#219 Flood Zone (3-13)

avg: 376.91  •  sd: 63.01  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
187 Oasis Ultimate Loss 8-13 82.18 Jul 8th Summer Glazed Daze 2023
98 FlyTrap** Loss 4-15 453.4 Ignored Jul 8th Summer Glazed Daze 2023
137 Catalyst Loss 8-15 289.96 Jul 8th Summer Glazed Daze 2023
201 Spice Loss 4-15 -76.07 Jul 9th Summer Glazed Daze 2023
61 Malice in Wonderland** Loss 3-15 694.25 Ignored Aug 12th HoDown Showdown 2023
148 Verdant Loss 8-14 274.35 Aug 12th HoDown Showdown 2023
93 Crown Peach** Loss 4-14 478.61 Ignored Aug 12th HoDown Showdown 2023
237 Rampage Win 13-3 803.33 Aug 13th HoDown Showdown 2023
248 Pickles Win 15-6 626.54 Aug 13th HoDown Showdown 2023
199 MoonPi Loss 9-11 282.7 Aug 13th HoDown Showdown 2023
43 Dirty Bird** Loss 3-13 878.32 Ignored Sep 9th 2023 Mixed East Coast Sectional Championship
199 MoonPi Loss 8-9 406.91 Sep 9th 2023 Mixed East Coast Sectional Championship
87 m'kay Ultimate** Loss 3-13 519.04 Ignored Sep 9th 2023 Mixed East Coast Sectional Championship
93 Crown Peach Loss 7-13 521.08 Sep 9th 2023 Mixed East Coast Sectional Championship
220 Hairy Otter Win 11-10 500.26 Sep 10th 2023 Mixed East Coast Sectional Championship
148 Verdant Loss 4-13 210.39 Sep 10th 2023 Mixed East Coast Sectional Championship
**Blowout Eligible

FAQ

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
  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
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)