Rankings / ORA_KillMathy / Planetary Qualifier Toronto

ORA_KillMathy at Planetary Qualifier Toronto

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Planetary Premier January 12, 2025 Record 4-3-0 Field 103
Rating after 1,537
+37
Rating before 1,500 RD 250
Rating after 1,537 RD 174
Effective multiplier 1.0× weighted avg
Performance 1,569 vs field +71
Field strength Mean 1,498 · Median 1,500 · 103 rated 6th 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
Largewoody 2
SidW 0
JamesUgly 2
drimas 1
Krehlo_MythicForce 2
Alex O’Shea 1
CST_CaptainTarpals 2
Byrus_MythicForce 1
Semifinals
Largewoody 2
JamesUgly 1
Krehlo_MythicForce 2
CST_CaptainTarpals 1
Finals
Krehlo_MythicForce 2
Largewoody 1
See full bracket on the event page →
Phase Record Raw Δ Multiplier Bonus Applied Running
Swiss · 7 matches 4-3 +36.5 1.0× +0.0 +36.5 1537
Total 4-3-0 +36.5 1537
Biggest upset Beat Bond3114 1500 at 50% odds / surprise +0.5
Costliest loss Lost to SidW 1500 at 50% odds / surprise -0.5 / +170 swing

Matches (7)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 ▲ 1500~ Bond3114 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 1331~ Nick Sementelli Win 64% 2-0 1.0× +65.4 Swiss
R3 ▼ 1500~ SidW 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).
R4 1500~ Raskazz 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~ CardsMcGee 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~ GnomeGarden 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).
R7 1500~ DarK3 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).

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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