#90 Twisted Womxn (4-12)

avg: 103.82  •  sd: 80.39  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
60 Wicked** Loss 1-13 122.92 Ignored Jun 24th Spirit of the Plains
66 Banshee Loss 0-13 25.96 Jun 24th Spirit of the Plains
54 Stellar Loss 7-13 319.41 Jun 24th Spirit of the Plains
29 Pop** Loss 1-13 802.13 Ignored Jun 24th Spirit of the Plains
97 Minnesota Superior A Win 9-8 101.66 Jun 25th Spirit of the Plains
81 Stormborn Win 7-5 619.19 Jun 25th Spirit of the Plains
60 Wicked** Loss 1-13 122.92 Ignored Jul 8th Heavyweights 2023
83 Autonomous Win 12-11 401.44 Jul 8th Heavyweights 2023
62 Dish Loss 4-13 102.78 Jul 8th Heavyweights 2023
93 Freshwater Ultimate Loss 6-10 -450.78 Jul 8th Heavyweights 2023
83 Autonomous Loss 6-11 -270.25 Jul 9th Heavyweights 2023
34 Indy Rogue** Loss 1-13 586.34 Ignored Jul 9th Heavyweights 2023
92 Sureshot Win 10-8 310.49 Jul 9th Heavyweights 2023
60 Wicked** Loss 3-15 122.92 Ignored Sep 9th 2023 Womens West Plains Sectional Championship
66 Banshee Loss 5-15 25.96 Sep 9th 2023 Womens West Plains Sectional Championship
54 Stellar** Loss 5-15 276.94 Ignored Sep 9th 2023 Womens West Plains 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)