#39 Florida (14-8)

avg: 1741.42  •  sd: 81.64  •  top 16/20: 0.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
11 Brown Loss 11-13 1845.88 Feb 3rd Florida Warm Up 2023
72 Auburn Loss 10-13 1169.66 Feb 3rd Florida Warm Up 2023
14 Carleton College Loss 6-13 1449.87 Feb 3rd Florida Warm Up 2023
3 Massachusetts Loss 8-13 1815.25 Feb 4th Florida Warm Up 2023
8 Pittsburgh Loss 6-11 1608.48 Feb 4th Florida Warm Up 2023
67 Virginia Tech Win 13-9 1971.86 Feb 4th Florida Warm Up 2023
79 Texas A&M Win 13-9 1892.25 Feb 5th Florida Warm Up 2023
147 Connecticut Win 13-9 1581.04 Feb 5th Florida Warm Up 2023
87 Tennessee-Chattanooga Win 13-7 1994.13 Feb 25th Mardi Gras XXXV
331 Texas Tech** Win 13-1 859.24 Ignored Feb 25th Mardi Gras XXXV
336 Trinity** Win 13-5 814.33 Ignored Feb 25th Mardi Gras XXXV
259 Jacksonville State** Win 13-4 1296.22 Ignored Feb 25th Mardi Gras XXXV
43 Alabama-Huntsville Loss 8-13 1206.97 Feb 26th Mardi Gras XXXV
88 Central Florida Win 7-3 2034.22 Feb 26th Mardi Gras XXXV
89 Mississippi State Win 11-5 2034.06 Feb 26th Mardi Gras XXXV
60 Middlebury Win 13-10 1906.17 Mar 18th Centex 2023
47 Colorado State Win 12-10 1885.34 Mar 18th Centex 2023
86 Dartmouth Win 12-9 1782.33 Mar 18th Centex 2023
13 Tufts Loss 10-13 1740.08 Mar 18th Centex 2023
60 Middlebury Win 15-14 1703.03 Mar 19th Centex 2023
79 Texas A&M Loss 14-15 1348.68 Mar 19th Centex 2023
54 Northwestern Win 11-7 2083.08 Mar 19th Centex 2023
**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)