over any seven-game sample, but we can take a look at players who historically perform well in the playoffs and see if there are any conclusions to draw. To start I pulled the same big board data for the last three playoffs.
Name | Pos | Age | Team | GP | G | A | PTS | PTS/GP |
SIDNEY CROSBY | C | 32 | PIT | 64 | 23 | 45 | 68 | 1.06 |
LOGAN COUTURE | C | 31 | S.J | 60 | 30 | 35 | 65 | 1.08 |
EVGENI MALKIN | C | 33 | PIT | 61 | 21 | 36 | 57 | 0.93 |
PHIL KESSEL | R | 32 | ARI | 65 | 20 | 36 | 56 | 0.86 |
ALEX OVECHKIN | L | 34 | WSH | 56 | 29 | 27 | 56 | 1 |
NICKLAS BACKSTROM | C | 32 | WSH | 52 | 18 | 37 | 55 | 1.06 |
EVGENY KUZNETSOV | C | 28 | WSH | 56 | 19 | 31 | 50 | 0.89 |
BRENT BURNS | D | 35 | S.J | 60 | 15 | 35 | 50 | 0.83 |
T.J. OSHIE | R | 33 | WSH | 53 | 19 | 26 | 45 | 0.85 |
JOE PAVELSKI | C | 35 | DAL | 53 | 22 | 22 | 44 | 0.83 |
Logan Couture and T.J. Oshie are names that stand out on this list as players who usually don't put up the kinds of regular season numbers as the others here, but otherwise we see a lot of top scorers on good teams who have played a lot of playoff games.
If we sort by points per game instead, we get a bit more variation in the list, but again the stand out here is Logan Couture.
Name | Pos | Age | Team | GP | G | A | PTS | PTS/GP |
LEON DRAISAITL | C | 24 | EDM | 13 | 6 | 10 | 16 | 1.23 |
BLAKE WHEELER | R | 33 | WPG | 23 | 4 | 22 | 26 | 1.13 |
REILLY SMITH | R | 29 | VGK | 33 | 10 | 26 | 36 | 1.09 |
MARK SCHEIFELE | C | 27 | WPG | 23 | 16 | 9 | 25 | 1.09 |
LOGAN COUTURE | C | 31 | S.J | 60 | 30 | 35 | 65 | 1.08 |
SIDNEY CROSBY | C | 32 | PIT | 64 | 23 | 45 | 68 | 1.06 |
NICKLAS BACKSTROM | C | 32 | WSH | 52 | 18 | 37 | 55 | 1.06 |
NATHAN MACKINNON | C | 24 | COL | 18 | 9 | 10 | 19 | 1.06 |
BRAD MARCHAND | L | 32 | BOS | 42 | 14 | 30 | 44 | 1.05 |
JAKE GUENTZEL | L | 25 | PIT | 41 | 24 | 19 | 43 | 1.05 |
Couture is essentially even with Sidney Crosby. Crosby has averaged 94 points over his last five seasons, with Couture averaging 63. There is a 30-point gap in their average performances during the regular season, but yet Couture is matching him stride for stride in the playoffs (smaller sample? Sure, but a 60-game sample is pretty reasonable.)
Couture's playoff performance clearly exceeds his regular season performance, and while he isn't alone in this phenomenon, he does occupy some pretty distinguished company. The following table is the Playoff Big Board from above, but with the additions of the regular season point-per-game numbers, and a column comparing the playoff point-per-game numbers and the playoff point-per-game numbers. It is then sorted by change in points per game.
Name | Pos | Age | Team | GP | G | A | PTS | PTS/GP | Season PTS/GP | Change PTS/G |
JASON SPEZZA | C | 36 | TOR | 24 | 8 | 10 | 18 | 0.75 | 0.37 | 0.38 |
DUSTIN BYFUGLIEN | D | 35 | WPG | 23 | 7 | 17 | 24 | 1.04 | 0.68 | 0.36 |
TROY BROUWER | R | 34 | STL | 24 | 8 | 7 | 15 | 0.63 | 0.27 | 0.36 |
JOEL WARD | R | 39 | S.J | 30 | 8 | 9 | 17 | 0.57 | 0.23 | 0.34 |
JORI LEHTERA | C | 32 | PHI | 34 | 4 | 11 | 15 | 0.44 | 0.12 | 0.32 |
REILLY SMITH | R | 29 | VGK | 33 | 10 | 26 | 36 | 1.09 | 0.79 | 0.3 |
RYAN KESLER | C | 35 | ANA | 28 | 5 | 9 | 14 | 0.5 | 0.21 | 0.29 |
LOGAN COUTURE | C | 31 | S.J | 60 | 30 | 35 | 65 | 1.08 | 0.81 | 0.27 |
JAKE GUENTZEL | L | 25 | PIT | 41 | 24 | 19 | 43 | 1.05 | 0.82 | 0.23 |
JOAKIM NORDSTROM | C | 28 | BOS | 23 | 3 | 5 | 8 | 0.35 | 0.13 | 0.22 |
Oh man, Jason Spezza. He has been doubling his rate of production in the playoffs lately. There are a couple of familiar names on this list. We have already seen Couture and Reilly Smith, and Jake Guentzel was featured in the most recent points-per-game sorting. The thing that really stands out about Couture, though, is his 60 games played. Guentzel's 41 games is the next highest on this list top 10 list, and the average number of games played for the top 10 is 32. In fact we have to drop all the way down to 28th on this list to reach another player who played 60-plus games.
So what gives with these guys? Why are they performing better in the playoffs? One answer comes from a usual culprit: opportunity.
Name | GP | PTS | PTS/GP | Season PTS/GP | Change PTS/G | Playoff TOI | Season TOI | Playoff %PP | Season %PP |
JASON SPEZZA | 24 | 18 | 0.75 | 0.37 | 0.38 | 14:08 | 12:30 | 57.7 | 41.1 |
DUSTIN BYFUGLIEN | 23 | 24 | 1.04 | 0.68 | 0.36 | 26:17 | 24:21 | 67.1 | 60.6 |
TROY BROUWER | 24 | 15 | 0.63 | 0.27 | 0.36 | 18:10 | 13:01 | 50.3 | 13.4 |
JOEL WARD | 30 | 17 | 0.57 | 0.23 | 0.34 | 15:38 | 11:52 | 30.4 | 4.6 |
JORI LEHTERA | 34 | 15 | 0.44 | 0.12 | 0.32 | 13:58 | 9:58 | 10.8 | 7.8 |
REILLY SMITH | 33 | 36 | 1.09 | 0.79 | 0.3 | 20:56 | 18:02 | 46.9 | 47.3 |
RYAN KESLER | 28 | 14 | 0.5 | 0.21 | 0.29 | 20:38 | 17:09 | 52.1 | 35.8 |
LOGAN COUTURE | 60 | 65 | 1.08 | 0.81 | 0.27 | 19:39 | 18:34 | 63.3 | 60.7 |
JAKE GUENTZEL | 41 | 43 | 1.05 | 0.82 | 0.23 | 18:00 | 18:26 | 41.5 | 45.6 |
JOAKIM NORDSTROM | 23 | 8 | 0.35 | 0.13 | 0.22 | 13:43 | 12:00 | 0.7 | 1.4 |
I added a couple of columns for comparison here. We have Playoff Time On Ice next to Season Time On Ice, and Playoff Percent Power Play next to Season Percent Power Play. With only one exception (Jake Guentzel), everyone on this list sees an increase in total time on ice during the playoffs, and an increase in the percentage of time they spend on the power play. In some cases, these changes are massive. Troy Brouwer, for instance has averaged more than five extra minutes of time on ice in the playoffs and an almost 37 percent larger share of his team's power-play time. Couture's minute of total time and 2.5 percent share of power-play time seem small potatoes by comparison, but it appears that it is enough for him to produce at elite levels come the postseason.
There is much more to dig in here as always, but we will leave it for now.
That is all for this week. Thanks for reading.
Stay safe out there.
Want more tool talk? Check out these recent Frozen Tool Forensics Posts.