Rankings / ShazamPowers / Planetary Qualifier Yuma

ShazamPowers at Planetary Qualifier Yuma

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Planetary Limited April 25, 2026 Record 4-2-0 Field 43
Rating after 1,639
+82
Rating before 1,557 RD 152
Rating after 1,639 RD 132
Effective multiplier 1.0× weighted avg
Performance 1,864 vs field +289
Field strength Mean 1,575 · Median 1,552 · 42 rated 46th 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
Quarterfinals
Spencer Freeman 2
JonahBR 0
llopez101 2
_ODIN_WAN_KENOBI_ 1
CBG_Taylor 2
CheeseKahuna 0
Arteiris 2
CBG_Ruben 1
Semifinals
Spencer Freeman 2
CBG_Taylor 0
llopez101 2
Arteiris 0
Finals
Spencer Freeman 2
llopez101 1
See full bracket on the event page →
Phase Record Raw Δ Multiplier Bonus Applied Running
Swiss · 6 matches 4-2 +81.9 1.0× +0.0 +81.9 1639
Total 4-2-0 +81.9 1639
Biggest upset Beat tehPyro 1961 at 20% odds / surprise +0.8
Costliest loss Lost to _ODIN_WAN_KENOBI_ 1500 at 54% odds / surprise -0.54 / +69 swing

Matches (6)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 1676~ Kazrig Win 40% 2-0 1.0× +43.8 Swiss
Glicko-2 predicted only 40% 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 152, true skill could span ±304).
R2 1438~ Beeb Win 60% 2-0 1.0× +29.7 Swiss
R3 ▼ 1500~ _ODIN_WAN_KENOBI_ Loss 54% 0-2 1.0× -37.7 Swiss
Glicko-2 predicted 54% 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 ▲ 1961 tehPyro Win 20% 2-1 1.0× +60.3 Swiss
Glicko-2 predicted only 20% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.
R5 1588~ JonahBR Loss 47% 1-2 1.0× -34.7 Swiss
R6 1675~ Nauk Win 40% 2-1 1.0× +43.7 Swiss
Glicko-2 predicted only 40% 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 152, true skill could span ±304).

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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