#183 South Florida (9-16)

avg: 1186.53  •  sd: 43.53  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
85 Cornell Loss 5-13 975.01 Feb 2nd Florida Warm Up 2024
9 Vermont** Loss 1-13 1647.89 Ignored Feb 2nd Florida Warm Up 2024
23 Tufts** Loss 3-13 1424.06 Ignored Feb 2nd Florida Warm Up 2024
54 Emory Loss 5-13 1155.32 Feb 3rd Florida Warm Up 2024
89 Florida State Loss 7-15 951.28 Feb 3rd Florida Warm Up 2024
20 Washington University** Loss 3-13 1486.16 Ignored Feb 3rd Florida Warm Up 2024
69 Central Florida Loss 4-15 1038.53 Feb 4th Florida Warm Up 2024
195 Alabama-Birmingham Win 9-7 1421.6 Feb 24th Joint Summit 2024
146 Clemson Loss 9-10 1193.35 Feb 24th Joint Summit 2024
346 Coastal Carolina** Win 13-4 1131.02 Ignored Feb 24th Joint Summit 2024
325 South Carolina-B Win 13-4 1220.93 Feb 24th Joint Summit 2024
146 Clemson Loss 8-13 822.19 Feb 25th Joint Summit 2024
261 Georgia Tech-B Win 11-7 1371.17 Feb 25th Joint Summit 2024
261 Georgia Tech-B Win 13-6 1504.28 Feb 25th Joint Summit 2024
69 Central Florida Loss 9-10 1513.53 Mar 16th Tally Classic XVIII
96 Notre Dame Loss 6-11 984.29 Mar 16th Tally Classic XVIII
203 Spring Hill Win 10-9 1238.28 Mar 16th Tally Classic XVIII
195 Alabama-Birmingham Loss 9-11 893.05 Mar 17th Tally Classic XVIII
73 Ave Maria Loss 1-13 1014.71 Mar 17th Tally Classic XVIII
203 Spring Hill Win 9-7 1392.62 Mar 17th Tally Classic XVIII
46 Florida Loss 6-15 1185.88 Apr 13th Florida D I Mens Conferences 2024
309 Florida Gulf Coast Win 15-9 1196.85 Apr 13th Florida D I Mens Conferences 2024
186 Miami (Florida) Win 12-10 1414.49 Apr 13th Florida D I Mens Conferences 2024
69 Central Florida Loss 8-15 1073.73 Apr 14th Florida D I Mens Conferences 2024
89 Florida State Loss 10-13 1223.14 Apr 14th Florida D I Mens Conferences 2024
**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)