#58 Stellar (9-10)

avg: 663.29  •  sd: 78.49  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
57 Helix Loss 9-11 423.2 Jun 16th SCINNY 2018
55 Sureshot Loss 9-11 445.43 Jun 16th SCINNY 2018
31 Indy Rogue Loss 1-13 603.99 Jun 16th SCINNY 2018
75 Autonomous Win 13-5 735.98 Jun 16th SCINNY 2018
- Belle Win 12-9 582.94 Jun 24th SCINNY 2018
70 Lady Forward Loss 11-12 252.96 Jun 24th SCINNY 2018
16 Heist** Loss 2-13 1122.08 Ignored Aug 4th Heavyweights 2018
73 Honey Pot Win 13-8 745.59 Aug 4th Heavyweights 2018
74 MystiKuE Win 13-4 831.1 Aug 4th Heavyweights 2018
66 Iowa Wild Rose Win 13-7 1034.8 Aug 4th Heavyweights 2018
37 Fiasco Loss 9-10 971.58 Aug 5th Heavyweights 2018
57 Helix Win 12-9 1017.78 Aug 5th Heavyweights 2018
34 Dish Loss 3-9 560.8 Aug 5th Heavyweights 2018
24 Wicked** Loss 1-11 892.17 Ignored Sep 22nd North Central Womens Regional Championship 2018
19 Pop** Loss 3-11 1079.32 Ignored Sep 22nd North Central Womens Regional Championship 2018
70 Lady Forward Win 10-9 502.96 Sep 22nd North Central Womens Regional Championship 2018
74 MystiKuE Win 11-3 831.1 Sep 22nd North Central Womens Regional Championship 2018
66 Iowa Wild Rose Loss 9-12 131.9 Sep 23rd North Central Womens Regional Championship 2018
74 MystiKuE Win 15-4 831.1 Sep 23rd North Central Womens Regional Championship 2018
**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)