#51 Seven Devils (8-11)

avg: 907.19  •  sd: 58.3  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
27 Underground Loss 1-11 825.92 Jun 24th Summer Solstice 2023
50 Drift Loss 7-11 448.05 Jun 24th Summer Solstice 2023
77 Portland Rain Check Win 11-4 1010.41 Jun 24th Summer Solstice 2023
33 Seattle END Loss 7-9 995.39 Jun 24th Summer Solstice 2023
38 FAB Loss 6-13 519.26 Jun 25th Summer Solstice 2023
84 Seattle Soul** Win 13-3 869.19 Ignored Jun 25th Summer Solstice 2023
20 Wildfire Loss 6-11 1013.92 Aug 19th Ski Town Classic 2023
87 Haboob** Win 13-1 756.71 Ignored Aug 19th Ski Town Classic 2023
100 Just Add Water** Win 13-2 418.85 Ignored Aug 19th Ski Town Classic 2023
52 Void Cat Rewind Win 11-6 1440.28 Aug 20th Ski Town Classic 2023
20 Wildfire Loss 6-13 960.61 Aug 20th Ski Town Classic 2023
32 Crush City Loss 8-12 872.24 Aug 20th Ski Town Classic 2023
19 Dark Sky Loss 7-15 981.24 Sep 9th 2023 Womens Big Sky Sectional Championship
79 Swell Win 15-3 949.65 Sep 9th 2023 Womens Big Sky Sectional Championship
27 Underground Loss 3-13 825.92 Sep 23rd 2023 Northwest Womens Regional Championship
10 Traffic** Loss 2-13 1501.06 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
84 Seattle Soul** Win 13-2 869.19 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
56 Eugene Further Win 10-9 970.32 Sep 23rd 2023 Northwest Womens Regional Championship
33 Seattle END Loss 8-15 709.92 Sep 24th 2023 Northwest 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)