Vicky_MoP at Sector Qualifier Munich

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Sector Premier November 01, 2025 Record 4-4-0 Field 512
Rating after 1,540
-13
Rating before 1,554 RD 132
Rating after 1,540 RD 114
Effective multiplier 1.15× weighted avg
Performance 1,513 vs field -72
Field strength Mean 1,585 · Median 1,552 · 512 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 Sector tier multiplier (1.15×) amplifies every gain and every loss.

Making cut at this Sector event adds a flat +25 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
Phase Record Raw Δ Multiplier Bonus Applied Running
Swiss · 8 matches 4-4 -11.5 1.15× -1.7 -13.2 1540
Total 4-4-0 -13.2 1540
Biggest upset Beat PeekaySK 1616 at 45% odds / surprise +0.55
Costliest loss Lost to Ralph Jacoby 1344 at 67% odds / surprise -0.67 / +66 swing

Matches (8)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 1545~ jlegolas_TT Loss 51% 0-2 1.15× -32.6 Swiss
Glicko-2 predicted 51% 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 167, true skill could span ±334).
R2 1327~ Harvey Win 68% 2-0 1.15× +20.6 Swiss
R3 1676~ eMeF13 Loss 40% 0-2 1.15× -25.9 Swiss
R4 1344 Ralph Jacoby Loss 67% 1-2 1.15× -44.5 Swiss
Glicko-2 predicted 67% odds in your favor. Unexpected losses carry the most rating signal — the system learns more from one upset than from many predictable wins.
R5 1482~ Starfox8 Win 56% 2-0 1.15× +28.5 Swiss
R6 1327~ BuboS Win 68% 2-1 1.15× +20.9 Swiss
R7 1790 Firebrand Loss 31% 0-2 1.15× -20.6 Swiss
R8 1616 PeekaySK Win 45% 2-0 1.15× +36.3 Swiss
Glicko-2 predicted only 45% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.
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