#252 American-B (4-15)

avg: -60.35  •  sd: 97.39  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
36 Haverford/Bryn Mawr** Loss 0-13 1019.05 Ignored Mar 1st Cherry Blossom Classic 2025
169 Miami (Florida) Loss 4-7 112.88 Mar 1st Cherry Blossom Classic 2025
166 East Carolina** Loss 1-4 25.91 Ignored Mar 1st Cherry Blossom Classic 2025
207 Towson Loss 2-5 -298.71 Mar 1st Cherry Blossom Classic 2025
244 Pennsylvania-B Win 7-0 635.03 Mar 2nd Cherry Blossom Classic 2025
168 Delaware** Loss 2-12 9.22 Ignored Mar 2nd Cherry Blossom Classic 2025
179 George Mason** Loss 2-8 -56.22 Ignored Mar 2nd Cherry Blossom Classic 2025
179 George Mason** Loss 4-11 -56.22 Ignored Mar 22nd Atlantic Coast Open 2025
84 Richmond** Loss 1-15 548.86 Ignored Mar 22nd Atlantic Coast Open 2025
223 Dickinson Loss 2-8 -394.85 Mar 22nd Atlantic Coast Open 2025
50 Liberty** Loss 0-15 889.97 Ignored Mar 22nd Atlantic Coast Open 2025
223 Dickinson Loss 7-10 -184.51 Mar 23rd Atlantic Coast Open 2025
179 George Mason** Loss 4-10 -56.22 Ignored Mar 23rd Atlantic Coast Open 2025
211 Georgetown-B Loss 5-8 -179.3 Apr 12th Atlantic Coast Dev Womens Conferences 2025
260 Virginia-B Win 7-5 -81.44 Apr 12th Atlantic Coast Dev Womens Conferences 2025
238 William & Mary-B Win 5-4 200.82 Apr 12th Atlantic Coast Dev Womens Conferences 2025
226 South Carolina-B Loss 5-10 -386.43 Apr 12th Atlantic Coast Dev Womens Conferences 2025
151 North Carolina-B** Loss 1-13 89.22 Ignored Apr 13th Atlantic Coast Dev Womens Conferences 2025
260 Virginia-B Win 8-1 190.42 Apr 13th Atlantic Coast Dev Womens Conferences 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)