#52 Appalachian State (17-7)

avg: 1634.05  •  sd: 57.17  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
1 North Carolina Loss 9-15 1877.17 Jan 28th Carolina Kickoff
33 Duke Loss 10-15 1337.06 Jan 28th Carolina Kickoff
45 Georgetown Loss 14-15 1571.69 Jan 28th Carolina Kickoff
36 Penn State Loss 12-13 1652.62 Jan 28th Carolina Kickoff
142 Carleton College-CHOP Win 15-9 1698.98 Jan 29th Carolina Kickoff
45 Georgetown Win 11-7 2163.58 Jan 29th Carolina Kickoff
134 Carnegie Mellon Win 14-6 1836.31 Feb 11th Queen City Tune Up1
13 Tufts Loss 9-15 1552.74 Feb 11th Queen City Tune Up1
27 South Carolina Loss 9-15 1332.7 Feb 11th Queen City Tune Up1
69 Maryland Loss 13-15 1325.78 Feb 11th Queen City Tune Up1
36 Penn State Win 13-12 1902.62 Feb 12th Queen City Tune Up1
49 Notre Dame Win 11-10 1768.26 Feb 12th Queen City Tune Up1
233 Florida-B** Win 13-2 1379.91 Ignored Mar 18th College Southerns XXI
268 Georgia Southern** Win 13-3 1251.41 Ignored Mar 18th College Southerns XXI
142 Carleton College-CHOP Win 12-9 1528.87 Mar 18th College Southerns XXI
219 Georgia-B Win 11-8 1225.2 Mar 18th College Southerns XXI
240 Georgia College** Win 15-6 1359.14 Ignored Mar 19th College Southerns XXI
142 Carleton College-CHOP Win 15-9 1698.98 Mar 19th College Southerns XXI
172 East Carolina Win 15-9 1578.91 Mar 19th College Southerns XXI
258 South Carolina-B** Win 13-2 1298.2 Ignored Mar 25th Needle in a Ho Stack2
194 Christopher Newport Win 13-6 1558.07 Mar 25th Needle in a Ho Stack2
317 North Carolina-Asheville** Win 13-0 938.85 Ignored Mar 25th Needle in a Ho Stack2
- Union (Tennessee) Win 12-6 1792.83 Mar 25th Needle in a Ho Stack2
140 Cedarville Win 14-4 1788.18 Mar 26th Needle in a Ho Stack2
**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)