JohnYellow at Planetary Qualifier Radcliff

melee.gg
Planetary Premier April 25, 2026 Record 5-2-1 Field 44
Rating after 2,160
+3
Rating before 2,157 RD 79
Rating after 2,160 RD 82
Effective multiplier 0.85× weighted avg
Performance 1,944 vs field +361
Field strength Mean 1,583 · Median 1,522 · 44 rated 54th Percentile Planetaries, 2026 Q2

// 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.

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.

Top Cut Bracket Full bracket on the event page
Phase Record Raw Δ Multiplier Bonus Applied Running
Swiss · 6 matches 4-1-1 -5.1 1.0× +0.0 -5.1 2152
Quarterfinals · 1 match 1-0 +4.9 1.12× 1.00 · +0.12 +0.6 +5.5 2155
Semifinals · 1 match 0-1 -16.4 0.73× 1.00 · -0.27 +4.4 -12.0 2143
Top-cut bonus Tier-scaled additive ‧ Planetary at 44 +15.0 2158
Total 5-2-1 +3.4 2160
Toughest win Beat Yamil Pietri 1816 at 76% odds
Costliest loss Lost to Slagleevan 1671 at 84% odds / surprise -0.84 / +22 swing

Matches (8)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 1816 Yamil Pietri Win 76% 2-1 1.0× +5.2 Swiss
R2 1671 Slagleevan Loss 84% 0-2 1.0× -18.6 Swiss
Glicko-2 predicted 84% odds in your favor. Unexpected losses carry the most rating signal — the system learns more from one upset than from many predictable wins.
R3 1274 JMHIMEL Win 95% 2-0 1.0× +1.1 Swiss
R4 1685 julianharmon Win 83% 2-0 1.0× +3.7 Swiss
R5 1645 blakeken Win 85% 2-0 1.0× +3.3 Swiss
R6 1920 NoahG1212 Draw 0-0 1.0× ±0.0 Swiss
QF 1632~ AlbatrossLuke Win 85% 2-0 1.12× +3.6 Top Cut
Semis 1827 Aaron Higgins Loss 75% 1-2 0.73× -12.1 Top Cut
Glicko-2 predicted 75% odds in your favor. Unexpected losses carry the most rating signal — the system learns more from one upset than from many predictable wins.
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