#158 Davidson (9-4)

avg: 993.18  •  sd: 88.4  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
304 North Carolina State-B Win 13-6 909.67 Feb 8th BatCH Bash 2020
178 North Carolina-B Win 13-9 1344.4 Feb 8th BatCH Bash 2020
255 South Carolina-B Win 13-4 1209.53 Feb 8th BatCH Bash 2020
178 North Carolina-B Win 14-10 1324.54 Feb 9th BatCH Bash 2020
290 Virginia-B Win 15-5 1011.59 Feb 9th BatCH Bash 2020
255 South Carolina-B Win 13-4 1209.53 Feb 9th BatCH Bash 2020
162 Air Force Loss 9-13 563.56 Feb 29th FCS D III Tune Up 2020
127 Brandeis Loss 6-12 542.54 Feb 29th FCS D III Tune Up 2020
143 Oberlin Loss 10-12 788.24 Feb 29th FCS D III Tune Up 2020
135 Messiah Win 13-12 1191.07 Feb 29th FCS D III Tune Up 2020
122 Portland Loss 11-13 900.33 Mar 1st FCS D III Tune Up 2020
291 Wooster Win 10-9 534.48 Mar 1st FCS D III Tune Up 2020
170 Shippensburg Win 12-6 1539.65 Mar 1st FCS D III Tune Up 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)