#161 Ironside (5-21)

avg: 207.16  •  sd: 81.39  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
170 Bird Patrol Win 13-4 625.47 Jul 21st Layout for Summer 2018
154 Black Market II Win 10-8 569.5 Jul 21st Layout for Summer 2018
119 MomINtuM Loss 3-12 112.34 Jul 21st Layout for Summer 2018
- Illinois Youth Ultimate - u20 Loss 3-8 -75.58 Jul 21st Layout for Summer 2018
154 Black Market II Loss 9-11 57.63 Jul 22nd Layout for Summer 2018
139 Kentucky Flying Circus Loss 2-9 -74.5 Jul 22nd Layout for Summer 2018
- Illinois Youth Ultimate - u20 Loss 6-11 -22.28 Jul 22nd Layout for Summer 2018
- Baemaker Loss 8-13 -201.31 Aug 4th Heavyweights 2018
112 Enigma Loss 4-13 164.44 Aug 4th Heavyweights 2018
111 Cryptic Loss 6-13 166.47 Aug 4th Heavyweights 2018
120 KC SmokeStack Loss 7-13 154.22 Aug 5th Heavyweights 2018
163 Hippie Mafia Loss 6-10 -347.64 Aug 5th Heavyweights 2018
- Kettering Win 13-2 600 Ignored Aug 5th Heavyweights 2018
31 Black Market** Loss 5-13 746.73 Ignored Aug 25th The Bropen 2018
48 Four** Loss 3-13 616.08 Ignored Aug 25th The Bropen 2018
47 MKE** Loss 1-13 624.38 Ignored Aug 25th The Bropen 2018
53 Illusion** Loss 3-13 574.08 Ignored Aug 25th The Bropen 2018
121 BlackER Market Loss 5-13 106.46 Aug 26th The Bropen 2018
103 houSE** Loss 3-13 223.54 Ignored Aug 26th The Bropen 2018
132 DingWop Loss 5-13 9.19 Sep 8th Northwest Plains Mens Sectional Championship 2018
32 General Strike** Loss 4-13 746.5 Ignored Sep 8th Northwest Plains Mens Sectional Championship 2018
72 Swans** Loss 2-13 416.86 Ignored Sep 8th Northwest Plains Mens Sectional Championship 2018
103 houSE Loss 6-12 244.23 Sep 8th Northwest Plains Mens Sectional Championship 2018
124 Wisconsin Hops Win 12-11 821.35 Sep 9th Northwest Plains Mens Sectional Championship 2018
152 Green Bay Quackers Loss 11-12 207.36 Sep 9th Northwest Plains Mens Sectional Championship 2018
163 Hippie Mafia Win 15-6 748.52 Sep 9th Northwest Plains Mens Sectional Championship 2018
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)