#200 Miami (Florida) (2-9)

avg: -78.75  •  sd: 198.57  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
78 Central Florida** Loss 0-13 412.53 Ignored Jan 28th Florida Winter Classic 2023
45 Florida** Loss 1-13 761.44 Ignored Jan 28th Florida Winter Classic 2023
46 Florida State** Loss 1-13 759.02 Ignored Jan 28th Florida Winter Classic 2023
208 Florida Tech Win 8-4 379.24 Jan 28th Florida Winter Classic 2023
214 Florida-B Win 9-2 218.13 Jan 29th Florida Winter Classic 2023
10 Northeastern** Loss 0-13 1410.32 Ignored Jan 29th Florida Winter Classic 2023
152 Sam Houston Loss 5-10 -73.35 Feb 25th Mardi Gras XXXV
78 Central Florida** Loss 0-13 412.53 Ignored Feb 25th Mardi Gras XXXV
81 Trinity** Loss 0-13 398.62 Ignored Feb 25th Mardi Gras XXXV
168 Jacksonville State Loss 0-11 -306.73 Feb 26th Mardi Gras XXXV
175 LSU Loss 2-12 -369.87 Feb 26th Mardi Gras XXXV
**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)