#52 William & Mary (16-9)

avg: 1551.49  •  sd: 61.11  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
206 Christopher Newport Win 13-7 1335.79 Jan 25th Mid Atlantic Warm Up 2025
298 Navy** Win 13-0 988.51 Ignored Jan 25th Mid Atlantic Warm Up 2025
179 Pennsylvania** Win 13-3 1511.9 Ignored Jan 25th Mid Atlantic Warm Up 2025
115 Vermont-B Win 13-7 1752.25 Jan 25th Mid Atlantic Warm Up 2025
75 Carnegie Mellon Win 15-9 1886.73 Jan 26th Mid Atlantic Warm Up 2025
64 James Madison Win 14-9 1931.25 Jan 26th Mid Atlantic Warm Up 2025
78 Richmond Win 13-8 1844.19 Jan 26th Mid Atlantic Warm Up 2025
35 Chicago Loss 12-13 1529.33 Feb 15th Queen City Tune Up 2025
17 Tufts Loss 12-13 1772.01 Feb 15th Queen City Tune Up 2025
48 Maryland Loss 12-13 1440.66 Feb 15th Queen City Tune Up 2025
60 Michigan State Win 9-8 1611.35 Feb 16th Queen City Tune Up 2025
81 North Carolina-Charlotte Win 10-7 1711.92 Feb 16th Queen City Tune Up 2025
96 Appalachian State Win 12-7 1794.93 Feb 22nd Easterns Qualifier 2025
66 Dartmouth Win 13-8 1948.41 Feb 22nd Easterns Qualifier 2025
67 Indiana Loss 8-10 1180.58 Feb 22nd Easterns Qualifier 2025
49 North Carolina State Win 11-8 1930.41 Feb 22nd Easterns Qualifier 2025
37 North Carolina-Wilmington Loss 9-13 1216.5 Feb 23rd Easterns Qualifier 2025
27 South Carolina Win 15-12 2080.09 Feb 23rd Easterns Qualifier 2025
32 Virginia Loss 9-12 1334.51 Feb 23rd Easterns Qualifier 2025
71 Case Western Reserve Loss 10-11 1280.06 Mar 29th East Coast Invite 2025
120 Connecticut Win 11-6 1725.72 Mar 29th East Coast Invite 2025
88 Georgetown Win 10-9 1433.67 Mar 29th East Coast Invite 2025
101 Yale Win 11-10 1387.48 Mar 29th East Coast Invite 2025
75 Carnegie Mellon Loss 8-11 1005.64 Mar 30th East Coast Invite 2025
56 Cornell Loss 11-14 1210.6 Mar 30th East Coast Invite 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)