#81 Case Western Reserve (10-13)

avg: 1200.8  •  sd: 72.07  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
108 Alabama-Huntsville Win 13-8 1519.46 Feb 15th Queen City Tune Up 2025
24 Minnesota** Loss 2-13 1221.36 Ignored Feb 15th Queen City Tune Up 2025
9 North Carolina** Loss 1-13 1518.27 Ignored Feb 15th Queen City Tune Up 2025
65 Florida Loss 4-5 1193.59 Feb 16th Queen City Tune Up 2025
60 South Carolina Loss 1-7 784.06 Feb 16th Queen City Tune Up 2025
213 Georgia-B** Win 13-1 872.97 Ignored Mar 29th Needle in a Ho Stack 2025
100 Emory Loss 6-9 656.55 Mar 29th Needle in a Ho Stack 2025
128 North Carolina-Wilmington Win 13-1 1447.3 Mar 29th Needle in a Ho Stack 2025
84 Clemson Win 9-4 1763.38 Mar 29th Needle in a Ho Stack 2025
80 Appalachian State Loss 4-14 614.74 Mar 30th Needle in a Ho Stack 2025
127 Georgia College Win 11-3 1456.5 Mar 30th Needle in a Ho Stack 2025
103 Virginia Tech Win 9-6 1467.23 Mar 30th Needle in a Ho Stack 2025
32 Ohio Loss 0-13 1071.08 Apr 12th Ohio D I Womens Conferences 2025
22 Ohio State** Loss 2-13 1291.53 Ignored Apr 12th Ohio D I Womens Conferences 2025
124 Cincinnati Loss 6-8 611.47 Apr 12th Ohio D I Womens Conferences 2025
236 Miami (Ohio)** Win 13-3 709.99 Ignored Apr 12th Ohio D I Womens Conferences 2025
75 Penn State Loss 5-8 805.04 Apr 26th Ohio Valley D I College Womens Regionals 2025
22 Ohio State** Loss 5-13 1291.53 Ignored Apr 26th Ohio Valley D I College Womens Regionals 2025
115 West Chester Win 9-6 1369.53 Apr 26th Ohio Valley D I College Womens Regionals 2025
31 Pittsburgh Loss 3-13 1091.14 Apr 26th Ohio Valley D I College Womens Regionals 2025
71 Carnegie Mellon Win 8-7 1403.34 Apr 27th Ohio Valley D I College Womens Regionals 2025
20 Pennsylvania Loss 7-13 1354.87 Apr 27th Ohio Valley D I College Womens Regionals 2025
75 Penn State Win 9-6 1677.21 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)