#80 Notorious C.L.E. (10-12)

avg: 302.98  •  sd: 86.73  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
32 Crush City** Loss 3-15 713.39 Ignored Jul 29th TCT Select Flight East 2023
50 Drift Loss 6-10 418.78 Jul 29th TCT Select Flight East 2023
41 Heist** Loss 1-15 441.1 Ignored Jul 29th TCT Select Flight East 2023
83 Autonomous Loss 9-12 -68.92 Jul 30th TCT Select Flight East 2023
66 Banshee Loss 9-15 110.48 Jul 30th TCT Select Flight East 2023
73 Incline Win 11-4 1070.29 Aug 19th Motown Throwdown 2023
73 Incline Win 8-6 770.78 Aug 19th Motown Throwdown 2023
92 Sureshot Win 10-3 647.83 Aug 19th Motown Throwdown 2023
102 Solstice** Win 12-1 281.02 Ignored Aug 19th Motown Throwdown 2023
96 Y'all Win 8-6 296.13 Sep 9th 2023 Womens East Plains Sectional Championship
83 Autonomous Loss 6-8 -24.05 Sep 9th 2023 Womens East Plains Sectional Championship
31 Rival** Loss 2-13 766.44 Ignored Sep 9th 2023 Womens East Plains Sectional Championship
102 Solstice Win 11-6 227.72 Sep 9th 2023 Womens East Plains Sectional Championship
83 Autonomous Loss 10-11 151.44 Sep 10th 2023 Womens East Plains Sectional Championship
92 Sureshot Win 9-8 172.83 Sep 10th 2023 Womens East Plains Sectional Championship
92 Sureshot Loss 5-9 -481.23 Sep 10th 2023 Womens East Plains Sectional Championship
34 Indy Rogue** Loss 4-15 586.34 Ignored Sep 23rd 2023 Great Lakes Womens Regional Championship
12 Nemesis** Loss 1-15 1331.46 Ignored Sep 23rd 2023 Great Lakes Womens Regional Championship
102 Solstice** Win 15-6 281.02 Ignored Sep 23rd 2023 Great Lakes Womens Regional Championship
96 Y'all Win 15-6 595.64 Sep 24th 2023 Great Lakes Womens Regional Championship
83 Autonomous Win 13-10 604.59 Sep 24th 2023 Great Lakes Womens Regional Championship
62 Dish Loss 6-15 102.78 Sep 24th 2023 Great Lakes Womens Regional 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)