Tommycallahan123 at Planetary Qualifier Yuma

melee.gg

Planetary Limited April 25, 2026 Record 3-3-0 Field 43

Rating after 1,572

+72

Rating before 1,500 RD 250

Rating after 1,572 RD 182

Effective multiplier 1.0×

Performance 1,644

Field strength Mean 1,575 · Median 1,552 · 42 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 1500 → Swiss 1572 +71.9

Top Cut Bracket Full bracket on the event page

Quarterfinals

Spencer Freeman 2

JonahBR 0

llopez101 2

_ODIN_WAN_KENOBI_ 1

CBG_Taylor 2

CheeseKahuna 0

Arteiris 2

CBG_Ruben 1

Semifinals

Spencer Freeman 2

CBG_Taylor 0

llopez101 2

Arteiris 0

Finals

Spencer Freeman 2

llopez101 1

See full bracket on the event page →

Swiss · 6 matches 3-3 +71.9 1.0× +0.0 +71.9 1572

Total 3-3-0 +71.9 1572

Biggest upset Beat WhiteFlex 1678 at 36% odds / surprise +0.64

Costliest loss Lost to _ODIN_WAN_KENOBI_ 1500 at 50% odds / surprise -0.5 /

Matches (6)

R1 Bepsiman Win 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 Arteiris Win 2-0 1.0× +99.0 Swiss

Glicko-2 predicted only 44% 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 160, true skill could span ±321).

R3 WhiteFlex Win 2-0 1.0× +116.0 Swiss

Glicko-2 predicted only 36% 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 152, true skill could span ±304).

R4 Spencer Freeman Loss 0-2 1.0× Swiss

R5 CBG_Taylor Loss 0-2 1.0× Swiss

R6 _ODIN_WAN_KENOBI_ Loss 1-2 1.0× 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).