#46 DeMo (17-8)

avg: 1563.65  •  sd: 60.71  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
148 Minnesota Superior A Win 13-6 1529.49 Jun 24th Spirit of the Plains
135 Trident II Win 13-7 1581.2 Jun 24th Spirit of the Plains
127 Nomads Win 13-4 1651.02 Jun 24th Spirit of the Plains
219 THE BODY** Win 13-1 1131.59 Ignored Jun 25th Spirit of the Plains
73 Knights of Ni Win 12-9 1731.21 Jun 25th Spirit of the Plains
103 Scythe Win 13-4 1803.08 Jun 25th Spirit of the Plains
24 Blueprint Loss 9-12 1419.15 Jul 29th TCT Select Flight East 2023
110 CITYWIDE Special Win 13-11 1396.59 Jul 29th TCT Select Flight East 2023
29 Mallard Loss 8-11 1362.66 Jul 29th TCT Select Flight East 2023
83 Red Wolves Win 10-9 1472.98 Jul 30th TCT Select Flight East 2023
48 Alamode Loss 14-15 1431.94 Jul 30th TCT Select Flight East 2023
47 Beacon Win 15-9 2078.72 Jul 30th TCT Select Flight East 2023
47 Beacon Win 12-10 1801.36 Aug 19th Cooler Classic 34
17 STL Lounar Loss 10-13 1581.21 Aug 19th Cooler Classic 34
76 Haymaker Win 12-8 1818.31 Aug 19th Cooler Classic 34
96 Bux Win 15-11 1670.03 Aug 20th Cooler Classic 34
36 Kansas City Smokestack Loss 12-15 1341.4 Aug 20th Cooler Classic 34
29 Mallard Loss 7-15 1128.26 Aug 20th Cooler Classic 34
143 STL Moonar Win 15-14 1091.77 Sep 9th 2023 Mens West Plains Sectional Shampionship
36 Kansas City Smokestack Loss 11-13 1413.06 Sep 9th 2023 Mens West Plains Sectional Shampionship
214 Meadowlark** Win 13-3 1148.73 Ignored Sep 9th 2023 Mens West Plains Sectional Shampionship
131 NOx Win 13-6 1638.61 Sep 9th 2023 Mens West Plains Sectional Shampionship
17 STL Lounar Loss 12-15 1608.86 Sep 10th 2023 Mens West Plains Sectional Shampionship
36 Kansas City Smokestack Win 9-8 1766.9 Sep 10th 2023 Mens West Plains Sectional Shampionship
103 Scythe Win 15-5 1803.08 Sep 10th 2023 Mens West Plains Sectional Shampionship
**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)