Buy-Low / Sell-High

2025-26 · finishing = goals − expected goals (xGF). Ranked by the LUCK share of the surplus — the unsustainable part of a hot streak.

❓ How to read this (plain English)

Finding players whose goal totals lie about how well they're actually playing.

G − xG
Goals scored minus goals EXPECTED from the shots taken. Negative = getting robbed; positive = running hot.
Luck (regresses)
The share of that gap that's genuinely bad/good luck — it fades. This is the number that predicts a rebound.
Skill (persists)
The share that's just how this player finishes, year after year. Chronic under-finishers are NOT buy-lows.
Iron
Posts + crossbars hit (and the goal value of those shots). Pure bad luck — centimeters from goals.
ENG
Empty-net goals — real points, but they say nothing about shooting skill, so we remove them before judging.
Snakebit
The bottom line: luck deficit plus iron. Most negative = most deserving of better results = best buy.
#PlayerTeamPosGPGxGFSOGG − xGLuck (regresses)Skill (persists)IronENGHD finishHD shotsSnakebit score
1Anthony Mantha NJD · RNJDR813317.18152+15.8+7.8+7.01 (0 xG)1+11.165+7.8
2Morgan Geekie BOS · CBOSC813922.43181+16.6+7.1+8.42 (0.2 xG)1+5.558+6.9
3William Nylander TOR · RTORR653016.58156+13.4+5.6+4.92 (0.2 xG)3+10.946+5.4
4Filip Forsberg NSH · LNSHL824024.95246+15.1+5.2+6.81 (0.1 xG)3+3.558+5.1
5Parker Kelly COL · CCOLC822111.86104+9.1+5.0+3.11+3.737+5.0
6Nikita Kucherov TBL · RTBLR774424.48231+19.5+5.5+7.06 (0.6 xG)7+9.145+4.9
7Cutter Gauthier ANA · LANAL764126.76285+14.2+4.7+6.63+2.752+4.7
8Sidney Crosby PIT · CPITC682918.52160+10.5+4.8+4.72 (0.2 xG)1+3.762+4.6
9Bobby McMann SEA · CSEAC782915.38183+13.6+4.8+3.95 (0.3 xG)5+4.951+4.5
10Pavel Zacha BOS · CBOSC783019.8131+10.2+4.5+4.71+6.961+4.5
11Adrian Kempe LAK · RLAKR813623.57226+12.4+4.7+5.87 (0.3 xG)2+5.359+4.4
12Robert Thomas STL · CSTLC642513.83102+11.2+4.7+3.42 (0.3 xG)3+2.543+4.4
13Brad Marchand FLA · LFLAL522715.89138+11.1+4.4+3.73+4.4
14Matthew Schaefer NYI · DNYID822315.11222+7.9+4.4+3.51 (0 xG)-0.618+4.4
15Logan Cooley UTA · CUTAC542414.6102+9.4+4.2+3.22+4.2
16Darren Raddysh TOR · DTORD732213.86212+8.1+4.7+3.46 (0.5 xG)-1.211+4.2
17Cole Caufield MTL · RMTLR815136.9258+14.1+4.8+9.36 (0.8 xG)+6.479+4.0
18Martin Necas COL · CCOLC783824.89206+13.1+4.4+5.73 (0.4 xG)3+1.354+4.0
19Alex Tuch WSH · RWSHR793322.34195+10.7+4.4+5.26 (0.4 xG)1+4.966+4.0
20Mark Scheifele WPG · CWPGC823623.9175+12.1+4.0+5.12 (0.1 xG)3-2.166+3.9
21Leon Draisaitl EDM · CEDMC653524.41186+10.6+4.2+5.45 (0.3 xG)1+4.553+3.9
22Jakob Chychrun WSH · DWSHD802618.22221+7.8+4.0+3.83 (0.2 xG)-1.126+3.8
23Steven Stamkos NSH · CNSHC824225.81206+16.2+4.3+5.96 (0.6 xG)6+6.563+3.7
24Jean-Gabriel Pageau NYI · CNYIC741710.3381+6.7+3.7+2.02 (0.1 xG)1+4.538+3.6
25Wyatt Johnston DAL · CDALC824533.8206+11.2+3.7+6.52 (0.2 xG)1+8111+3.5
26Cody Glass NJD · CNJDC701913.28104+5.7+3.4+2.4+4.145+3.4
27Macklin Celebrini SJS · CSJSC824530.68287+14.3+3.6+5.83 (0.3 xG)5+1.359+3.3
28Matt Boldy MIN · LMINL764228.67254+13.3+3.3+5.02 (0.2 xG)5+5.484+3.1
29Drake Batherson OTT · ROTTR793324.82167+8.2+3.5+4.65 (0.5 xG)+10.270+3.0
30Nick Schmaltz UTA · CUTAC823325.48206+7.5+3.2+4.32 (0.2 xG)+4.688+3.0
31Zach Werenski CBJ · DCBJD752216.36260+5.6+3.0+2.62 (0 xG)-2.417+3.0
32Dylan Guenther UTA · RUTAR794030.36242+9.6+3.7+5.911 (0.8 xG)+4.359+2.9
33Vladimir Tarasenko · RR752315.92148+7.1+3.3+2.84 (0.4 xG)1-1.739+2.9
34Mitch Marner VGK · RVGKR812417.42165+6.6+3.4+3.14 (0.5 xG)+3.742+2.9
35Nathan MacKinnon COL · CCOLC805335.96350+17.0+3.1+5.94 (0.3 xG)8+2.683+2.8
36Mika Zibanejad NYR · CNYRC813425.05215+8.9+3.4+4.57 (0.6 xG)1+3.553+2.8
37Alexander Wennberg SJS · CSJSC801812.293+5.8+2.9+1.91 (0.1 xG)1+2.553+2.8
38Nikolaj Ehlers CAR · LCARL822618.72207+7.3+3.2+3.14 (0.5 xG)1+2.732+2.7
39Brock Nelson COL · CCOLC813324.53186+8.5+2.8+3.62 (0.2 xG)2+0.474+2.6
40Egor Chinakhov PIT · RPITR722116140+5.0+2.7+2.32 (0.1 xG)+0.135+2.6