#89 Central Florida (14-14)

avg: 1246.55  •  sd: 61.14  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
117 Georgia State Win 13-7 1668.1 Jan 28th T Town Throwdown1
49 Vanderbilt Loss 10-13 1118.28 Jan 28th T Town Throwdown1
225 Spring Hill** Win 13-4 1201.62 Ignored Jan 28th T Town Throwdown1
253 Georgia Southern** Win 15-4 1092.01 Ignored Jan 28th T Town Throwdown1
84 Alabama Loss 11-13 1043.34 Jan 29th T Town Throwdown1
36 Alabama-Huntsville Win 12-11 1701.17 Jan 29th T Town Throwdown1
87 Mississippi State Loss 2-7 660.75 Jan 29th T Town Throwdown1
90 Virginia Tech Win 12-10 1483.87 Feb 3rd Florida Warm Up 2023
1 Massachusetts Loss 6-13 1620.24 Feb 3rd Florida Warm Up 2023
29 Wisconsin Loss 7-13 1109.13 Feb 4th Florida Warm Up 2023
5 Vermont Loss 7-13 1453.18 Feb 4th Florida Warm Up 2023
21 Georgia Loss 7-13 1178.78 Feb 4th Florida Warm Up 2023
4 Texas Loss 8-15 1456.7 Feb 4th Florida Warm Up 2023
18 Brown Loss 10-13 1418.01 Feb 5th Florida Warm Up 2023
99 Temple Win 12-11 1322.41 Feb 5th Florida Warm Up 2023
324 Mississippi** Win 13-3 541.36 Ignored Feb 25th Mardi Gras XXXV
87 Mississippi State Win 10-9 1385.75 Feb 25th Mardi Gras XXXV
209 Tulane-B Win 13-4 1288.22 Feb 25th Mardi Gras XXXV
80 Texas A&M Win 12-7 1805.16 Feb 25th Mardi Gras XXXV
104 Kennesaw State Win 13-10 1500.51 Feb 26th Mardi Gras XXXV
56 Indiana Loss 7-13 850.99 Feb 26th Mardi Gras XXXV
37 Florida Loss 3-7 941.54 Feb 26th Mardi Gras XXXV
153 Minnesota-Duluth Loss 10-11 821.98 Mar 11th Tally Classic XVII
204 South Florida Win 12-7 1243.89 Mar 11th Tally Classic XVII
94 Tulane Loss 8-10 956.17 Mar 11th Tally Classic XVII
106 Florida State Loss 8-13 664.29 Mar 11th Tally Classic XVII
253 Georgia Southern** Win 15-4 1092.01 Ignored Mar 12th Tally Classic XVII
204 South Florida Win 11-3 1323.38 Mar 12th Tally Classic XVII
**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)