#330 Arizona State-C (0-6)

avg: 91  •  sd: 188.7  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
149 Brigham Young-B** Loss 1-13 407.43 Ignored Jan 25th New Year Fest 2020
179 Grand Canyon** Loss 0-13 324.73 Ignored Jan 25th New Year Fest 2020
44 Utah State** Loss 1-13 988.6 Ignored Jan 25th New Year Fest 2020
94 Denver** Loss 1-11 655.85 Ignored Jan 25th New Year Fest 2020
228 Colorado State-B** Loss 1-13 139.97 Ignored Jan 25th New Year Fest 2020
318 Arizona-B Loss 3-4 91 Jan 26th New Year Fest 2020
**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)