Toronto Maple Leafs

How Toronto plays, trait by trait, against the rest of the league.

Real numbers through Oct 10: 5 games this season, blended with last season. Traits that take longer to settle lean more on last season.

Real numbers through Oct 10, from this season’s 5 games only. Expect these to swing a lot this early, which is why the likely ranges are so wide.

Offense

• Shot volume
  SelectiveRelentless

  54.4 shot attempts per 6022nd of 32

  56.8 shot attempts per 6014th of 32

• Shot quality
  HopefulDangerous

  a 6.0% chance of scoring on the average shot27th of 32

  a 5.4% chance of scoring on the average shot30th of 32

• Second chances
  One-and-doneCrashers

  10.1% of chances come from rebounds23rd of 32

  3.1% of chances come from rebounds29th of 32

• Off-turnover offense
  PatientOpportunist

  8.4% of chances come right after a turnover1st of 32

  1.8% of chances come right after a turnover31st of 32

• Shots from defensemen
  Forward-ledActive D

  31.8% of shot attempts come from defensemen28th of 32

  28.5% of shot attempts come from defensemen31st of 32

• Shots from in close
  From distanceIn close

  31.2% of shots come from within 20 feet9th of 32

  35.8% of shots come from within 20 feet5th of 32

Defense

• Shot suppression
  PorousStingy

  60.8 shot attempts allowed per 6028th of 32

  55.8 shot attempts allowed per 6018th of 32

• Quality allowed
  ExposedSheltered

  a 6.4% chance of scoring on the average shot against19th of 32

  a 7.4% chance of scoring on the average shot against29th of 32

• Breakdowns avoided
  Fire drillComposed

  0.43 defensive breakdowns per 6024th of 32

  0.78 defensive breakdowns per 6028th of 32

• Goaltending
  SieveWall

  +0.05 goals saved above expected per 6026th of 32

  +0.99 goals saved above expected per 607th of 32

Tempo and edge

• Pace
  SlogTrack meet

  84.4 shots per 60, both teams combined5th of 32

  83.9 shots per 60, both teams combined9th of 32

• Forecheck pressure
  PassiveHounding

  forecheck score 90 (100 is league average)24th of 3222.1 forecheck plays recorded per 6017th of 32

  forecheck score 82 (100 is league average)25th of 3222.3 forecheck plays recorded per 6020th of 32

• Physicality
  FinesseBruising

  hit score 85 (100 is league average)28th of 3225.6 hits recorded per 6021st of 32

  hit score 72 (100 is league average)30th of 3224.7 hits recorded per 6026th of 32

• Depth
  Top-heavyDeep

  40.8% of forward ice time at 5-on-5 goes to the bottom six25th of 32

  40.4% of forward ice time at 5-on-5 goes to the bottom six27th of 32

Special teams

• Power play
  HarmlessLethal

  7.8 expected goals per 60 on the power play10th of 32

  8.4 expected goals per 60 on the power play6th of 32

• Penalty kill
  LeakyAirtight

  8.6 expected goals allowed per 60 shorthanded32nd of 32

  9.7 expected goals allowed per 60 shorthanded29th of 32

• Power kill
  BunkeredPredatory

  0.8 expected goals created per 60 shorthanded23rd of 32

  0.0 expected goals created per 60 shorthanded31st of 32

• Discipline
  RecklessClean

  0.23 more penalties taken than drawn per 6031st of 32

  0.00 more penalties drawn than taken per 6015th of 32

Forecheck pressure and physicality are shown as arena-adjusted scores; switch “Hits and takeaways” to Raw in the settings to see the numbers as recorded. How the arena adjustment works.

Who Toronto leans on, and what they do best.

• Auston Matthews

  22:14 a game

  #34 · Center · 6 P in 5 GP (2 G, 4 A)

  • Most ice time
  • L1 · Roslovic, Marchenko
  • PP1
  • Tough minutes
  • Creating his own chancesTop 5
  • Takeawaystop 6%
  • Shooting volumetop 7%
  • Drawing penaltiesbottom 23%

  No clear strengths or weak spots yet.

• William Nylander

  20:14 a game

  #88 · Right wing · 6 P in 5 GP (3 G, 3 A)

  • 2nd in ice time
  • L4 · Tavares, Cowan
  • PP1
  • Tough minutes
  • FinishingTop 5
  • Setting up goalstop 5%
  • Takeawaystop 7%
  • Preventing chancesbottom 1%
  • Shooting volumetop 19%
  • Hardest shotTop 10
  • Preventing chancesbottom 8%

• Kirill Marchenko

  19:33 a game

  #86 · Right wing · 4 P in 5 GP (1 G, 3 A)

  • L1 · Roslovic, Matthews
  • PP1
  • TakeawaysTop 10
  • Shooting volumeTop 10
  • Distance skatedTop 10
  • Shot blockingbottom 23%
  • Shooting volumetop 11%
  • Fast burststop 12%

• John Tavares

  17:10 a game

  #91 · Center · 3 P in 5 GP (1 G, 2 A)

  • L4 · Nylander, Cowan
  • PP1
  • Tough minutes
  • Power-play chancestop 5%
  • Faceoffstop 7%
  • Creating his own chancestop 9%
  • Hittingbottom 25%
  • Preventing chancesbottom 9%

• Darren Raddysh

  23:33 a game

  #43 · Defenseman · 2 P in 5 GP (0 G, 2 A)

  • Most ice time
  • P2 · Rielly
  • PP1
  • Shooting volumeTop 10
  • Setting up goalstop 10%
  • Finishingtop 11%
  • Shot blockingbottom 9%
  • Shooting volumetop 11%
  • Hardest shottop 9%
  • Hittingbottom 23%

• Morgan Rielly

  20:16 a game

  #44 · Defenseman · 3 P in 5 GP (0 G, 3 A)

  • 2nd in ice time
  • P2 · Raddysh
  • PP2
  • Kills penalties
  • Distance skatedtop 15%
  • Preventing chancesbottom 1%
  • Preventing chancesbottom 17%

The 4 forwards and 2 defensemen with the most ice time a game (at least half the games played). Tags show how each is used; rows show his clearest strengths, and a weak spot in grey, as a percentile against his position. Point at anything for details. All Maple Leafs players.

Who plays together at 5-on-5, and how each group is doing.

Lines and pairs are numbered by 5-on-5 ice time together over the team’s last 10 games (5 so far this season), with each older game counting half as much as the one after it, so the latest combinations come first. With so few games, each group’s results are pulled toward the average for its line number.

Last game

Which opposing lines each Toronto group has actually been out against at 5-on-5.

Each strip adds to 100%: it shows how one Toronto group’s 5-on-5 time is split across the opponent’s four forward lines, numbered the same way as above. Sized by ice time, a wider box means more time against that line; even columns line the boxes up so you can compare groups straight down. At home the coach changes last and picks these matchups; on the road the other coach does. Faded rows have under ten minutes.

What “with no line matching” means: if a coach just rolled his lines and took whoever was out there, each of his groups would face the opponent’s first line about as often as that line is on the ice. If opposing first lines play a third of the time against Toronto, every Toronto group would see them for about a third of its shifts. A number well above that means the coach is sending that group out against them on purpose; well below means he is keeping it away.

Who Toronto sends out with a man up and a man down.

Each power-play unit is built around its quarterback, the defenseman running it, plus the four players most often out with him over the last 10 games (5 so far). Power-play units’ expected goals per 60 are pulled toward league average while samples are small. Penalty kills are too loose for fixed units (the most common foursome covers only about a tenth of a typical team’s shorthanded time), so penalty killers are grouped by when they go on: the ones who start kills, the second wave who come on after the first change, and spot duty. Point at a power-play name to see his shots on the map.

What the tags mean

Quarterbackthe defenseman who runs the unit from the blue line.

Triggerthe unit’s main shooter, with the most dangerous power-play shots on the team over this season and last.

Distributorthe main passer, with the most first assists on the team’s power-play goals over this season and last.

Net-frontparks in front of the net. At least 40% of his power-play shots come from within 15 feet.

Pointa second defenseman on the unit, alongside the quarterback.

Is he stopping more than he should, and what is his team asking him to face?

Expected-goal models can’t see passes before a shot, so a goalie whose team gives up a lot of cross-ice passes will look worse here than he really is. Ratings compare him with goalies who have at least 10 starts over two seasons, or 2 this season.

How Toronto’s goals are scored and allowed, against the league average, and how much of any difference luck alone could explain.

Every goal counts once, in the first of these that applies: empty net, power play, shorthanded, goalie pulled, then rebound, turnover, faceoff win, and everyday play. These mixes are mostly luck: over three seasons, teams’ goals came from much the same places, and most of the differences here didn’t last.