#84 Richmond (10-10)

avg: 1449.92  •  sd: 64.26  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
142 Carleton College-CHOP Win 15-7 1783.5 Jan 28th Carolina Kickoff
24 North Carolina-Charlotte Loss 9-15 1379 Jan 28th Carolina Kickoff
27 South Carolina Loss 5-15 1248.18 Jan 28th Carolina Kickoff
20 North Carolina State Loss 9-15 1429.92 Jan 28th Carolina Kickoff
142 Carleton College-CHOP Win 10-8 1446.17 Jan 29th Carolina Kickoff
1 North Carolina** Loss 6-15 1792.65 Ignored Jan 29th Carolina Kickoff
33 Duke Loss 9-13 1372.1 Jan 29th Carolina Kickoff
171 Brandeis Win 13-4 1666.85 Mar 4th FCS D III Tune Up 2023
60 Middlebury Win 11-9 1827.23 Mar 4th FCS D III Tune Up 2023
74 Lewis & Clark Win 13-12 1615.93 Mar 4th FCS D III Tune Up 2023
202 Wooster Win 13-9 1354.94 Mar 4th FCS D III Tune Up 2023
126 Franciscan Win 11-8 1633.21 Mar 5th FCS D III Tune Up 2023
81 Whitman Loss 12-13 1339.12 Mar 5th FCS D III Tune Up 2023
168 Johns Hopkins Win 13-9 1505.14 Apr 1st Atlantic Coast Open 2023
369 George Mason** Win 13-2 600 Ignored Apr 1st Atlantic Coast Open 2023
36 Penn State Loss 6-13 1177.62 Apr 1st Atlantic Coast Open 2023
101 Navy Loss 8-10 1113.82 Apr 1st Atlantic Coast Open 2023
106 Liberty Win 15-10 1796.51 Apr 2nd Atlantic Coast Open 2023
36 Penn State Loss 8-13 1281.46 Apr 2nd Atlantic Coast Open 2023
33 Duke Loss 8-14 1254.63 Apr 2nd Atlantic Coast Open 2023
**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)