#32 Ohio (19-5)

avg: 1671.76  •  sd: 82.57  •  top 16/20: 0.3%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
59 Central Florida Win 11-4 1997.07 Feb 22nd 2025 Commonwealth Cup Weekend 2
76 Columbia Win 11-4 1819.45 Feb 22nd 2025 Commonwealth Cup Weekend 2
53 Maryland Win 10-4 2057.11 Feb 22nd 2025 Commonwealth Cup Weekend 2
45 American Loss 10-12 1304.29 Feb 23rd 2025 Commonwealth Cup Weekend 2
62 Brown Win 10-4 1951.28 Feb 23rd 2025 Commonwealth Cup Weekend 2
33 Georgia Tech Win 10-8 1914.27 Feb 23rd 2025 Commonwealth Cup Weekend 2
5 Vermont** Loss 4-14 1689.71 Ignored Feb 23rd 2025 Commonwealth Cup Weekend 2
163 Alabama Win 13-6 1240.88 Mar 22nd Moxie Madness 2025
233 Auburn** Win 13-0 724.76 Ignored Mar 22nd Moxie Madness 2025
155 Berry Win 11-7 1144.32 Mar 22nd Moxie Madness 2025
209 Vanderbilt** Win 13-1 880.82 Ignored Mar 23rd Moxie Madness 2025
154 Xavier** Win 13-2 1277.47 Ignored Mar 23rd Moxie Madness 2025
71 Union (Tennessee) Win 11-2 1870.35 Mar 23rd Moxie Madness 2025
80 Case Western Reserve Win 13-0 1801.55 Apr 12th Ohio D I Womens Conferences 2025
122 Cincinnati Win 11-5 1511.56 Apr 12th Ohio D I Womens Conferences 2025
235 Miami (Ohio)** Win 13-0 708.1 Ignored Apr 12th Ohio D I Womens Conferences 2025
22 Ohio State Loss 7-8 1768.06 Apr 12th Ohio D I Womens Conferences 2025
70 Carnegie Mellon Win 10-6 1775.31 Apr 26th Ohio Valley D I College Womens Regionals 2025
122 Cincinnati** Win 13-0 1511.56 Ignored Apr 26th Ohio Valley D I College Womens Regionals 2025
20 Pennsylvania Loss 5-6 1787.93 Apr 26th Ohio Valley D I College Womens Regionals 2025
109 Temple Win 7-5 1349.83 Apr 26th Ohio Valley D I College Womens Regionals 2025
74 Penn State Win 15-9 1775.21 Apr 27th Ohio Valley D I College Womens Regionals 2025
31 Pittsburgh Win 8-7 1819.22 Apr 27th Ohio Valley D I College Womens Regionals 2025
20 Pennsylvania Loss 9-13 1494.37 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)