#125 Davidson (15-9)

avg: 1146.57  •  sd: 59.55  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
72 Georgetown Loss 8-15 800.89 Jan 27th Carolina Kickoff 2024
16 Penn State Loss 7-15 1321.23 Jan 27th Carolina Kickoff 2024
13 North Carolina State Loss 8-15 1381.79 Jan 27th Carolina Kickoff 2024
78 Carleton College-CHOP Loss 8-15 784.13 Jan 28th Carolina Kickoff 2024
91 Indiana Loss 6-14 670.81 Jan 28th Carolina Kickoff 2024
296 South Carolina-B** Win 13-3 969.12 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
216 North Carolina State-B Win 10-5 1334.21 Feb 17th Commonwealth Cup Weekend 1 2024
199 Messiah Win 10-9 957.24 Feb 17th Commonwealth Cup Weekend 1 2024
77 Cedarville Win 10-9 1480.5 Feb 18th Commonwealth Cup Weekend 1 2024
144 Pittsburgh-B Win 12-6 1641.33 Feb 18th Commonwealth Cup Weekend 1 2024
58 Maryland Loss 7-10 1053.3 Feb 18th Commonwealth Cup Weekend 1 2024
59 Whitman Loss 6-13 836.78 Mar 2nd FCS D III Tune Up 2024
179 Missouri S&T Win 13-8 1400.26 Mar 2nd FCS D III Tune Up 2024
163 Xavier Win 11-6 1520.17 Mar 2nd FCS D III Tune Up 2024
226 Embry-Riddle Win 13-9 1148.56 Mar 2nd FCS D III Tune Up 2024
80 Lewis & Clark Loss 10-12 1101.56 Mar 3rd FCS D III Tune Up 2024
118 Michigan Tech Win 13-9 1592.19 Mar 3rd FCS D III Tune Up 2024
122 Oberlin Win 12-11 1277.55 Mar 3rd FCS D III Tune Up 2024
235 North Carolina-B Win 9-8 812.46 Mar 23rd Needle in a Ho Stack 2024
244 Georgia Southern Win 10-7 1040.21 Mar 23rd Needle in a Ho Stack 2024
77 Cedarville Loss 3-10 755.5 Mar 24th Needle in a Ho Stack 2024
210 Charleston Win 11-3 1376.87 Mar 24th Needle in a Ho Stack 2024
324 Coastal Carolina Win 11-6 772.85 Mar 24th Needle in a Ho Stack 2024
116 Liberty Win 10-9 1307.96 Mar 24th Needle in a Ho Stack 2024
**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)