Vicky_MoP at Planetary Qualifier London

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Planetary Premier August 16, 2025 Record 4-1-2 Field 60

Rating after 1,558

+178

Rating before 1,379 RD 179

Rating after 1,558 RD 152

Effective multiplier 0.98×

Performance 1,958

Field strength Mean 1,549 · Median 1,505 · 60 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 1379 → Swiss 1564 +184.8 → Quarterfinals 1540 -24.1 → Top-cut bonus 1555 +15.0

Top Cut Bracket Full bracket on the event page

Quarterfinals

Andres Ramos 2

WoTDS_Deowag 0

Vicky_MoP 0

LXO_AIB0 2

Darthsilk_DSP 0

Josh Bayton 2

NXS_CheifGoose 2

yehosera 0

Semifinals

Andres Ramos 2

LXO_AIB0 1

Josh Bayton 0

NXS_CheifGoose 2

Finals

Andres Ramos 2

NXS_CheifGoose 1

See full bracket on the event page →

Swiss · 6 matches 4-0-2 +184.8 1.0× +0.0 +184.8 1564

Quarterfinals · 1 match 0-1 -26.4 0.82× 1.00 · -0.18 +4.8 -21.7 1540

Top-cut bonus Tier-scaled additive ‧ Planetary at 60 +15.0 1555

Total 4-1-2 +178.2 1558

Biggest upset Beat Josh Bayton 1525 at 38% odds / surprise +0.62

Costliest loss Lost to LXO_AIB0 1713 at 38% odds / surprise -0.38 /

Matches (7)

R1 Falconmonkey_DSP Win 2-1 1.0× +55.8 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 250, true skill could span ±500).

R2 GCS1598 Win 2-0 1.0× +50.9 Swiss

Glicko-2 predicted only 48% 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 178, true skill could span ±356).

R3 dandannoodle Win 2-1 1.0× +58.6 Swiss

R4 Josh Bayton Win 2-1 1.0× +62.6 Swiss

Glicko-2 predicted only 38% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.

R5 NXS_CheifGoose Draw 0-0 1.0× ±0.0 Swiss

R6 LXO_AIB0 Draw 0-0 1.0× ±0.0 Swiss

QF LXO_AIB0 Loss 0-2 0.82× Top Cut