Rankings / FF_LordBuckethead / Planetary Qualifier London

FF_LordBuckethead at Planetary Qualifier London

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Planetary Premier January 18, 2025 Record 3-4-0 Field 102
Rating after 1,465
-35
Rating before 1,500 RD 250
Rating after 1,465 RD 173
Effective multiplier 1.0× weighted avg
Performance 1,433 vs field -99
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 3-4 -34.5 1.0× +0.0 -34.5 1465
Total 3-4-0 -34.5 1465
Biggest upset Beat Sesran 1547 at 46% odds / surprise +0.54
Costliest loss Lost to MotoTaleric_DSP 1500 at 50% odds / surprise -0.5 / +170 swing

Matches (7)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 1500~ ItsLewis96 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).
R2 ▼ 1500~ MotoTaleric_DSP 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).
R3 1500~ ThomErritt Win 50% 2-1 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).
R4 1612~ BigONotation Loss 41% 0-2 1.0× -73.3 Swiss
R5 1500~ OllieEN 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).
R6 ▲ 1547~ Sesran Win 46% 2-1 1.0× +95.2 Swiss
Glicko-2 predicted only 46% 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 174, true skill could span ±348).
R7 1500~ KadC1 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).

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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