#37 William & Mary (15-9)

avg: 1601.64  •  sd: 57.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
128 North Carolina-Wilmington** Win 13-2 1447.3 Ignored Feb 15th Queen City Tune Up 2025
5 Vermont** Loss 4-13 1686.29 Ignored Feb 15th Queen City Tune Up 2025
35 Wisconsin Win 13-9 2051.96 Feb 15th Queen City Tune Up 2025
11 Michigan Loss 2-11 1510.35 Feb 16th Queen City Tune Up 2025
24 Minnesota Win 8-7 1946.36 Feb 16th Queen City Tune Up 2025
47 American Loss 7-9 1262.23 Mar 29th East Coast Invite 2025
22 Ohio State Loss 4-11 1291.53 Mar 29th East Coast Invite 2025
75 Penn State Win 8-7 1383.65 Mar 29th East Coast Invite 2025
39 Wesleyan Loss 10-11 1461.83 Mar 29th East Coast Invite 2025
64 Connecticut Win 5-2 1919.26 Mar 30th East Coast Invite 2025
31 Pittsburgh Loss 5-12 1091.14 Mar 30th East Coast Invite 2025
109 Temple Win 13-3 1621.02 Mar 30th East Coast Invite 2025
57 James Madison Win 8-6 1705.33 Apr 12th Virginia D I Womens Conferences 2025
50 Liberty Win 11-7 1955.89 Apr 12th Virginia D I Womens Conferences 2025
21 Virginia Win 10-9 2024.53 Apr 12th Virginia D I Womens Conferences 2025
103 Virginia Tech Win 9-6 1467.23 Apr 12th Virginia D I Womens Conferences 2025
50 Liberty Win 10-7 1878.66 Apr 13th Virginia D I Womens Conferences 2025
159 Virginia Commonwealth** Win 13-2 1266.49 Ignored Apr 13th Virginia D I Womens Conferences 2025
43 Duke Loss 11-13 1335.56 Apr 26th Atlantic Coast D I College Womens Regionals 2025
140 George Washington** Win 14-1 1391.33 Ignored Apr 26th Atlantic Coast D I College Womens Regionals 2025
53 Maryland Win 10-5 2030.21 Apr 26th Atlantic Coast D I College Womens Regionals 2025
25 Georgetown Loss 8-12 1358.64 Apr 27th Atlantic Coast D I College Womens Regionals 2025
57 James Madison Win 12-10 1642.96 Apr 27th Atlantic Coast D I College Womens Regionals 2025
21 Virginia Loss 7-12 1379.02 Apr 27th Atlantic Coast 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)