David_MoP at Sector Qualifier Munich

melee.gg

Sector Premier November 01, 2025 Record 2-6-0 Field 512

Rating after 1,260

-28

Rating before 1,289 RD 153

Rating after 1,260 RD 129

Effective multiplier 1.15×

Performance 1,208

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.

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 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.

Start 1289 → Swiss 1260 -28.3

Top Cut Bracket Full bracket on the event page

Quarterfinals

T85Challenge 2

SobekSobek 0

TheFrostwolf 1

OliTheTraveler 2

Jona Roos 2

StevoHN 0

Menicks 0

TSF_Wulle 2

Semifinals

T85Challenge 0

OliTheTraveler 2

Jona Roos 2

TSF_Wulle 0

Finals

OliTheTraveler 0

Jona Roos 2

See full bracket on the event page →

Swiss · 8 matches 2-6 -24.6 1.15× -3.7 -28.3 1260

Total 2-6-0 -28.3 1260

Biggest upset Beat BEO_Filou 1573 at 28% odds / surprise +0.72

Costliest loss Lost to CraggleTT 1394 at 41% odds / surprise -0.41 /

Matches (8)

R1 MrAngezockt Loss 1-2 1.15× Swiss

R2 Itsmenikeyy Win 2-0 1.15× +53.5 Swiss

Glicko-2 predicted only 34% 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).

R3 Purenvy Loss 0-2 1.15× Swiss

R4 Avesdel Loss 0-2 1.15× Swiss

R5 CraggleTT Loss 1-2 1.15× Swiss

R6 dukszz Loss 1-2 1.15× Swiss

R7 Masteryourface Loss 1-2 1.15× Swiss

R8 BEO_Filou Win 2-0 1.15× +63.4 Swiss

Glicko-2 predicted only 28% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.