Swally1993 at Planetary Qualifier Radcliff

melee.gg
Planetary Premier April 25, 2026 Record 2-4-0 Field 44
Rating after 1,352
-65
Rating before 1,417 RD 170
Rating after 1,352 RD 141
Effective multiplier 1.0× weighted avg
Performance 1,227 vs field -356
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 2-4 -64.9 1.0× +0.0 -64.9 1352
Total 2-4-0 -64.9 1352
Biggest upset Beat Jocampo17 1500 at 44% odds / surprise +0.56
Costliest loss Lost to BKO158 1328 at 57% odds / surprise -0.57 / +91 swing

Matches (6)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 1451~ Antony Mock Loss 47% 0-2 1.0× -42.1 Swiss
R2 1500~ Jocampo17 Win 44% 2-0 1.0× +48.3 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 250, true skill could span ±500).
R3 1457 BeardedBabyy Loss 47% 1-2 1.0× -42.7 Swiss
R4 1328~ BKO158 Loss 57% 0-2 1.0× -52.4 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 126, true skill could span ±252).
R5 1408~ ShaS2 Loss 51% 0-2 1.0× -46.3 Swiss
Glicko-2 predicted 51% 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 124, true skill could span ±248).
R6 1500~ Shane Lines Win 44% 2-1 1.0× +48.3 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 250, true skill could span ±500).
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