#82 Central Florida (15-12)

avg: 1337.27  •  sd: 65.1  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
74 Cincinnati Loss 10-13 1033.05 Feb 2nd Florida Warm Up 2024
14 Texas Loss 3-13 1336.64 Feb 2nd Florida Warm Up 2024
2 Georgia Loss 7-13 1715.28 Feb 2nd Florida Warm Up 2024
20 Northeastern Loss 2-13 1230.32 Feb 3rd Florida Warm Up 2024
11 Minnesota** Loss 3-13 1402.24 Ignored Feb 3rd Florida Warm Up 2024
52 Virginia Tech Loss 13-15 1261.35 Feb 3rd Florida Warm Up 2024
185 South Florida Win 15-4 1466.3 Feb 4th Florida Warm Up 2024
220 Sam Houston Win 13-8 1239.21 Feb 24th Mardi Gras XXXVI college
379 Tulane-B** Win 13-1 172.13 Ignored Feb 24th Mardi Gras XXXVI college
274 Trinity** Win 13-0 1103.96 Ignored Feb 24th Mardi Gras XXXVI college
37 Texas A&M Loss 9-13 1172.37 Feb 24th Mardi Gras XXXVI college
200 Spring Hill Win 13-4 1428.24 Feb 25th Mardi Gras XXXVI college
41 Florida Win 10-9 1696.02 Feb 25th Mardi Gras XXXVI college
44 Tulane Loss 9-12 1196.31 Feb 25th Mardi Gras XXXVI college
91 Indiana Win 9-2 1870.81 Feb 25th Mardi Gras XXXVI college
75 Ave Maria Loss 9-11 1110.37 Mar 16th Tally Classic XVIII
185 South Florida Win 10-9 991.3 Mar 16th Tally Classic XVIII
200 Spring Hill Win 13-3 1428.24 Mar 16th Tally Classic XVIII
75 Ave Maria Win 15-14 1484.58 Mar 17th Tally Classic XVIII
106 Notre Dame Win 13-7 1767.85 Mar 17th Tally Classic XVIII
106 Notre Dame Win 15-11 1591.48 Mar 17th Tally Classic XVIII
118 Michigan Tech Loss 8-10 910.96 Mar 30th Huck Finn 2024
204 Ohio Win 13-7 1367.12 Mar 30th Huck Finn 2024
67 Chicago Loss 8-9 1262.02 Mar 30th Huck Finn 2024
117 Vanderbilt Loss 8-9 1054.97 Mar 31st Huck Finn 2024
121 Iowa State Win 12-7 1675.57 Mar 31st Huck Finn 2024
209 Oklahoma Win 11-9 1033.21 Mar 31st Huck Finn 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)