#104 Appalachian State (8-8)

avg: 1080.83  •  sd: 53.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
73 North Carolina-Charlotte Loss 5-12 682.49 Jan 25th Carolina Kickoff 2020
192 Air Force Win 10-9 731.26 Jan 25th Carolina Kickoff 2020
18 South Carolina Loss 6-11 1247.03 Jan 25th Carolina Kickoff 2020
63 Georgetown Loss 11-12 1218.15 Jan 25th Carolina Kickoff 2020
119 Richmond Win 11-9 1264.8 Jan 26th Carolina Kickoff 2020
100 Indiana Loss 9-11 861.66 Jan 26th Carolina Kickoff 2020
- Ohio State** Loss 4-13 1302.73 Feb 8th Queen City Tune Up 2020 Open
59 Duke Loss 11-12 1228.62 Feb 8th Queen City Tune Up 2020 Open
- Virginia Loss 11-13 1262.11 Feb 8th Queen City Tune Up 2020 Open
- Maryland Win 12-10 1073.96 Feb 9th Queen City Tune Up 2020 Open
222 Charleston** Win 11-2 983.92 Ignored Feb 15th Chucktown Throwdown XVII
214 East Carolina** Win 11-3 1071.35 Ignored Feb 15th Chucktown Throwdown XVII
245 North Florida** Win 12-3 795.94 Ignored Feb 15th Chucktown Throwdown XVII
166 Wake Forest Win 10-9 860.04 Feb 15th Chucktown Throwdown XVII
109 Clemson Loss 10-11 934.25 Feb 16th Chucktown Throwdown XVII
166 Wake Forest Win 14-5 1335.04 Feb 16th Chucktown Throwdown XVII
**Blowout Eligible

FAQ

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
  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
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)