SC_louloup at Planetary Qualifier Paris
melee.gg
Rating before
1,453
RD 174
Rating after
1,620
RD 139
Effective multiplier
0.98×
weighted avg
Performance
1,850
vs field
Field strength
Mean 1,514
· Median 1,500
· 118 rated
95th Percentile Planetaries, 2024 Q4
Biggest upset
Beat Phoenixton
at 46% odds
/
surprise +0.54
Costliest loss
Lost to NO_Jeremew
at 60% odds
/
surprise -0.6
/
+87 swing
Matches (8)
Round
HRI
Opponent
Result
Odds
Game W-L
Multiplier
Δ
Type
Glicko-2 predicted only 46% 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).
Glicko-2 predicted only 46% 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).
Glicko-2 predicted only 46% 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).
Glicko-2 predicted only 46% 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).
Glicko-2 predicted only 46% 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).
Glicko-2 predicted only 46% 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).
Glicko-2 predicted 60% 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).