Rankings / EM_Fenix / Planetary Qualifier Madrid Fuenlabrada

EM_Fenix at Planetary Qualifier Madrid Fuenlabrada

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Planetary Premier February 15, 2025 Record 1-6-0 Field 124
Rating after 1,273
-227
Rating before 1,500 RD 250
Rating after 1,273 RD 174
Effective multiplier 1.0× weighted avg
Performance 950 vs field -558
Field strength Mean 1,508 · Median 1,500 · 124 rated 19th Percentile Planetaries, 2025 Q1

// 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
Quarterfinals
tarkin84 2
Xexu 1
Durgan 0
Miguel Angel Utiel Peñaranda 2
Alfonso Bueno 1
2183d7804da94cf1990e5dc3a6e5bf63 2
Docnicka 1
shadowblade 2
Semifinals
tarkin84 2
Miguel Angel Utiel Peñaranda 0
2183d7804da94cf1990e5dc3a6e5bf63 0
shadowblade 2
Finals
tarkin84 0
shadowblade 2
See full bracket on the event page →
Phase Record Raw Δ Multiplier Bonus Applied Running
Swiss · 7 matches 1-6 -226.9 1.0× +0.0 -226.9 1273
Total 1-6-0 -226.9 1273
Biggest upset Beat Minho 1500 at 50% odds / surprise +0.5
Costliest loss Lost to lazeford 1500 at 50% odds / surprise -0.5 / +170 swing

Matches (7)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 ▼ 1500~ lazeford 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).
R2 1642~ Saibot Loss 39% 0-2 1.0× -68.5 Swiss
R3 ▲ 1500~ Minho 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).
R4 1500~ TGB_Mariotelli 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).
R5 1500~ vitovik Loss 50% 0-2 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).
R6 1500~ gonzomerl 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).
R7 1500~ RafexNewGaia 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).

Per-match multipliers reflect what the engine applied at the time this snapshot was recorded. The Effective Multiplier above is a delta-weighted average across all phases — Finals matches swing the headline more than Swiss rounds when they produced bigger rating changes. How it works.

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