#75 Richmond (7-6)

avg: 1312  •  sd: 49.12  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
142 Carleton College-CHOP Win 15-7 1593.41 Jan 28th Carolina Kickoff
24 North Carolina-Charlotte Loss 9-15 1184.94 Jan 28th Carolina Kickoff
17 South Carolina Loss 5-15 1173.87 Jan 28th Carolina Kickoff
15 North Carolina State Loss 9-15 1289.8 Jan 28th Carolina Kickoff
142 Carleton College-CHOP Win 10-8 1256.07 Jan 29th Carolina Kickoff
2 North Carolina** Loss 6-15 1538.39 Ignored Jan 29th Carolina Kickoff
40 Duke Loss 9-13 1112.02 Jan 29th Carolina Kickoff
176 Brandeis Win 13-4 1457.85 Mar 4th FCS D III Tune Up 2023
69 Middlebury Win 11-9 1609.33 Mar 4th FCS D III Tune Up 2023
79 Lewis & Clark Win 13-12 1415.5 Mar 4th FCS D III Tune Up 2023
200 Wooster Win 13-9 1153.37 Mar 4th FCS D III Tune Up 2023
144 Franciscan Win 11-8 1346.61 Mar 5th FCS D III Tune Up 2023
96 Whitman Loss 12-13 1084.47 Mar 5th FCS D III Tune Up 2023
**Blowout Eligible


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)