#52 Mallard (12-7)

avg: 1329.58  •  sd: 52.96  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
56 Scythe Win 13-11 1516.9 Jun 29th Spirit of the Plains 2019
25 General Strike Loss 9-13 1146.33 Jun 29th Spirit of the Plains 2019
130 Kansas City Smokestack Win 12-10 1134.76 Jun 29th Spirit of the Plains 2019
102 THE BODY Win 12-5 1629.14 Jun 30th Spirit of the Plains 2019
20 Yogosbo Loss 8-13 1236.88 Jun 30th Spirit of the Plains 2019
60 Swans Win 10-7 1660.94 Jun 30th Spirit of the Plains 2019
34 Mad Men Loss 6-13 874.41 Jul 13th The Bropen 2019
87 Red Hots - u20 Win 13-10 1442.16 Jul 13th The Bropen 2019
175 Milwaukee Revival** Win 13-5 1237.91 Ignored Jul 13th The Bropen 2019
100 Timber Win 13-6 1641.48 Jul 13th The Bropen 2019
34 Mad Men Win 12-11 1599.41 Jul 14th The Bropen 2019
94 Minnesota Superior U20B Win 13-7 1628.91 Jul 14th The Bropen 2019
20 Yogosbo Loss 6-13 1133.03 Jul 14th The Bropen 2019
41 MKE Loss 9-13 992.27 Sep 7th Northwest Plains Mens Club Sectional Championship 2019
100 Timber Win 12-10 1279.6 Sep 7th Northwest Plains Mens Club Sectional Championship 2019
95 HouSE Win 12-9 1415.36 Sep 7th Northwest Plains Mens Club Sectional Championship 2019
20 Yogosbo Loss 10-13 1404.89 Sep 8th Northwest Plains Mens Club Sectional Championship 2019
175 Milwaukee Revival** Win 13-5 1237.91 Ignored Sep 8th Northwest Plains Mens Club Sectional Championship 2019
60 Swans Loss 13-14 1146.27 Sep 8th Northwest Plains Mens 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)