T5M_Hammer at Planetary Qualifier Oviedo

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Planetary Premier April 25, 2026 Record 3-3-0 Field 44

Rating after 1,345

+38

Rating before 1,306 RD 94

Rating after 1,345 RD 91

Effective multiplier 1.0×

Performance 1,616

Field strength Mean 1,545 · Median 1,519 · 44 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 1306 → Swiss 1345 +38.1

Top Cut Bracket Full bracket on the event page

Quarterfinals

JAEstwald 1

Diego-jr 2

Loira 2

Farreli 0

Evilcloud 0

miguividal 2

Fuegoscuro86 2

McLion34 1

Semifinals

Diego-jr 0

Loira 2

miguividal 2

Fuegoscuro86 1

Finals

Loira 2

miguividal 1

See full bracket on the event page →

Swiss · 6 matches 3-3 +38.1 1.0× +0.0 +38.1 1345

Total 3-3-0 +38.1 1345

Biggest upset Beat Gorka 1704 at 21% odds / surprise +0.79

Costliest loss Lost to klyssus 1403 at 42% odds / surprise -0.42 /

Matches (6)

R1 Ferrepower Win 2-1 1.0× +22.8 Swiss

Glicko-2 predicted only 24% 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 156, true skill could span ±313).

R2 Vanieh Loss 0-2 1.0× Swiss

R3 AbelRM Win 2-1 1.0× +20.4 Swiss

Glicko-2 predicted only 33% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.

R4 JAEstwald Loss 0-2 1.0× Swiss

R5 klyssus Loss 1-2 1.0× Swiss

R6 Gorka Win 2-1 1.0× +24.3 Swiss

Glicko-2 predicted only 21% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.