Gounaki at Planetary Qualifier Montreal

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Planetary Premier April 25, 2026 Record 5-2-0 Field 82
Rating after 1,671
+171
Rating before 1,500 RD 250
Rating after 1,671 RD 172
Effective multiplier 1.0× weighted avg
Performance 1,831 vs field +252
Field strength Mean 1,579 · Median 1,558 · 82 rated 49th 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 · 7 matches 5-2 +171.2 1.0× +0.0 +171.2 1671
Total 5-2-0 +171.2 1671
Biggest upset Beat Miranda Ketita 1713 at 33% odds / surprise +0.67
Costliest loss Lost to Zorthak 1500 at 50% odds / surprise -0.5 / +170 swing

Matches (7)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 1682~ Christopher Lefrancois Loss 35% 0-2 1.0× -63.5 Swiss
R2 1500~ Cocodelmundo Win 50% 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).
R3 1500~ Zorthak Loss 50% 1-2 1.0× -85.1 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).
R4 1420~ Gharuwill Win 57% 2-0 1.0× +77.9 Swiss
R5 1713 Miranda Ketita Win 33% 2-1 1.0× +123.6 Swiss
Glicko-2 predicted only 33% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.
R6 1492 Order514_Stormenter Win 51% 2-0 1.0× +89.1 Swiss
R7 1500~ ValP10 Win 50% 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).
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