#149 Arizona State (8-8)

avg: 420.66  •  sd: 64.27  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
126 Arizona Loss 7-11 204.35 Jan 28th New Year Fest 2023
175 Grand Canyon Loss 7-9 -155.35 Jan 28th New Year Fest 2023
199 Arizona-B** Win 11-2 123.99 Ignored Jan 28th New Year Fest 2023
139 Denver Loss 9-10 424.81 Jan 28th New Year Fest 2023
171 Northern Arizona Win 7-4 687.67 Jan 28th New Year Fest 2023
126 Arizona Loss 5-8 217.64 Jan 29th New Year Fest 2023
199 Arizona-B** Win 13-4 123.99 Ignored Jan 29th New Year Fest 2023
171 Northern Arizona Win 8-4 756.32 Jan 29th New Year Fest 2023
59 California-Santa Cruz** Loss 0-13 673.6 Ignored Feb 18th Santa Clara Rage Tournament
172 Chico State Win 10-5 764.04 Feb 18th Santa Clara Rage Tournament
179 California-Davis-B Win 6-4 409.82 Feb 18th Santa Clara Rage Tournament
83 Nevada-Reno Loss 6-11 456.65 Feb 18th Santa Clara Rage Tournament
184 California-B Win 9-1 554.97 Feb 18th Santa Clara Rage Tournament
93 California-Irvine Loss 7-10 547.75 Feb 19th Santa Clara Rage Tournament
118 California-San Diego-B Loss 3-8 175.56 Feb 19th Santa Clara Rage Tournament
179 California-Davis-B Win 6-4 409.82 Feb 19th Santa Clara Rage Tournament
**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)