Richard Blais at Planetary Qualifier Montreal

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Planetary Premier April 25, 2026 Record 3-2-2 Field 82

Rating after 1,518

+32

Rating before 1,486 RD 114

Rating after 1,518 RD 103

Effective multiplier 1.0×

Performance 1,644

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 1486 → Swiss 1518 +32.3

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 3-2-2 +32.3 1.0× +0.0 +32.3 1518

Total 3-2-2 +32.3 1518

Biggest upset Beat RFrancisR 1780 at 27% odds / surprise +0.73

Costliest loss Lost to Elkady 1500 at 49% odds / surprise -0.49 /

Matches (7)

R1 D3ploy_MythicForce Draw 1-1 1.0× +3.0 Swiss

R2 Elkady Loss 0-1 1.0× Swiss

R3 ORA_Undead Win 2-1 1.0× +20.6 Swiss

R4 carbonite Draw 1-1 1.0× +2.0 Swiss

R5 LouisValiquette Win 1-0 1.0× +20.4 Swiss

Glicko-2 predicted only 49% 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 250, true skill could span ±500).

R6 RFrancisR Win 2-0 1.0× +31.7 Swiss

Glicko-2 predicted only 27% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.

R7 PBJ12 Loss 0-2 1.0× Swiss