#96 Appalachian State (14-12)

avg: 1274.42  •  sd: 67.16  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
84 Ohio State Loss 10-13 991.53 Feb 1st Carolina Kickoff mens 2025
37 North Carolina-Wilmington Loss 7-13 1077.53 Feb 1st Carolina Kickoff mens 2025
27 South Carolina Loss 10-13 1451.46 Feb 1st Carolina Kickoff mens 2025
54 Carleton College-CHOP Loss 7-15 938.06 Feb 2nd Carolina Kickoff mens 2025
88 Georgetown Loss 11-13 1079.83 Feb 2nd Carolina Kickoff mens 2025
84 Ohio State Loss 8-13 823.51 Feb 2nd Carolina Kickoff mens 2025
49 North Carolina State Loss 9-10 1439.8 Feb 22nd Easterns Qualifier 2025
67 Indiana Loss 10-13 1115.1 Feb 22nd Easterns Qualifier 2025
52 William & Mary Loss 7-12 1030.98 Feb 22nd Easterns Qualifier 2025
66 Dartmouth Loss 6-13 852.25 Feb 22nd Easterns Qualifier 2025
183 Kennesaw State Win 14-13 1015.74 Feb 23rd Easterns Qualifier 2025
102 Syracuse Win 13-12 1385.39 Feb 23rd Easterns Qualifier 2025
128 SUNY-Binghamton Win 10-6 1624.23 Feb 23rd Easterns Qualifier 2025
388 American-B** Win 15-1 179.27 Ignored Mar 22nd Atlantic Coast Open 2025
163 Messiah Win 14-8 1517.63 Mar 22nd Atlantic Coast Open 2025
139 Florida State Win 14-12 1304.64 Mar 22nd Atlantic Coast Open 2025
115 Vermont-B Win 13-8 1690.87 Mar 22nd Atlantic Coast Open 2025
171 Dickinson Win 15-11 1334.37 Mar 23rd Atlantic Coast Open 2025
105 Liberty Win 15-12 1533.4 Mar 23rd Atlantic Coast Open 2025
43 Virginia Tech Loss 9-14 1138.33 Mar 23rd Atlantic Coast Open 2025
260 Georgia-B** Win 13-3 1167.19 Ignored Mar 29th Needle in a Ho Stack 2025
345 Georgia College** Win 13-2 722.42 Ignored Mar 29th Needle in a Ho Stack 2025
252 Embry-Riddle Win 13-6 1190.43 Mar 29th Needle in a Ho Stack 2025
187 North Carolina-B Win 15-10 1317.99 Mar 30th Needle in a Ho Stack 2025
65 Tennessee Loss 10-12 1215.42 Mar 30th Needle in a Ho Stack 2025
130 Charleston Win 15-2 1723.67 Mar 30th Needle in a Ho Stack 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)