Jocampo17 at Planetary Qualifier Radcliff

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Planetary Premier April 25, 2026 Record 2-4-0 Field 44

Rating after 1,366

-134

Rating before 1,500 RD 250

Rating after 1,366 RD 179

Effective multiplier 1.0×

Performance 1,227

Field strength Mean 1,583 · Median 1,522 · 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 1500 → Swiss 1366 -134.4

Top Cut Bracket Full bracket on the event page

Quarterfinals

RelicRelic 2

blakeken 1

Devias 2

NoahG1212 1

AlbatrossLuke 0

JohnYellow 2

Aaron Higgins 2

projector101 0

Semifinals

RelicRelic 2

Devias 0

JohnYellow 1

Aaron Higgins 2

Finals

RelicRelic 1

Aaron Higgins 2

See full bracket on the event page →

Swiss · 6 matches 2-4 -134.4 1.0× +0.0 -134.4 1366

Total 2-4-0 -134.4 1366

Biggest upset Beat Cody Murphy 1500 at 50% odds / surprise +0.5

Costliest loss Lost to Bridget Faulkner 1257 at 69% odds / surprise -0.69 /

Matches (6)

R1 Charlieeee B Loss 0-2 1.0× Swiss

Glicko-2 predicted 52% odds in your favor. Unexpected losses carry the most rating signal — the system learns more from one upset than from many predictable wins.

R2 Swally1993 Loss 0-2 1.0× Swiss

Glicko-2 predicted 57% 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 170, true skill could span ±341).

R3 Cody Murphy Win 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 Ray Mack Win 2-0 1.0× +62.7 Swiss

R5 Justin_B Loss 1-2 1.0× Swiss

R6 Bridget Faulkner Loss 1-2 1.0× Swiss

Glicko-2 predicted 69% 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 176, true skill could span ±352).