#124 Cincinnati (12-12)

avg: 911.96  •  sd: 57.71  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
130 Butler Loss 8-9 720.61 Mar 1st Huckleberry Flick 2025
199 Oberlin Win 9-3 1041.28 Mar 1st Huckleberry Flick 2025
214 Dayton Win 9-7 549.21 Mar 1st Huckleberry Flick 2025
236 Miami (Ohio)** Win 12-4 709.99 Ignored Mar 2nd Huckleberry Flick 2025
49 Kenyon Loss 4-15 898.7 Mar 2nd Huckleberry Flick 2025
155 Xavier Win 12-9 1023.26 Mar 2nd Huckleberry Flick 2025
254 Purdue-B** Win 7-2 490.29 Ignored Mar 29th Corny Classic College 2025
200 Truman State Win 13-2 1036.94 Mar 29th Corny Classic College 2025
244 Notre Dame-B** Win 12-1 636.06 Ignored Mar 29th Corny Classic College 2025
146 Knox Loss 6-8 425.91 Mar 29th Corny Classic College 2025
121 Loyola-Chicago Win 5-3 1348.27 Mar 30th Corny Classic College 2025
67 Illinois Loss 5-7 981.45 Mar 30th Corny Classic College 2025
146 Knox Win 5-4 851.4 Mar 30th Corny Classic College 2025
81 Case Western Reserve Win 8-6 1501.3 Apr 12th Ohio D I Womens Conferences 2025
236 Miami (Ohio)** Win 13-1 709.99 Ignored Apr 12th Ohio D I Womens Conferences 2025
32 Ohio Loss 5-11 1071.08 Apr 12th Ohio D I Womens Conferences 2025
22 Ohio State** Loss 5-13 1291.53 Ignored Apr 12th Ohio D I Womens Conferences 2025
71 Carnegie Mellon Loss 5-9 749.28 Apr 26th Ohio Valley D I College Womens Regionals 2025
20 Pennsylvania** Loss 4-11 1312.4 Ignored Apr 26th Ohio Valley D I College Womens Regionals 2025
109 Temple Win 7-6 1146.02 Apr 26th Ohio Valley D I College Womens Regionals 2025
32 Ohio** Loss 0-13 1071.08 Ignored Apr 26th Ohio Valley D I College Womens Regionals 2025
71 Carnegie Mellon Loss 5-11 678.34 Apr 27th Ohio Valley D I College Womens Regionals 2025
31 Pittsburgh** Loss 4-15 1091.14 Ignored Apr 27th Ohio Valley D I College Womens Regionals 2025
75 Penn State Loss 4-11 658.65 Apr 27th Ohio Valley D I College Womens 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)