#29 Mallard (15-9)

avg: 1728.26  •  sd: 64.18  •  top 16/20: 0.3%

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# Opponent Result Game Rating Status Date Event
4 Chain Lightning Loss 8-15 1647.08 Jul 8th TCT Pro Elite Challenge West 2023
57 Fungi Win 15-13 1706.72 Jul 8th TCT Pro Elite Challenge West 2023
22 SoCal Condors Loss 4-15 1252.4 Jul 8th TCT Pro Elite Challenge West 2023
3 Revolver Loss 6-15 1645.3 Jul 9th TCT Pro Elite Challenge West 2023
4 Chain Lightning Loss 7-15 1611.89 Jul 9th TCT Pro Elite Challenge West 2023
18 Dark Star-D Loss 10-15 1429.69 Jul 9th TCT Pro Elite Challenge West 2023
24 Blueprint Loss 10-12 1526.39 Jul 29th TCT Select Flight East 2023
110 CITYWIDE Special Win 15-4 1767.75 Jul 29th TCT Select Flight East 2023
46 DeMo Win 11-8 1929.26 Jul 29th TCT Select Flight East 2023
24 Blueprint Win 9-7 2043.85 Jul 30th TCT Select Flight East 2023
38 Phantom Win 13-12 1749.98 Jul 30th TCT Select Flight East 2023
21 Phoenix Loss 10-14 1460.55 Jul 30th TCT Select Flight East 2023
96 Bux Win 13-10 1617 Aug 19th Cooler Classic 34
90 HouSE Win 13-8 1792.7 Aug 19th Cooler Classic 34
36 Kansas City Smokestack Win 13-9 2060.46 Aug 19th Cooler Classic 34
47 Beacon Win 15-4 2163.24 Aug 20th Cooler Classic 34
17 STL Lounar Loss 11-15 1528.19 Aug 20th Cooler Classic 34
46 DeMo Win 15-7 2163.65 Aug 20th Cooler Classic 34
245 Milwaukee Revival** Win 13-3 800.48 Ignored Sep 9th 2023 Mens Northwest Plains Sectional Championship
191 DCVIII** Win 13-4 1335.58 Ignored Sep 9th 2023 Mens Northwest Plains Sectional Championship
127 Nomads** Win 13-5 1651.02 Ignored Sep 9th 2023 Mens Northwest Plains Sectional Championship
19 Sub Zero Win 15-14 1990.58 Sep 10th 2023 Mens Northwest Plains Sectional Championship
16 General Strike Loss 8-15 1371.8 Sep 10th 2023 Mens Northwest Plains Sectional Championship
73 Knights of Ni Win 13-11 1614.69 Sep 10th 2023 Mens Northwest Plains 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)