#50 Drift (9-13)

avg: 914.94  •  sd: 42.35  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
27 Underground Loss 6-9 1007.35 Jun 24th Summer Solstice 2023
51 Seven Devils Win 11-7 1374.08 Jun 24th Summer Solstice 2023
33 Seattle END Loss 7-11 807.83 Jun 24th Summer Solstice 2023
77 Portland Rain Check Win 11-6 957.11 Jun 24th Summer Solstice 2023
28 Oregon Downpour Loss 8-13 912.47 Jun 25th Summer Solstice 2023
36 remix Loss 10-12 928.6 Jun 25th Summer Solstice 2023
22 Siege** Loss 2-15 941.48 Ignored Jul 29th TCT Select Flight East 2023
80 Notorious C.L.E. Win 10-6 799.14 Jul 29th TCT Select Flight East 2023
34 Indy Rogue Loss 6-14 586.34 Jul 29th TCT Select Flight East 2023
43 Zephyr Loss 7-12 504.78 Jul 30th TCT Select Flight East 2023
66 Banshee Win 12-8 1067.12 Jul 30th TCT Select Flight East 2023
55 Shiver Win 9-8 995.65 Jul 30th TCT Select Flight East 2023
27 Underground Loss 7-15 825.92 Sep 9th 2023 Womens Washington Sectional Championship
84 Seattle Soul** Win 15-2 869.19 Ignored Sep 9th 2023 Womens Washington Sectional Championship
33 Seattle END Loss 11-14 961.39 Sep 9th 2023 Womens Washington Sectional Championship
77 Portland Rain Check Win 15-5 1010.41 Sep 10th 2023 Womens Washington Sectional Championship
53 Hucklebears Win 14-13 1002.79 Sep 10th 2023 Womens Washington Sectional Championship
19 Dark Sky** Loss 3-13 981.24 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
9 Schwa** Loss 0-13 1501.71 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
33 Seattle END Loss 10-13 946.58 Sep 23rd 2023 Northwest Womens Regional Championship
77 Portland Rain Check Win 13-6 1010.41 Sep 23rd 2023 Northwest Womens Regional Championship
27 Underground Loss 7-15 825.92 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)