#61 William & Mary (14-13)

avg: 1432.01  •  sd: 56.5  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
96 Connecticut Win 10-9 1374.4 Jan 27th Mid Atlantic Warm Up
98 Dartmouth Loss 9-10 1120.64 Jan 27th Mid Atlantic Warm Up
123 Pennsylvania Win 13-7 1705.01 Jan 27th Mid Atlantic Warm Up
298 Mary Washington Win 13-7 921.31 Jan 27th Mid Atlantic Warm Up
142 Boston University Win 13-6 1668.71 Jan 28th Mid Atlantic Warm Up
85 Carnegie Mellon Win 12-11 1443.33 Jan 28th Mid Atlantic Warm Up
73 Richmond Loss 13-14 1239.26 Jan 28th Mid Atlantic Warm Up
70 Case Western Reserve Win 12-10 1604.83 Feb 10th Queen City Tune Up 2024
154 Harvard Loss 10-13 695.04 Feb 10th Queen City Tune Up 2024
36 North Carolina-Charlotte Loss 13-14 1493.26 Feb 10th Queen City Tune Up 2024
16 Penn State Loss 11-14 1607.89 Feb 10th Queen City Tune Up 2024
84 Appalachian State Win 13-12 1451.8 Feb 11th Queen City Tune Up 2024
48 Missouri Loss 6-9 1096.2 Feb 11th Queen City Tune Up 2024
34 Ohio State Loss 10-13 1313.73 Feb 24th Easterns Qualifier 2024
29 South Carolina Loss 10-12 1445.79 Feb 24th Easterns Qualifier 2024
111 SUNY-Binghamton Win 13-6 1791.72 Feb 24th Easterns Qualifier 2024
60 Temple Loss 10-11 1310.02 Feb 24th Easterns Qualifier 2024
74 Cincinnati Loss 4-11 761.2 Feb 25th Easterns Qualifier 2024
27 Georgia Tech Loss 9-11 1490.93 Feb 25th Easterns Qualifier 2024
158 Kennesaw State Win 13-10 1338.86 Feb 25th Easterns Qualifier 2024
52 Virginia Tech Win 12-11 1600.52 Feb 25th Easterns Qualifier 2024
84 Appalachian State Win 12-9 1672.17 Mar 30th Atlantic Coast Open 2024
38 Duke Loss 12-15 1290.26 Mar 30th Atlantic Coast Open 2024
97 Florida State Win 15-6 1847.77 Mar 30th Atlantic Coast Open 2024
87 Tennessee-Chattanooga Win 15-9 1825.46 Mar 30th Atlantic Coast Open 2024
38 Duke Loss 13-15 1376.58 Mar 31st Atlantic Coast Open 2024
56 Emory Win 15-14 1572.58 Mar 31st Atlantic Coast Open 2024
**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)