RocketLight at Planetary Qualifier Cape Town

melee.gg

Planetary Premier April 25, 2026 Record 3-3-0 Field 22

Rating after 1,474

+44

Rating before 1,430 RD 157

Rating after 1,474 RD 136

Effective multiplier 0.98×

Performance 1,508

Field strength Mean 1,526 · Median 1,500 · 22 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 +0.06 +0.09 +0.12 Losses -0.09 -0.14 -0.18

Making cut at this Planetary event adds a flat +8 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 1430 → Swiss 1481 +50.8 → Quarterfinals 1456 -25.2 → Top-cut bonus 1464 +8.0

Top Cut Bracket Full bracket on the event page

Quarterfinals

Sungazer 2

RocketLight 0

UnableToWin 2

Adjcass 0

luka8luka 2

TonyFBaby 0

john196 0

Zeyaad Pandey 2

Semifinals

Sungazer 2

UnableToWin 0

luka8luka 0

Zeyaad Pandey 2

Finals

Sungazer 1

Zeyaad Pandey 2

See full bracket on the event page →

Swiss · 5 matches 3-2 +50.8 1.0× +0.0 +50.8 1481

Quarterfinals · 1 match 0-1 -16.4 0.91× 1.00 · -0.09 +1.5 -14.9 1456

Top-cut bonus Tier-scaled additive ‧ Planetary at 22 +8.0 1464

Total 3-3-0 +43.9 1474

Biggest upset Beat Luke_C 1596 at 37% odds / surprise +0.63

Costliest loss Lost to Adjcass 1426 at 50% odds / surprise -0.5 /

Matches (6)

R1 Adjcass Loss 0-2 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 187, true skill could span ±373).

R2 Goggas Win 2-0 1.0× +31.4 Swiss

R3 Luke_C Win 2-1 1.0× +49.2 Swiss

Glicko-2 predicted only 37% 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 157, true skill could span ±313).

R4 FranscoisdT Win 2-0 1.0× +47.7 Swiss

Glicko-2 predicted only 38% 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 180, true skill could span ±361).

R5 TonyFBaby Loss 0-2 1.0× Swiss

QF Sungazer Loss 0-2 0.91× Top Cut