Disnard at Planetary Qualifier Montreal

melee.gg

Planetary Premier April 25, 2026 Record 3-2-0 Field 82

Rating after 1,457

+114

Rating before 1,343 RD 171

Rating after 1,457 RD 149

Effective multiplier 1.0×

Performance 1,738

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 1343 → Swiss 1457 +114.0

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 · 5 matches 3-2 +114.0 1.0× +0.0 +114.0 1457

Total 3-2-0 +114.0 1457

Biggest upset Beat Christopher Lefrancois 1682 at 25% odds / surprise +0.75

Costliest loss Lost to BennyBlues 1593 at 30% odds / surprise -0.3 /

Matches (5)

R1 Obi-Dan Win 2-0 1.0× +53.8 Swiss

Glicko-2 predicted only 41% 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 127, true skill could span ±253).

R2 Colazel Win 2-0 1.0× +64.2 Swiss

Glicko-2 predicted only 31% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.

R3 Christopher Lefrancois Win 2-1 1.0× +69.4 Swiss

Glicko-2 predicted only 25% 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).

R4 Kevin Anctil Loss 0-2 1.0× Swiss

R5 BennyBlues Loss 1-2 1.0× Swiss