SC_louloup at Planetary Qualifier Paris

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Planetary Premier October 27, 2024 Record 6-2-0 Field 118
Rating after 1,620
+168
Rating before 1,453 RD 174
Rating after 1,620 RD 139
Effective multiplier 0.98× weighted avg
Performance 1,850 vs field +336
Field strength Mean 1,514 · Median 1,500 · 118 rated 95th Percentile Planetaries, 2024 Q4

// 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 6-1 +173.5 1.0× +0.0 +173.5 1626
Quarterfinals · 1 match 0-1 -25.5 0.82× 1.00 · -0.18 +4.6 -20.9 1596
Top-cut bonus Tier-scaled additive ‧ Planetary at 118 +15.0 1611
Total 6-2-0 +167.6 1620
Biggest upset Beat Phoenixton 1500 at 46% odds / surprise +0.54
Costliest loss Lost to NO_Jeremew 1500 at 60% odds / surprise -0.6 / +87 swing

Matches (8)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 1500~ stephenwolf Loss 46% 1-2 1.0× -41.2 Swiss
R2 1500~ Phoenixton Win 46% 2-0 1.0× +47.8 Swiss
Glicko-2 predicted only 46% 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~ Bryou666 Win 46% 2-1 1.0× +47.8 Swiss
Glicko-2 predicted only 46% 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 1500~ Tsutsu Win 46% 2-1 1.0× +47.8 Swiss
Glicko-2 predicted only 46% 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).
R5 1500~ TheSparkk Win 46% 2-0 1.0× +47.8 Swiss
Glicko-2 predicted only 46% 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).
R6 1500~ Rofellos Win 46% 2-0 1.0× +47.8 Swiss
Glicko-2 predicted only 46% 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).
R7 1500~ EC_ChuQi Win 46% 2-0 1.0× +47.8 Swiss
Glicko-2 predicted only 46% 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).
QF 1500~ NO_Jeremew Loss 60% 0-2 0.82× -33.8 Top Cut
Glicko-2 predicted 60% 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).
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