#70 Imperial (13-13)

avg: 1030.56  •  sd: 62.27  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
31 Black Market Loss 9-13 928.16 Jun 30th Spirit of the Plains 2018
32 General Strike Loss 7-13 788.97 Jun 30th Spirit of the Plains 2018
39 Mad Men Loss 7-12 758.46 Jun 30th Spirit of the Plains 2018
53 Illusion Loss 5-13 574.08 Jun 30th Spirit of the Plains 2018
120 KC SmokeStack Win 13-2 1311.75 Jul 1st Spirit of the Plains 2018
39 Mad Men Loss 9-10 1153.97 Jul 1st Spirit of the Plains 2018
53 Illusion Win 11-10 1299.08 Jul 1st Spirit of the Plains 2018
123 Satellite Win 11-9 950.36 Aug 4th Heavyweights 2018
146 Dirty D** Win 13-4 1013.23 Ignored Aug 4th Heavyweights 2018
- Kettering** Win 13-1 600 Ignored Aug 4th Heavyweights 2018
39 Mad Men Loss 9-12 933.6 Aug 5th Heavyweights 2018
61 Tanasi Loss 10-11 970.92 Aug 5th Heavyweights 2018
117 THE BODY Win 13-3 1330.94 Aug 5th Heavyweights 2018
132 DingWop Win 15-8 1173.99 Aug 25th Rutabaga Rebellion
72 Swans Win 15-9 1532.34 Aug 25th Rutabaga Rebellion
117 THE BODY Win 15-8 1295.75 Aug 25th Rutabaga Rebellion
59 Mallard Loss 3-7 498.06 Aug 26th Rutabaga Rebellion
59 Mallard Loss 13-15 883.88 Aug 26th Rutabaga Rebellion
72 Swans Win 11-9 1266.07 Aug 26th Rutabaga Rebellion
124 Wisconsin Hops Win 15-8 1261.16 Sep 8th Northwest Plains Mens Sectional Championship 2018
59 Mallard Loss 10-15 644.45 Sep 8th Northwest Plains Mens Sectional Championship 2018
163 Hippie Mafia** Win 15-4 748.52 Ignored Sep 8th Northwest Plains Mens Sectional Championship 2018
59 Mallard Loss 13-15 883.88 Sep 9th Northwest Plains Mens Sectional Championship 2018
47 MKE Loss 10-13 896.23 Sep 9th Northwest Plains Mens Sectional Championship 2018
47 MKE Loss 9-15 708.9 Sep 9th Northwest Plains Mens Sectional Championship 2018
103 houSE Win 15-6 1423.54 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)