#144 Franciscan (6-6)

avg: 981  •  sd: 53.25  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
101 Liberty Loss 8-12 748.41 Feb 18th Commonwealth Cup Weekend1 2023
77 Wisconsin-Milwaukee Loss 12-13 1180.41 Feb 18th Commonwealth Cup Weekend1 2023
203 North Carolina-B Win 13-5 1325.25 Feb 18th Commonwealth Cup Weekend1 2023
272 Georgia Tech-B Win 13-3 1001.52 Feb 19th Commonwealth Cup Weekend1 2023
203 North Carolina-B Win 11-8 1090.86 Feb 19th Commonwealth Cup Weekend1 2023
176 Brandeis Win 11-10 982.85 Mar 4th FCS D III Tune Up 2023
241 Air Force Win 13-7 1098.68 Mar 4th FCS D III Tune Up 2023
116 Kenyon Loss 10-13 783.11 Mar 4th FCS D III Tune Up 2023
240 Xavier Win 12-11 668.78 Mar 4th FCS D III Tune Up 2023
69 Middlebury Loss 10-12 1122 Mar 5th FCS D III Tune Up 2023
79 Lewis & Clark Loss 9-13 871.93 Mar 5th FCS D III Tune Up 2023
75 Richmond Loss 8-11 946.4 Mar 5th FCS D III Tune Up 2023
**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)