#44 Red Circus (7-6)

avg: 1585.84  •  sd: 67.73  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
7 DiG Loss 8-15 1602.7 Sep 9th 2023 Mens East New England Sectional Championship
71 Big Wrench Win 15-9 1910.49 Sep 9th 2023 Mens East New England Sectional Championship
181 Jerk Factory Win 15-11 1169.61 Sep 9th 2023 Mens East New England Sectional Championship
82 Lantern Win 12-9 1695.08 Sep 9th 2023 Mens East New England Sectional Championship
32 Scoop Loss 11-13 1446.73 Sep 10th 2023 Mens East New England Sectional Championship
71 Big Wrench Win 13-10 1723.15 Sep 10th 2023 Mens East New England Sectional Championship
43 Mystery Box Win 13-9 2016.1 Sep 10th 2023 Mens East New England Sectional Championship
32 Scoop Win 13-11 1904.41 Sep 23rd 2023 Northeast Mens Regional Championship
15 GOAT Loss 8-15 1407.47 Sep 23rd 2023 Northeast Mens Regional Championship
21 Phoenix Loss 7-13 1301.72 Sep 23rd 2023 Northeast Mens Regional Championship
62 Shade Win 12-11 1574.64 Sep 23rd 2023 Northeast Mens Regional Championship
32 Scoop Loss 12-15 1375.08 Sep 24th 2023 Northeast Mens Regional Championship
24 Blueprint Loss 11-13 1535.68 Sep 24th 2023 Northeast 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)