#30 Steel (14-4)

avg: 1348.29  •  sd: 94.36  •  top 16/20: 0.1%

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# Opponent Result Game Rating Status Date Event
88 Honey Pot** Win 11-4 835.43 Ignored Jul 6th Huntsville Huckfest 2019
88 Honey Pot** Win 11-0 835.43 Ignored Jul 6th Huntsville Huckfest 2019
60 Huntsville Laika Win 10-3 1410.8 Jul 6th Huntsville Huckfest 2019
60 Huntsville Laika Win 11-4 1410.8 Jul 6th Huntsville Huckfest 2019
88 Honey Pot** Win 13-0 835.43 Ignored Jul 20th 2019 Club Terminus
60 Huntsville Laika Win 13-3 1410.8 Jul 20th 2019 Club Terminus
50 Crush City Win 13-7 1556.84 Jul 20th 2019 Club Terminus
46 Queen Cake Win 12-8 1512.12 Jul 20th 2019 Club Terminus
60 Huntsville Laika Win 13-4 1410.8 Jul 21st 2019 Club Terminus
50 Crush City Win 13-5 1599.31 Jul 21st 2019 Club Terminus
60 Huntsville Laika Win 15-6 1410.8 Sep 7th Gulf Coast Womens Club Sectional Championship 2019
46 Queen Cake Loss 10-12 832.84 Sep 7th Gulf Coast Womens Club Sectional Championship 2019
49 Fiasco Win 15-12 1325.6 Sep 21st Southeast Club Womens Regional Championship 2019
46 Queen Cake Win 12-7 1591.47 Sep 21st Southeast Club Womens Regional Championship 2019
15 Ozone Loss 7-15 1126.66 Sep 21st Southeast Club Womens Regional Championship 2019
7 Phoenix Loss 6-13 1451.86 Sep 22nd Southeast Club Womens Regional Championship 2019
46 Queen Cake Win 12-9 1416.33 Sep 22nd Southeast Club Womens Regional Championship 2019
23 Tabby Rosa Loss 9-13 1077.06 Sep 22nd Southeast Club Womens Regional 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)