ECL_Mickeyd at Planetary Qualifier Chaska

melee.gg
Planetary Premier October 13, 2024 Record 6-2-0 Field 116
Rating after 1,677
+177
Rating before 1,500 RD 197
Rating after 1,677 RD 150
Effective multiplier 0.98× weighted avg
Performance 1,880 vs field +372
Field strength Mean 1,508 · Median 1,500 · 116 rated 76th 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 +183.5 1.0× +0.0 +183.5 1683
Quarterfinals · 1 match 0-1 -26.3 0.82× 1.00 · -0.18 +4.7 -21.6 1655
Top-cut bonus Tier-scaled additive ‧ Planetary at 116 +15.0 1670
Total 6-2-0 +176.9 1677
Biggest upset Beat TheFiremind 1500 at 50% odds / surprise +0.5
Costliest loss Lost to Tyler Studer 1500 at 50% odds / surprise -0.5 / +112 swing

Matches (8)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 1500~ TheFiremind Win 50% 2-0 1.0× +55.9 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).
R2 1500~ MND_thetripofcj Win 50% 2-1 1.0× +55.9 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~ Tyler Studer Loss 50% 0-2 1.0× -55.9 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 1500~ Goggleman2323 Win 50% 2-0 1.0× +55.9 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).
R5 1500~ LukeReplicaDroid Win 50% 2-1 1.0× +55.9 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).
R6 1500~ Connor Paulsen Win 50% 2-0 1.0× +55.9 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).
R7 1500~ TCGrami Win 50% 2-0 1.0× +55.9 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).
QF 1714~ Junior64 Loss 48% 1-2 0.82× -32.1 Top Cut
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