#102 The Matriarchy (1-10)

avg: -365.15  •  sd: 116.08  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
72 Helix** Loss 4-13 33.8 Ignored Jun 29th Spirit of the Plains 2019
53 Stellar** Loss 3-13 372.24 Ignored Jun 29th Spirit of the Plains 2019
103 Superior Win 8-7 -365.15 Jun 29th Spirit of the Plains 2019
76 Iowa Wild Rose Loss 3-6 -66.93 Jun 30th Spirit of the Plains 2019
91 MystiKuE Loss 5-9 -347.77 Jun 30th Spirit of the Plains 2019
62 Trainwreck** Loss 1-13 169.67 Ignored Aug 17th Cooler Classic 31
91 MystiKuE Loss 3-11 -418.71 Aug 17th Cooler Classic 31
53 Stellar** Loss 0-13 372.24 Ignored Aug 17th Cooler Classic 31
90 Sureshot Loss 1-13 -418.44 Aug 17th Cooler Classic 31
87 Cold Cuts** Loss 0-11 -363.1 Ignored Aug 18th Cooler Classic 31
91 MystiKuE Loss 2-11 -418.71 Aug 18th Cooler Classic 31
**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)