#208 Piedmont United (4-14)

avg: 469.97  •  sd: 66.28  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
61 Malice in Wonderland** Loss 5-13 694.25 Ignored Jul 22nd Filling the Void 2023
195 Swampbenders Loss 9-10 423.56 Jul 22nd Filling the Void 2023
69 Too Much Fun Loss 9-13 798.71 Jul 22nd Filling the Void 2023
237 Rampage Win 13-7 760.86 Jul 23rd Filling the Void 2023
248 Pickles Win 13-5 626.54 Jul 23rd Filling the Void 2023
104 Legion Loss 7-13 476.65 Jul 23rd Filling the Void 2023
43 Dirty Bird** Loss 1-15 878.32 Ignored Aug 12th HoDown Showdown 2023
199 MoonPi Win 14-10 930.61 Aug 12th HoDown Showdown 2023
108 Bear Jordan Loss 5-15 427.09 Aug 12th HoDown Showdown 2023
124 Magnanimouse Loss 4-15 361.58 Aug 13th HoDown Showdown 2023
79 Brunch Club Loss 8-14 613.09 Aug 13th HoDown Showdown 2023
148 Verdant Loss 5-15 210.39 Aug 13th HoDown Showdown 2023
22 Storm** Loss 4-13 1089.35 Ignored Sep 9th 2023 Mixed North Carolina Sectional Championship
124 Magnanimouse Loss 6-11 414.89 Sep 9th 2023 Mixed North Carolina Sectional Championship
69 Too Much Fun** Loss 3-13 617.28 Ignored Sep 9th 2023 Mixed North Carolina Sectional Championship
79 Brunch Club** Loss 4-13 549.12 Ignored Sep 9th 2023 Mixed North Carolina Sectional Championship
237 Rampage Loss 11-13 -25.51 Sep 10th 2023 Mixed North Carolina Sectional Championship
248 Pickles Win 13-10 354.68 Sep 10th 2023 Mixed North Carolina Sectional Championship
**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)