#204 New Orleans Boil (6-10)

avg: 504.14  •  sd: 50.89  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
93 Crown Peach Loss 5-11 478.61 Jul 8th Club Terminus 2023
87 m'kay Ultimate** Loss 3-12 519.04 Ignored Jul 8th Club Terminus 2023
238 Victrix Win 11-6 720.84 Jul 8th Club Terminus 2023
89 B-Unit Loss 5-13 502.7 Jul 9th Club Terminus 2023
238 Victrix Win 13-12 299.15 Jul 9th Club Terminus 2023
171 Happy Hour Loss 8-13 206.72 Jul 9th Club Terminus 2023
239 Central Arkansas Surge Win 10-8 432.67 Aug 26th Ragna Rock 2023
142 Goosebumps Loss 8-10 580.95 Aug 26th Ragna Rock 2023
251 Trophic Cascade** Win 11-3 378.17 Ignored Aug 26th Ragna Rock 2023
123 Amber Loss 8-10 701.97 Aug 27th Ragna Rock 2023
222 LRU Win 10-6 836.06 Aug 27th Ragna Rock 2023
220 Hairy Otter Win 11-6 921.96 Aug 27th Ragna Rock 2023
123 Amber Loss 7-15 364.64 Sep 9th 2023 Mixed Gulf Coast Sectional Championship
126 Barefoot Loss 8-15 366.89 Sep 9th 2023 Mixed Gulf Coast Sectional Championship
153 Memphis STAX Loss 11-14 482.2 Sep 9th 2023 Mixed Gulf Coast Sectional Championship
153 Memphis STAX Loss 7-15 195.54 Sep 10th 2023 Mixed Gulf Coast Sectional Championship
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
What's the algorithm again?
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
Is there a longer explanation of all this?
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)