#145 Oberlin (18-8)

avg: 1284.8  •  sd: 60.11  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
203 North Carolina State-B Win 10-4 1655.73 Feb 15th 2025 Commonwealth Cup Weekend 1
15 Davenport Loss 5-9 1530.53 Feb 16th 2025 Commonwealth Cup Weekend 1
112 Liberty Win 7-6 1530.59 Feb 16th 2025 Commonwealth Cup Weekend 1
231 Air Force Win 13-9 1360.59 Mar 1st D III River City Showdown 2025
60 Carleton College-CHOP Loss 6-13 1061.88 Mar 1st D III River City Showdown 2025
142 Davidson Win 13-10 1619.58 Mar 1st D III River City Showdown 2025
282 Navy Win 13-5 1363.93 Mar 1st D III River City Showdown 2025
67 Franciscan Loss 9-13 1219.68 Mar 2nd D III River City Showdown 2025
78 Richmond Win 11-9 1831.67 Mar 2nd D III River City Showdown 2025
139 Puget Sound Win 13-11 1534.15 Mar 2nd D III River City Showdown 2025
243 Kent State Win 12-8 1347.91 Mar 15th Spring Spook
346 Wright State Win 11-6 1016.93 Mar 15th Spring Spook
243 Kent State Win 15-9 1422.24 Mar 16th Spring Spook
153 Kentucky Win 14-7 1848.47 Mar 16th Spring Spook
260 Toledo Win 15-8 1402.02 Mar 16th Spring Spook
131 Kenyon Loss 12-13 1204.6 Apr 12th Ohio D III Mens Conferences 2025
241 Xavier Win 11-9 1162.48 Apr 12th Ohio D III Mens Conferences 2025
209 Cedarville Loss 9-10 906.4 Apr 13th Ohio D III Mens Conferences 2025
67 Franciscan Loss 8-15 1073.44 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 Win 10-6 1574.63 Apr 26th Ohio Valley D III College Mens Regionals 2025
131 Kenyon Win 10-9 1454.6 Apr 26th Ohio Valley D III College Mens Regionals 2025
234 Scranton Win 13-7 1486.08 Apr 26th Ohio Valley D III College Mens Regionals 2025
180 Grove City Loss 5-15 558.45 Apr 27th Ohio Valley D III College Mens Regionals 2025
131 Kenyon Loss 7-15 729.6 Apr 27th Ohio Valley D III College Mens Regionals 2025
241 Xavier Win 13-11 1142.12 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)