#162 Air Force (8-11)

avg: 982.13  •  sd: 81.65  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
66 Georgetown Loss 5-13 792.84 Jan 25th Carolina Kickoff 2020
26 South Carolina** Loss 4-13 1144.93 Ignored Jan 25th Carolina Kickoff 2020
117 Appalachian State Loss 9-10 1018.66 Jan 25th Carolina Kickoff 2020
155 North Carolina-Asheville Loss 8-11 631.54 Jan 26th Carolina Kickoff 2020
153 Florida State Loss 3-12 403.71 Jan 26th Carolina Kickoff 2020
97 Richmond Loss 6-9 814.14 Jan 26th Carolina Kickoff 2020
236 Samford Win 10-8 974.43 Feb 29th FCS D III Tune Up 2020
158 Davidson Win 13-9 1411.75 Feb 29th FCS D III Tune Up 2020
143 Oberlin Loss 11-12 901.36 Feb 29th FCS D III Tune Up 2020
322 High Point** Win 13-5 788.87 Ignored Feb 29th FCS D III Tune Up 2020
127 Brandeis Loss 11-13 893.01 Mar 1st FCS D III Tune Up 2020
190 Berry Loss 8-13 382.22 Mar 1st FCS D III Tune Up 2020
118 Navy Loss 9-13 715.77 Mar 1st FCS D III Tune Up 2020
282 Colorado Mesa University Win 13-7 1002.37 Mar 7th Air Force Invite 2020
344 Colorado School of Mines-B** Win 13-2 576.85 Ignored Mar 7th Air Force Invite 2020
94 Denver Win 13-6 1855.85 Mar 7th Air Force Invite 2020
205 Colorado-Colorado Springs Win 13-8 1316.91 Mar 7th Air Force Invite 2020
205 Colorado-Colorado Springs Win 12-7 1341.26 Mar 8th Air Force Invite 2020
94 Denver Loss 6-9 837.29 Mar 8th Air Force Invite 2020
**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)