#204 Loaded Panda (5-15)

avg: 642.29  •  sd: 68.46  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
135 Trident II Loss 6-12 444.36 Jun 18th Spirit of the Plains
127 Nomads Win 11-9 1300.23 Jun 18th Spirit of the Plains
147 DINGWOP Loss 5-13 340.12 Jun 24th Spirit of the Plains
233 BeMo Win 12-10 649.02 Jun 24th Spirit of the Plains
192 Minnesota Superior B Loss 8-10 465.59 Jun 24th Spirit of the Plains
103 Scythe Loss 6-12 623.77 Jun 24th Spirit of the Plains
73 Knights of Ni Loss 8-13 889.69 Jun 25th Spirit of the Plains
147 DINGWOP Loss 7-11 473.22 Aug 19th Cooler Classic 34
179 Timber Loss 12-13 685.55 Aug 19th Cooler Classic 34
106 MKE Loss 5-13 582.41 Aug 19th Cooler Classic 34
191 DCVIII Win 13-8 1231.74 Aug 19th Cooler Classic 34
219 THE BODY Loss 11-14 218.25 Aug 20th Cooler Classic 34
179 Timber Loss 11-12 685.55 Aug 20th Cooler Classic 34
241 Middleton High School Win 13-4 849.82 Aug 20th Cooler Classic 34
17 STL Lounar** Loss 3-13 1309.35 Ignored Sep 9th 2023 Mens West Plains Sectional Shampionship
143 STL Moonar Loss 8-13 470.61 Sep 9th 2023 Mens West Plains Sectional Shampionship
103 Scythe Loss 10-13 874.94 Sep 9th 2023 Mens West Plains Sectional Shampionship
131 NOx Loss 5-14 438.61 Sep 10th 2023 Mens West Plains Sectional Shampionship
131 NOx Loss 5-15 438.61 Sep 10th 2023 Mens West Plains Sectional Shampionship
214 Meadowlark Win 12-11 673.73 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)