#301 North Florida (2-10)

avg: 318.87  •  sd: 100.12  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
191 Georgia College Loss 6-9 451.26 Jan 25th Clutch Classic 2020
220 North Georgia Win 12-7 1269.81 Jan 25th Clutch Classic 2020
215 Saint Louis Loss 5-11 171.7 Jan 25th Clutch Classic 2020
197 Georgia Southern Loss 4-11 236.26 Jan 25th Clutch Classic 2020
300 Belmont University Loss 7-8 214.97 Jan 26th Clutch Classic 2020
342 Kentucky-B Loss 7-8 -128.03 Jan 26th Clutch Classic 2020
117 Appalachian State** Loss 3-12 543.66 Ignored Feb 15th Chucktown Throwdown XVII
251 East Carolina Loss 6-10 123.1 Feb 15th Chucktown Throwdown XVII
262 Campbell Loss 7-9 277.79 Feb 15th Chucktown Throwdown XVII
128 Clemson** Loss 3-12 515.89 Ignored Feb 15th Chucktown Throwdown XVII
293 Charleston Win 10-9 520.6 Feb 16th Chucktown Throwdown XVII
262 Campbell Loss 5-10 -16.77 Feb 16th Chucktown Throwdown XVII
**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)