Obi-Dan at Planetary Qualifier Montreal

melee.gg

Planetary Premier April 25, 2026 Record 4-3-0 Field 82

Rating after 1,494

+49

Rating before 1,445 RD 127

Rating after 1,494 RD 112

Effective multiplier 1.0×

Performance 1,653

Field strength Mean 1,579 · Median 1,558 · 82 rated

// rating math · glicko-2 phase replay

How this rating change was computed

Glicko-2 doesn’t reward record — it rewards surprise. Winning a match the system expected you to win is worth almost nothing. Losing one is expensive. Losing to someone rated below you costs the most. The Planetary tier multiplier (1.0×) amplifies every gain and every loss.

Shape D top-cut adjustments QF SF F Wins +0.12 +0.18 +0.25 Losses -0.18 -0.27 -0.35

Making cut at this Planetary event adds a flat +15 top-cut bonus on top of phase math. Making cut never costs you net rating — the made-cut floor pins the tournament delta to ≥ 0.

Start 1445 → Swiss 1494 +49.4

Top Cut Bracket Full bracket on the event page

Quarterfinals

Krehlo_MythicForce 2

DavB87 0

Byrus_MythicForce 2

realready 1

Guacamol 0

Order514_Dinozor 2

Kevin Anctil 2

BennyBlues 0

Semifinals

Krehlo_MythicForce 2

Byrus_MythicForce 0

Order514_Dinozor 2

Kevin Anctil 1

Finals

Krehlo_MythicForce 1

Order514_Dinozor 2

See full bracket on the event page →

Swiss · 7 matches 4-3 +49.4 1.0× +0.0 +49.4 1494

Total 4-3-0 +49.4 1494

Biggest upset Beat rodrigo514 1718 at 28% odds / surprise +0.72

Costliest loss Lost to Disnard 1343 at 58% odds / surprise -0.58 /

Matches (7)

R1 Disnard Loss 0-2 1.0× Swiss

Glicko-2 predicted 58% odds in your favor. Unexpected losses carry the most rating signal — the system learns more from one upset than from many predictable wins. Their rating wasn't well-established (RD 171, true skill could span ±342).

R2 JonD37 Win 2-1 1.0× +28.6 Swiss

Glicko-2 predicted only 45% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect. Their rating wasn't well-established (RD 166, true skill could span ±332).

R3 ORA_Louisere Win 2-0 1.0× +26.1 Swiss

Glicko-2 predicted only 50% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect. Their rating wasn't well-established (RD 150, true skill could span ±299).

R4 Miranda Ketita Win 2-1 1.0× +38.0 Swiss

Glicko-2 predicted only 29% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.

R5 rodrigo514 Win 2-1 1.0× +38.0 Swiss

Glicko-2 predicted only 28% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect. Their rating wasn't well-established (RD 122, true skill could span ±245).

R6 realready Loss 0-2 1.0× Swiss

R7 Mcpobid Loss 0-2 1.0× Swiss