#72 Helix (13-8)

avg: 633.8  •  sd: 59.03  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
102 The Matriarchy** Win 13-4 234.85 Ignored Jun 29th Spirit of the Plains 2019
53 Stellar Loss 8-13 476.08 Jun 29th Spirit of the Plains 2019
87 Cold Cuts Win 8-4 801.71 Jun 30th Spirit of the Plains 2019
60 Crackle Loss 3-8 233.07 Jun 30th Spirit of the Plains 2019
31 Fusion** Loss 2-12 783.04 Ignored Jun 30th Spirit of the Plains 2019
53 Stellar Win 8-6 1272.73 Jun 30th Spirit of the Plains 2019
87 Cold Cuts Win 13-9 655.47 Aug 3rd Heavyweights 2019
106 Frenzy** Win 13-3 600 Ignored Aug 3rd Heavyweights 2019
91 MystiKuE Win 12-10 419.41 Aug 3rd Heavyweights 2019
76 Iowa Wild Rose Loss 10-11 354.76 Aug 3rd Heavyweights 2019
75 Viva Win 11-4 1161.06 Aug 4th Heavyweights 2019
31 Fusion** Loss 3-13 783.04 Aug 4th Heavyweights 2019
84 Autonomous Win 10-9 420.43 Aug 24th Indy Invite Club 2019
85 Lady Forward Win 10-9 382.2 Aug 24th Indy Invite Club 2019
90 Sureshot Win 13-7 739.1 Aug 24th Indy Invite Club 2019
63 Huntsville Laika Win 11-10 890.09 Aug 24th Indy Invite Club 2019
90 Sureshot Win 13-9 600.13 Aug 25th Indy Invite Club 2019
63 Huntsville Laika Loss 10-15 311.48 Aug 25th Indy Invite Club 2019
55 Dish Loss 9-10 750.68 Sep 7th Central Plains Womens Club Sectional Championship 2019
106 Frenzy** Win 13-2 600 Ignored Sep 7th Central Plains Womens Club Sectional Championship 2019
46 Indy Rogue Loss 9-13 681.6 Sep 7th Central Plains 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)