Rankings / François Bousquet / Planetary Qualifier Montreal

François Bousquet at Planetary Qualifier Montreal

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Planetary Premier October 19, 2024 Record 5-2-0 Field 119
Rating after 1,642
+142
Rating before 1,500 RD 250
Rating after 1,642 RD 174
Effective multiplier 1.0× weighted avg
Performance 1,794 vs field +291
Field strength Mean 1,503 · Median 1,500 · 117 rated 57th 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
Christopher Lefrancois 2
Lavarennez 1
AndM41 0
eprieur 2
Order514_Damon 2
Pascal Maynard 1
cooldeamon 2
Kal_MythicForce 1
Semifinals
Christopher Lefrancois 2
eprieur 1
Order514_Damon 2
cooldeamon 0
Finals
Christopher Lefrancois 1
Order514_Damon 2
See full bracket on the event page →
Phase Record Raw Δ Multiplier Bonus Applied Running
Swiss · 7 matches 5-2 +142.0 1.0× +0.0 +142.0 1642
Total 5-2-0 +142.0 1642
Biggest upset Beat BillyB 1500 at 50% odds / surprise +0.5
Costliest loss Lost to Order514_Damon 1500 at 50% odds / surprise -0.5 / +170 swing

Matches (7)

Round HRI Opponent Result Odds Game W-L Multiplier Δ Type
R1 ▲ 1500~ BillyB 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).
R2 1500~ Antux09 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 ▼ 1500~ Order514_Damon 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~ JeaD9 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).
R5 1500~ laurent0031 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).
R6 1500~ Louis Gagnon 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~ cooldeamon Loss 50% 1-2 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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