#34 Trident I (13-8)

avg: 1661.81  •  sd: 69.44  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
9 Doublewide Loss 10-13 1778.67 Jul 15th TCT Pro Elite Challenge East 2023
51 TireBizFriz Win 12-7 2073.01 Jul 15th TCT Pro Elite Challenge East 2023
12 Raleigh-Durham United Loss 7-15 1406.62 Jul 15th TCT Pro Elite Challenge East 2023
38 Phantom Win 13-10 1953.12 Jul 16th TCT Pro Elite Challenge East 2023
7 DiG Loss 6-15 1567.51 Aug 19th TCT Elite Select Challenge 2023
31 Garden State Ultimate Win 12-9 2021.48 Aug 19th TCT Elite Select Challenge 2023
22 SoCal Condors Loss 10-13 1524.26 Aug 19th TCT Elite Select Challenge 2023
28 Tanasi Win 12-9 2077.81 Aug 20th TCT Elite Select Challenge 2023
50 H.I.P Loss 12-13 1428.51 Aug 20th TCT Elite Select Challenge 2023
25 Mad Men Loss 9-13 1338.71 Aug 20th TCT Elite Select Challenge 2023
239 8-Bit Defenders** Win 15-3 908.71 Ignored Sep 9th 2023 Mens Central Plains Sectional Championship
126 Blink-122 Weekends Win 15-9 1569.87 Sep 9th 2023 Mens Central Plains Sectional Championship
221 Lafayette Street Dogs** Win 15-2 1119.61 Ignored Sep 9th 2023 Mens Central Plains Sectional Championship
135 Trident II** Win 15-6 1623.67 Ignored Sep 9th 2023 Mens Central Plains Sectional Championship
63 I-69 Win 11-8 1814.93 Sep 10th 2023 Mens Central Plains Sectional Championship
130 Diesel** Win 13-2 1640.66 Ignored Sep 23rd 2023 Great Lakes Mens Regional Championship
75 Flying Dutchmen Win 13-7 1935.07 Sep 23rd 2023 Great Lakes Mens Regional Championship
117 Chimney Win 13-5 1719.57 Sep 23rd 2023 Great Lakes Mens Regional Championship
47 Beacon Loss 10-12 1325.11 Sep 24th 2023 Great Lakes Mens Regional Championship
63 I-69 Win 15-12 1749.81 Sep 24th 2023 Great Lakes Mens Regional Championship
55 Colonels Loss 12-15 1203.2 Sep 24th 2023 Great Lakes Mens 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)