#144 South Florida (9-16)

avg: 1288.53  •  sd: 52.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
68 Alabama-Huntsville Loss 7-10 1240.25 Jan 31st Florida Warm Up 2025
51 Cornell Loss 5-9 1194.84 Jan 31st Florida Warm Up 2025
29 Pittsburgh Loss 9-12 1533.44 Jan 31st Florida Warm Up 2025
17 Brown Loss 9-13 1636.69 Feb 1st Florida Warm Up 2025
52 Purdue Loss 5-13 1114.07 Feb 1st Florida Warm Up 2025
45 Virginia Tech Loss 7-13 1218.07 Feb 1st Florida Warm Up 2025
132 Florida State Win 12-9 1672.9 Feb 2nd Florida Warm Up 2025
90 Texas A&M Loss 10-11 1371.44 Feb 2nd Florida Warm Up 2025
86 Colorado-B Loss 6-13 909.79 Feb 22nd Mardi Gras XXXVII
117 Mississippi State Loss 9-10 1250.25 Feb 22nd Mardi Gras XXXVII
283 Texas State Win 13-8 1258.73 Feb 22nd Mardi Gras XXXVII
293 Trinity Win 13-7 1286.16 Feb 22nd Mardi Gras XXXVII
122 Clemson Loss 10-13 1033.45 Mar 15th Tally Classic XIX
196 Kennesaw State Win 9-6 1501.22 Mar 15th Tally Classic XIX
286 North Florida Win 15-7 1349.45 Mar 15th Tally Classic XIX
38 Ave Maria Loss 4-15 1215.95 Apr 12th Florida D I Mens Conferences 2025
132 Florida State Win 15-10 1781.14 Apr 12th Florida D I Mens Conferences 2025
126 Central Florida Win 14-10 1745.18 Apr 13th Florida D I Mens Conferences 2025
193 Miami (Florida) Win 14-10 1487.46 Apr 13th Florida D I Mens Conferences 2025
286 North Florida Win 15-5 1349.45 Apr 13th Florida D I Mens Conferences 2025
100 Alabama Loss 13-15 1251.42 Apr 26th Southeast D I College Mens Regionals 2025
132 Florida State Loss 10-15 873.93 Apr 26th Southeast D I College Mens Regionals 2025
18 Georgia Loss 7-15 1438.84 Apr 26th Southeast D I College Mens Regionals 2025
171 Alabama-Birmingham Loss 9-14 718.68 Apr 27th Southeast D I College Mens Regionals 2025
98 Tennessee-Chattanooga Loss 10-14 1073.52 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)