#68 Franciscan (23-2)

avg: 1660.48  •  sd: 63.86  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
194 Ohio Win 13-3 1742.62 Feb 3rd Huckin in the Hills X
292 Kent State** Win 13-4 1347.63 Ignored Feb 3rd Huckin in the Hills X
212 West Virginia Win 13-5 1679.5 Feb 3rd Huckin in the Hills X
126 Towson Win 12-2 1989.09 Feb 3rd Huckin in the Hills X
182 Dayton Win 15-12 1490.89 Feb 4th Huckin in the Hills X
387 Ohio-B** Win 15-1 842.5 Ignored Feb 4th Huckin in the Hills X
126 Towson Win 15-7 1989.09 Feb 4th Huckin in the Hills X
274 Air Force Win 13-7 1408.05 Mar 2nd FCS D III Tune Up 2024
209 Christopher Newport Win 13-7 1639.01 Mar 2nd FCS D III Tune Up 2024
65 Richmond Win 12-11 1799.99 Mar 2nd FCS D III Tune Up 2024
129 Michigan Tech Win 13-10 1710.19 Mar 2nd FCS D III Tune Up 2024
88 Berry Win 13-6 2151.68 Mar 3rd FCS D III Tune Up 2024
172 Union (Tennessee) Win 13-11 1462.52 Mar 3rd FCS D III Tune Up 2024
176 Navy Win 13-5 1812.43 Mar 3rd FCS D III Tune Up 2024
86 Cedarville Loss 8-13 1059.36 Apr 13th Ohio D III Mens Conferences 2024
123 Oberlin Win 12-7 1917.23 Apr 13th Ohio D III Mens Conferences 2024
168 Kenyon Win 12-9 1597.72 Apr 13th Ohio D III Mens Conferences 2024
173 Xavier Win 12-9 1578.8 Apr 13th Ohio D III Mens Conferences 2024
174 Grove City Win 12-8 1666.15 Apr 27th Ohio Valley D III College Mens Regionals 2024
198 Messiah Loss 12-13 1003.99 Apr 27th Ohio Valley D III College Mens Regionals 2024
234 Haverford** Win 13-3 1599.52 Ignored Apr 27th Ohio Valley D III College Mens Regionals 2024
168 Kenyon Win 13-4 1852.36 Apr 27th Ohio Valley D III College Mens Regionals 2024
86 Cedarville Win 13-8 2051.68 Apr 28th Ohio Valley D III College Mens Regionals 2024
168 Kenyon Win 13-5 1852.36 Apr 28th Ohio Valley D III College Mens Regionals 2024
198 Messiah Win 13-10 1457.14 Apr 28th Ohio Valley D III College Mens Regionals 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)