David_MoP at Planetary Qualifier London

melee.gg

Planetary Premier January 11, 2026 Record 3-3-0 Field 54

Rating after 1,319

+59

Rating before 1,260 RD 131

Rating after 1,319 RD 120

Effective multiplier 1.0×

Performance 1,545

Field strength Mean 1,540 · Median 1,529 · 54 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 1260 → Swiss 1319 +58.6

Top Cut Bracket Full bracket on the event page

Quarterfinals

Darthsilk_DSP 2

DurW 1

Tommaso Madonia 0

Reader_DSP 2

OskarKristensen 2

MinionNumberQ 1

JamesDoering 1

Cortosis_DSP 2

Semifinals

Darthsilk_DSP 1

Reader_DSP 2

OskarKristensen 2

Cortosis_DSP 1

Finals

Reader_DSP 1

OskarKristensen 2

See full bracket on the event page →

Swiss · 6 matches 3-3 +58.6 1.0× +0.0 +58.6 1319

Total 3-3-0 +58.6 1319

Biggest upset Beat Stealthmoose_DSP 1708 at 18% odds / surprise +0.82

Costliest loss Lost to arranf 1500 at 32% odds / surprise -0.32 /

Matches (6)

R1 Reader_DSP Loss 0-2 1.0× Swiss

R2 HarA4 Win 2-0 1.0× +31.7 Swiss

Glicko-2 predicted only 42% 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 184, true skill could span ±368).

R3 arranf Loss 1-2 1.0× Swiss

R4 DennyProvenzano Loss 0-2 1.0× Swiss

R5 PSICORP Win 2-0 1.0× +27.5 Swiss

R6 Stealthmoose_DSP Win 2-0 1.0× +47.5 Swiss

Glicko-2 predicted only 18% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.