#66 Franciscan (19-4)

avg: 1623.97  •  sd: 49.42  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
132 Kenyon Win 12-11 1440.76 Mar 1st D III River City Showdown 2025
101 North Carolina-Asheville Win 13-3 2048.36 Mar 1st D III River City Showdown 2025
169 Michigan Tech Win 12-8 1629.76 Mar 1st D III River City Showdown 2025
33 Elon Win 10-9 1950.66 Mar 2nd D III River City Showdown 2025
144 Oberlin Win 13-9 1688.85 Mar 2nd D III River City Showdown 2025
101 North Carolina-Asheville Win 13-10 1776.51 Mar 2nd D III River City Showdown 2025
220 Christopher Newport** Win 13-0 1581.36 Ignored Mar 29th Easterns 2025
178 Dickinson Win 9-2 1752.8 Mar 29th Easterns 2025
292 Mary Washington** Win 13-0 1322.23 Ignored Mar 29th Easterns 2025
80 Williams Loss 9-10 1414.38 Mar 29th Easterns 2025
123 Bates Loss 10-13 1016.35 Mar 30th Easterns 2025
73 Wesleyan Loss 12-15 1286.8 Mar 30th Easterns 2025
77 Richmond Win 12-11 1689.2 Mar 30th Easterns 2025
212 Cedarville Win 15-7 1615.17 Apr 12th Ohio D III Mens Conferences 2025
144 Oberlin Win 15-8 1835.09 Apr 13th Ohio D III Mens Conferences 2025
132 Kenyon Loss 10-11 1190.76 Apr 13th Ohio D III Mens Conferences 2025
212 Cedarville** Win 13-2 1615.17 Ignored Apr 26th Ohio Valley D III College Mens Regionals 2025
170 Messiah Win 13-8 1679.43 Apr 26th Ohio Valley D III College Mens Regionals 2025
180 Grove City Win 13-6 1745.07 Apr 26th Ohio Valley D III College Mens Regionals 2025
241 Xavier** Win 13-5 1499.56 Ignored Apr 26th Ohio Valley D III College Mens Regionals 2025
178 Dickinson Win 15-9 1668.28 Apr 27th Ohio Valley D III College Mens Regionals 2025
180 Grove City Win 15-3 1745.07 Apr 27th Ohio Valley D III College Mens Regionals 2025
132 Kenyon Win 15-10 1769.37 Apr 27th Ohio Valley D III College Mens Regionals 2025
**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)