#81 Stormborn (8-14)

avg: 291.05  •  sd: 63.46  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
66 Banshee Loss 6-11 79.27 Jun 24th Spirit of the Plains
97 Minnesota Superior A Win 11-5 576.66 Jun 24th Spirit of the Plains
29 Pop** Loss 1-13 802.13 Ignored Jun 24th Spirit of the Plains
60 Wicked Loss 7-12 202.41 Jun 25th Spirit of the Plains
54 Stellar Loss 4-9 276.94 Jun 25th Spirit of the Plains
90 Twisted Womxn Loss 5-7 -224.33 Jun 25th Spirit of the Plains
41 Heist** Loss 5-13 441.1 Ignored Jul 8th Heavyweights 2023
54 Stellar Loss 8-13 380.78 Jul 8th Heavyweights 2023
92 Sureshot Win 10-8 310.49 Jul 8th Heavyweights 2023
102 Solstice** Win 13-2 281.02 Ignored Jul 8th Heavyweights 2023
62 Dish Loss 5-12 102.78 Jul 9th Heavyweights 2023
93 Freshwater Ultimate Win 13-6 645.38 Jul 9th Heavyweights 2023
76 Medusa Win 11-8 807.83 Jul 9th Heavyweights 2023
93 Freshwater Ultimate Loss 7-8 -79.62 Aug 19th Cooler Classic 34
41 Heist Loss 7-13 483.57 Aug 19th Cooler Classic 34
76 Medusa Win 7-5 770.36 Aug 19th Cooler Classic 34
97 Minnesota Superior A Loss 9-10 -148.34 Aug 19th Cooler Classic 34
62 Dish Loss 5-15 102.78 Aug 20th Cooler Classic 34
104 Minnesota Superior B** Win 15-4 45.49 Ignored Aug 20th Cooler Classic 34
93 Freshwater Ultimate Win 12-7 565.89 Sep 9th 2023 Womens Northwest Plains Sectional Championship
41 Heist** Loss 2-15 441.1 Ignored Sep 9th 2023 Womens Northwest Plains Sectional Championship
76 Medusa Loss 9-12 96.86 Sep 9th 2023 Womens Northwest Plains Sectional Championship
**Blowout Eligible

FAQ

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
  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
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)