#126 Barefoot (11-10)

avg: 967.13  •  sd: 58.17  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
- SpaceX Win 11-10 1190.16 Jun 24th Huntsville Huckfest
218 Hairy Otter Win 11-6 936.41 Jun 24th Huntsville Huckfest
- Forcey Loss 6-11 735.15 Jun 24th Huntsville Huckfest
150 Memphis STAX Loss 8-11 461.82 Jun 24th Huntsville Huckfest
53 Roma Ultima Loss 10-11 1223.88 Jul 8th Club Terminus 2023
81 B-Unit Loss 9-13 757.82 Jul 8th Club Terminus 2023
169 Happy Hour Win 13-8 1236.76 Jul 8th Club Terminus 2023
73 m'kay Ultimate Loss 3-13 633.75 Jul 9th Club Terminus 2023
169 Happy Hour Win 13-6 1340.6 Jul 9th Club Terminus 2023
238 Victrix** Win 13-3 793.58 Ignored Jul 9th Club Terminus 2023
34 Dirty Bird Loss 7-15 983.11 Aug 12th HoDown Showdown 2023
195 MoonPi Win 12-9 913.29 Aug 12th HoDown Showdown 2023
96 Bear Jordan Loss 5-15 476 Aug 12th HoDown Showdown 2023
150 Memphis STAX Win 15-11 1208.6 Aug 13th HoDown Showdown 2023
160 Moontower Win 15-11 1181.84 Aug 13th HoDown Showdown 2023
122 Magnanimouse Loss 9-11 732.08 Aug 13th HoDown Showdown 2023
123 Amber Win 11-9 1230.48 Sep 9th 2023 Mixed Gulf Coast Sectional Championship
150 Memphis STAX Loss 11-12 702.43 Sep 9th 2023 Mixed Gulf Coast Sectional Championship
204 New Orleans Boil Win 15-8 1088.38 Sep 9th 2023 Mixed Gulf Coast Sectional Championship
123 Amber Loss 12-13 856.28 Sep 10th 2023 Mixed Gulf Coast Sectional Championship
150 Memphis STAX Win 15-10 1281.03 Sep 10th 2023 Mixed Gulf Coast 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)