#76 Medusa (8-8)

avg: 442.22  •  sd: 64.25  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
41 Heist Loss 6-13 441.1 Jul 8th Heavyweights 2023
54 Stellar Loss 8-11 511.33 Jul 8th Heavyweights 2023
92 Sureshot Win 13-4 647.83 Jul 8th Heavyweights 2023
102 Solstice Win 11-8 46.63 Jul 8th Heavyweights 2023
62 Dish Loss 5-8 249.18 Jul 9th Heavyweights 2023
93 Freshwater Ultimate Win 10-5 619.28 Jul 9th Heavyweights 2023
81 Stormborn Loss 8-11 -74.56 Jul 9th Heavyweights 2023
93 Freshwater Ultimate Win 14-2 645.38 Aug 19th Cooler Classic 34
81 Stormborn Loss 5-7 -37.09 Aug 19th Cooler Classic 34
104 Minnesota Superior B** Win 15-4 45.49 Ignored Aug 19th Cooler Classic 34
62 Dish Loss 8-9 577.78 Aug 20th Cooler Classic 34
41 Heist Loss 5-10 467.2 Aug 20th Cooler Classic 34
97 Minnesota Superior A Win 11-6 523.36 Aug 20th Cooler Classic 34
93 Freshwater Ultimate Win 13-7 602.91 Sep 9th 2023 Womens Northwest Plains Sectional Championship
41 Heist Loss 7-15 441.1 Sep 9th 2023 Womens Northwest Plains Sectional Championship
81 Stormborn Win 12-9 636.41 Sep 9th 2023 Womens Northwest 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)