Rankings / Sturg / Planetary Qualifier Yuma

Sturg at Planetary Qualifier Yuma

melee.gg
Planetary Limited April 25, 2026 Record 2-4-0 Field 43
Rating after 1,399
-101
Rating before 1,500 RD 250
Rating after 1,399 RD 178
Effective multiplier 1.0× weighted avg
Performance 1,306 vs field -269
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 2-4 -101.1 1.0× +0.0 -101.1 1399
Total 2-4-0 -101.1 1399
Biggest upset Beat WHL824290 1500 at 50% odds / surprise +0.5
Costliest loss Lost to SYC_SailorJoeV 1401 at 58% odds / surprise -0.58 / +179 swing

Matches (6)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 ▼ 1401~ SYC_SailorJoeV Loss 58% 0-2 1.0× -104.5 Swiss
Glicko-2 predicted 58% 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 139, true skill could span ±278).
R2 ▲ 1500~ WHL824290 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).
R3 1631~ CaptainFTB Loss 39% 0-2 1.0× -70.3 Swiss
R4 1436~ MagicalSinner Loss 55% 0-2 1.0× -99.6 Swiss
Glicko-2 predicted 55% 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 126, true skill could span ±251).
R5 1500~ CSteel 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).
R6 1676~ Kazrig Loss 36% 0-2 1.0× -64.5 Swiss

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.

// keyboard

Shortcuts

Focus search
/
Next row
j
Previous row
k
Open focused row
Enter
Show / hide this panel
?
Close dialogs
Esc

j / k navigate rows on the leaderboard and tournaments index. Arrow keys work too.