#98 FlyTrap (12-11)

avg: 1053.4  •  sd: 49.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
187 Oasis Ultimate Win 15-9 1093.82 Jul 8th Summer Glazed Daze 2023
219 Flood Zone** Win 15-4 976.91 Ignored Jul 8th Summer Glazed Daze 2023
137 Catalyst Win 11-10 979.77 Jul 8th Summer Glazed Daze 2023
148 Verdant Loss 11-12 685.39 Aug 12th HoDown Showdown 2023
61 Malice in Wonderland Loss 12-13 1169.25 Aug 12th HoDown Showdown 2023
93 Crown Peach Loss 11-12 953.61 Aug 12th HoDown Showdown 2023
79 Brunch Club Win 13-12 1274.12 Aug 13th HoDown Showdown 2023
124 Magnanimouse Loss 13-14 836.58 Aug 13th HoDown Showdown 2023
154 Moontower Win 15-11 1175.76 Aug 13th HoDown Showdown 2023
112 Dizzy Kitty Loss 11-13 774.98 Aug 26th Soda City Round Robin
112 Dizzy Kitty Win 11-10 1128.82 Aug 26th Soda City Round Robin
248 Pickles** Win 13-3 626.54 Ignored Sep 9th 2023 Mixed North Carolina Sectional Championship
108 Bear Jordan Win 13-12 1152.09 Sep 9th 2023 Mixed North Carolina Sectional Championship
61 Malice in Wonderland Loss 10-12 1056.13 Sep 9th 2023 Mixed North Carolina Sectional Championship
108 Bear Jordan Win 15-13 1241.27 Sep 10th 2023 Mixed North Carolina Sectional Championship
69 Too Much Fun Loss 10-15 763.68 Sep 10th 2023 Mixed North Carolina Sectional Championship
43 Dirty Bird Loss 7-15 878.32 Sep 23rd 2023 Southeast Mixed Regional Championship
9 Space Force** Loss 4-13 1290.7 Ignored Sep 23rd 2023 Southeast Mixed Regional Championship
93 Crown Peach Win 13-9 1497.17 Sep 23rd 2023 Southeast Mixed Regional Championship
69 Too Much Fun Loss 9-13 798.71 Sep 23rd 2023 Southeast Mixed Regional Championship
108 Bear Jordan Win 14-13 1152.09 Sep 24th 2023 Southeast Mixed Regional Championship
87 m'kay Ultimate Win 15-11 1500.2 Sep 24th 2023 Southeast Mixed Regional Championship
61 Malice in Wonderland Loss 10-15 840.64 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)