#66 Iowa Wild Rose (6-16)

avg: 477.26  •  sd: 69.17  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
44 Crackle Loss 7-13 409.21 Jun 30th Spirit of the Plains 2018
44 Crackle Loss 4-11 366.74 Jun 30th Spirit of the Plains 2018
16 Heist** Loss 4-13 1122.08 Ignored Aug 4th Heavyweights 2018
57 Helix Loss 7-8 547.41 Aug 4th Heavyweights 2018
73 Honey Pot Loss 11-12 124.43 Aug 4th Heavyweights 2018
58 Stellar Loss 7-13 105.75 Aug 4th Heavyweights 2018
75 Autonomous Win 8-5 589.59 Aug 5th Heavyweights 2018
50 Cold Cuts Loss 8-11 452.66 Aug 5th Heavyweights 2018
74 MystiKuE Win 10-7 620.77 Aug 5th Heavyweights 2018
55 Sureshot Loss 10-13 366.5 Aug 18th Cooler Classic 30
50 Cold Cuts Loss 8-13 322.11 Aug 18th Cooler Classic 30
44 Crackle Loss 2-13 366.74 Aug 18th Cooler Classic 30
70 Lady Forward Win 13-10 706.1 Aug 18th Cooler Classic 30
34 Dish** Loss 2-15 560.8 Ignored Aug 19th Cooler Classic 30
74 MystiKuE Win 15-11 612.27 Aug 19th Cooler Classic 30
74 MystiKuE Win 11-5 831.1 Aug 19th Cooler Classic 30
16 Heist** Loss 2-15 1122.08 Ignored Sep 22nd North Central Womens Regional Championship 2018
50 Cold Cuts Loss 8-14 282.23 Sep 22nd North Central Womens Regional Championship 2018
44 Crackle Loss 6-10 470.58 Sep 22nd North Central Womens Regional Championship 2018
58 Stellar Win 12-9 1008.65 Sep 23rd North Central Womens Regional Championship 2018
50 Cold Cuts Loss 3-13 218.27 Sep 23rd North Central Womens Regional Championship 2018
44 Crackle Loss 7-10 577.08 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)