#71 Florida (12-11)

avg: 1590.96  •  sd: 38.73  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
14 Texas Loss 7-12 1521.72 Jan 31st Florida Warm Up 2025
26 Michigan Loss 8-11 1537.87 Jan 31st Florida Warm Up 2025
2 Massachusetts** Loss 3-13 1769.51 Ignored Jan 31st Florida Warm Up 2025
45 Virginia Tech Loss 10-11 1627.57 Feb 1st Florida Warm Up 2025
50 Tulane Loss 8-12 1266.93 Feb 1st Florida Warm Up 2025
92 Texas A&M Win 12-10 1716 Feb 1st Florida Warm Up 2025
128 Central Florida Win 11-8 1693.28 Feb 2nd Florida Warm Up 2025
51 Cornell Loss 9-10 1581.16 Feb 2nd Florida Warm Up 2025
102 Alabama Win 13-10 1775.45 Feb 22nd Mardi Gras XXXVII
35 Ave Maria Loss 10-12 1559.1 Feb 22nd Mardi Gras XXXVII
280 Jacksonville State Win 13-6 1366.09 Feb 22nd Mardi Gras XXXVII
134 Florida State Win 10-7 1700.44 Feb 22nd Mardi Gras XXXVII
124 Clemson Loss 10-11 1218.98 Mar 15th Tally Classic XIX
130 LSU Win 14-9 1797.02 Mar 15th Tally Classic XIX
184 Georgia State Win 12-9 1462.32 Mar 15th Tally Classic XIX
128 Central Florida Win 15-11 1708.83 Apr 12th Florida D I Mens Conferences 2025
290 North Florida** Win 15-3 1331.48 Ignored Apr 12th Florida D I Mens Conferences 2025
35 Ave Maria Loss 9-15 1281.74 Apr 13th Florida D I Mens Conferences 2025
128 Central Florida Win 15-10 1781.27 Apr 26th Southeast D I College Mens Regionals 2025
118 Mississippi State Win 14-10 1755.95 Apr 26th Southeast D I College Mens Regionals 2025
21 Georgia Tech Loss 9-15 1460.7 Apr 26th Southeast D I College Mens Regionals 2025
99 Tennessee-Chattanooga Win 14-10 1852.32 Apr 26th Southeast D I College Mens Regionals 2025
17 Georgia Loss 10-15 1561 Apr 27th Southeast D I College Mens Regionals 2025
**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)