Rankings / Vicky_MoP / Planetary Qualifier London

Vicky_MoP at Planetary Qualifier London

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Planetary Premier January 18, 2025 Record 2-5-0 Field 102
Rating after 1,379
-121
Rating before 1,500 RD 250
Rating after 1,379 RD 173
Effective multiplier 1.0× weighted avg
Performance 1,259 vs field -273
Field strength Mean 1,532 · Median 1,500 · 102 rated 74th Percentile Planetaries, 2025 Q1

// 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.

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.

Top Cut Bracket Full bracket on the event page
Quarterfinals
BobbySapphire 2
ThrawnZB 1
StrawSword 0
MotoTaleric_DSP 2
NXS_CheifGoose 1
GeorgiS 2
Beton 2
VVRATHH 0
Semifinals
BobbySapphire 0
MotoTaleric_DSP 2
GeorgiS 1
Beton 2
Finals
MotoTaleric_DSP 2
Beton 0
See full bracket on the event page →
Phase Record Raw Δ Multiplier Bonus Applied Running
Swiss · 7 matches 2-5 -120.5 1.0× +0.0 -120.5 1379
Total 2-5-0 -120.5 1379
Biggest upset Beat GreedoRevan 1500 at 50% odds / surprise +0.5
Costliest loss Lost to ConsolidatingPower 1454 at 54% odds / surprise -0.54 / +177 swing

Matches (7)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 1709~ TeeMouse Loss 33% 0-2 1.0× -60.1 Swiss
R2 1500~ ThomRawson Loss 50% 1-2 1.0× -85.1 Swiss
Glicko-2 predicted 50% 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 250, true skill could span ±500).
R3 ▼ 1454~ ConsolidatingPower Loss 54% 0-1 1.0× -94.9 Swiss
Glicko-2 predicted 54% 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 173, true skill could span ±347).
R4 ▲ 1500~ GreedoRevan Win 50% 2-0 1.0× +85.1 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 250, true skill could span ±500).
R5 1500~ AdTheNad Win 50% 1-0 1.0× +85.1 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 250, true skill could span ±500).
R6 1695~ ddanblack Loss 34% 0-2 1.0× -62.1 Swiss
R7 1500~ R8C2 Loss 50% 0-2 1.0× -85.1 Swiss
Glicko-2 predicted 50% 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 250, true skill could span ±500).

Per-match multipliers reflect what the engine applied at the time this snapshot was recorded. The Effective Multiplier above is a delta-weighted average across all phases — Finals matches swing the headline more than Swiss rounds when they produced bigger rating changes. How it works.

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