#27 Elevate (8-12)

avg: 1433.54  •  sd: 74.09  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
10 Traffic Loss 9-12 1591.87 Jun 22nd Eugene Summer Solstice 2019
30 Colorado Small Batch Loss 7-10 1008.04 Jun 22nd Eugene Summer Solstice 2019
9 Nightlock Loss 4-13 1348.3 Jun 22nd Eugene Summer Solstice 2019
1 Fury** Loss 1-13 1902.24 Ignored Jun 22nd Eugene Summer Solstice 2019
25 Sneaky House Hippos Loss 6-13 862.74 Jun 23rd Eugene Summer Solstice 2019
34 PDXtra Win 13-11 1590.64 Jun 23rd Eugene Summer Solstice 2019
38 FAB Loss 8-10 953.09 Jun 23rd Eugene Summer Solstice 2019
33 Heist Win 15-14 1496.72 Jul 27th TCT Select Flight Invite East 2019
11 Siege Loss 7-13 1356.16 Jul 27th TCT Select Flight Invite East 2019
38 FAB Win 13-8 1711.92 Jul 27th TCT Select Flight Invite East 2019
22 Tabby Rosa Loss 10-12 1319.57 Jul 27th TCT Select Flight Invite East 2019
30 Colorado Small Batch Loss 7-10 1008.04 Jul 28th TCT Select Flight Invite East 2019
39 Stella Win 11-9 1461.11 Jul 28th TCT Select Flight Invite East 2019
15 Nemesis Win 12-10 2002.1 Aug 17th TCT Elite Select Challenge 2019
11 Siege Loss 9-15 1398.21 Aug 17th TCT Elite Select Challenge 2019
20 Pop Win 12-11 1758.37 Aug 17th TCT Elite Select Challenge 2019
18 Underground Loss 7-8 1571.48 Aug 18th TCT Elite Select Challenge 2019
21 Grit Win 8-7 1738.28 Aug 18th TCT Elite Select Challenge 2019
15 Nemesis Loss 4-13 1163.98 Aug 18th TCT Elite Select Challenge 2019
82 Seven Devils** Win 15-6 900.21 Ignored Sep 7th Big Sky Womens Club Sectional Championship 2019
**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)