#131 Kenyon (16-8)

avg: 1329.6  •  sd: 62.38  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
67 Franciscan Loss 11-12 1513.25 Mar 1st D III River City Showdown 2025
169 Michigan Tech Win 9-7 1483.34 Mar 1st D III River City Showdown 2025
102 North Carolina-Asheville Loss 5-10 885.97 Mar 1st D III River City Showdown 2025
81 Rochester Loss 3-13 955.12 Mar 1st D III River City Showdown 2025
142 Davidson Loss 9-10 1166.44 Mar 2nd D III River City Showdown 2025
282 Navy Win 13-6 1363.93 Mar 2nd D III River City Showdown 2025
170 Messiah Win 12-11 1320.06 Mar 2nd D III River City Showdown 2025
384 Carthage** Win 13-3 819.74 Ignored Mar 22nd Meltdown 2025
308 Illinois-Chicago** Win 13-3 1246.69 Ignored Mar 22nd Meltdown 2025
273 Minnesota State-Mankato Win 13-4 1403.78 Mar 22nd Meltdown 2025
72 St Olaf Win 12-11 1731.39 Mar 22nd Meltdown 2025
163 Truman State Loss 7-8 1097.22 Mar 23rd Meltdown 2025
204 Winona State Win 10-7 1435.59 Mar 23rd Meltdown 2025
145 Oberlin Win 13-12 1409.8 Apr 12th Ohio D III Mens Conferences 2025
241 Xavier Win 15-5 1513.28 Apr 12th Ohio D III Mens Conferences 2025
209 Cedarville Win 14-11 1344.74 Apr 13th Ohio D III Mens Conferences 2025
67 Franciscan Win 11-10 1763.25 Apr 13th Ohio D III Mens Conferences 2025
179 Dickinson Win 10-9 1291.25 Apr 26th Ohio Valley D III College Mens Regionals 2025
197 Haverford Loss 6-13 478.47 Apr 26th Ohio Valley D III College Mens Regionals 2025
234 Scranton Win 13-9 1347.11 Apr 26th Ohio Valley D III College Mens Regionals 2025
145 Oberlin Loss 9-10 1159.8 Apr 26th Ohio Valley D III College Mens Regionals 2025
67 Franciscan Loss 10-15 1184.64 Apr 27th Ohio Valley D III College Mens Regionals 2025
170 Messiah Win 14-12 1416.02 Apr 27th Ohio Valley D III College Mens Regionals 2025
145 Oberlin Win 15-7 1884.8 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)