#22 Storm (17-4)

avg: 1689.35  •  sd: 81.38  •  top 16/20: 13%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
14 Rally Loss 10-15 1380.14 Jul 15th TCT Pro Elite Challenge East 2023
9 Space Force Loss 7-12 1370.19 Jul 15th TCT Pro Elite Challenge East 2023
5 Cleveland Crocs Loss 8-14 1400.48 Jul 15th TCT Pro Elite Challenge East 2023
19 Public Enemy Win 12-7 2259.04 Jul 16th TCT Pro Elite Challenge East 2023
149 ColorBomb Win 11-6 1352.01 Aug 5th Philly Open 2023
243 NYWT** Win 13-1 703.75 Ignored Aug 5th Philly Open 2023
91 Brackish Win 13-6 1686.1 Aug 5th Philly Open 2023
55 Garbage Plates Win 11-6 1888.96 Aug 6th Philly Open 2023
62 Funk Win 13-5 1861.89 Aug 6th Philly Open 2023
24 Loco Win 11-10 1795.85 Aug 6th Philly Open 2023
208 Piedmont United** Win 13-4 1069.97 Ignored Sep 9th 2023 Mixed North Carolina Sectional Championship
79 Brunch Club Win 13-9 1567.69 Sep 9th 2023 Mixed North Carolina Sectional Championship
124 Magnanimouse Win 13-10 1289.73 Sep 9th 2023 Mixed North Carolina Sectional Championship
69 Too Much Fun Win 13-4 1817.28 Sep 9th 2023 Mixed North Carolina Sectional Championship
61 Malice in Wonderland Win 15-7 1894.25 Sep 10th 2023 Mixed North Carolina Sectional Championship
43 Dirty Bird Win 13-4 2078.32 Sep 23rd 2023 Southeast Mixed Regional Championship
87 m'kay Ultimate Win 12-7 1639.55 Sep 23rd 2023 Southeast Mixed Regional Championship
148 Verdant** Win 13-5 1410.39 Ignored Sep 23rd 2023 Southeast Mixed Regional Championship
52 Roma Ultima Win 15-11 1735.26 Sep 24th 2023 Southeast Mixed Regional Championship
9 Space Force Win 14-13 2015.7 Sep 24th 2023 Southeast Mixed Regional Championship
12 'Shine Loss 7-15 1251.08 Sep 24th 2023 Southeast Mixed Regional 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)