Rankings / nWo_BoboWanKenobi / Planetary Qualifier Idstein

nWo_BoboWanKenobi at Planetary Qualifier Idstein

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Planetary Premier October 27, 2024 Record 5-3-0 Field 83
Rating after 1,674
+174
Rating before 1,500 RD 250
Rating after 1,674 RD 169
Effective multiplier 0.97× weighted avg
Performance 1,775 vs field +264
Field strength Mean 1,511 · Median 1,500 · 83 rated 81st 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
Quarterfinals
Jadimi_TCT 2
nWo_BoboWanKenobi 1
FatAlError 2
Jorit 1
T85Challenge 2
ChrisCunning 0
Django84_TcT 1
Lipizzaner 2
Semifinals
Jadimi_TCT 2
FatAlError 0
T85Challenge 2
Lipizzaner 0
Finals
Jadimi_TCT 0
T85Challenge 2
See full bracket on the event page →
Phase Record Raw Δ Multiplier Bonus Applied Running
Swiss · 7 matches 5-2 +185.3 1.0× +0.0 +185.3 1685
Quarterfinals · 1 match 0-1 -31.7 0.82× 1.00 · -0.18 +5.7 -26.0 1652
Top-cut bonus Tier-scaled additive ‧ Planetary at 83 +15.0 1667
Total 5-3-0 +174.3 1674
Biggest upset Beat Oscurita 1532 at 47% odds / surprise +0.53
Costliest loss Lost to Jadimi_TCT 1775 at 43% odds / surprise -0.43 / +189 swing

Matches (8)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 1500~ Lars2107 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).
R2 1500~ Cash_Payne 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~ Firebrand 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 1775~ Jadimi_TCT Loss 29% 0-2 1.0× -52.3 Swiss
R5 1775~ Django84_TcT Loss 29% 0-2 1.0× -52.3 Swiss
R6 ▲ 1532~ Oscurita Win 47% 2-0 1.0× +94.1 Swiss
Glicko-2 predicted only 47% 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 142, true skill could span ±284).
R7 1500~ RPGKuchen 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).
QF ▼ 1775~ Jadimi_TCT Loss 43% 1-2 0.82× -42.9 Top Cut

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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