Rankings / STK_Alexandre / Planetary Qualifier Sao Paulo

STK_Alexandre at Planetary Qualifier Sao Paulo

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Planetary Premier January 05, 2025 Record 2-3-0 Field 87
Rating after 1,444
-56
Rating before 1,500 RD 250
Rating after 1,444 RD 189
Effective multiplier 1.0× weighted avg
Performance 1,372 vs field -128
Field strength Mean 1,500 · Median 1,500 · 87 rated 7th 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
GustavoKenzo 2
taldejamal 0
Kaladesh 0
thicorfon 2
Viveiros 2
AlexHiro1996 1
AlexandreMeneghesso 0
Rafael Navarro 2
Semifinals
GustavoKenzo 2
thicorfon 1
Viveiros 0
Rafael Navarro 2
Finals
GustavoKenzo 1
Rafael Navarro 2
See full bracket on the event page →
Phase Record Raw Δ Multiplier Bonus Applied Running
Swiss · 5 matches 2-3 -55.5 1.0× +0.0 -55.5 1444
Total 2-3-0 -55.5 1444
Biggest upset Beat Baramallo 1500 at 50% odds / surprise +0.5
Costliest loss Lost to GuilhermeAlcantara 1500 at 50% odds / surprise -0.5 / +170 swing

Matches (5)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 ▼ 1500~ GuilhermeAlcantara 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).
R2 ▲ 1500~ Baramallo 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~ TDBst_Nomad0731 Win 50% 2-0 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~ Arthur Russo 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).
R5 1500~ Antonio Zanutto 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).

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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