#64 James Madison (12-13)

avg: 1457.38  •  sd: 52.53  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
180 American Win 11-6 1452.43 Jan 25th Mid Atlantic Warm Up 2025
75 Carnegie Mellon Win 11-9 1620.46 Jan 25th Mid Atlantic Warm Up 2025
184 East Carolina Win 11-6 1436.4 Jan 25th Mid Atlantic Warm Up 2025
101 Yale Loss 8-10 999.82 Jan 25th Mid Atlantic Warm Up 2025
206 Christopher Newport Win 14-8 1314.29 Jan 26th Mid Atlantic Warm Up 2025
39 Cincinnati Win 11-9 1879.25 Jan 26th Mid Atlantic Warm Up 2025
60 Michigan State Win 11-9 1735.55 Jan 26th Mid Atlantic Warm Up 2025
52 William & Mary Loss 9-14 1077.62 Jan 26th Mid Atlantic Warm Up 2025
49 North Carolina State Loss 6-13 964.8 Feb 15th Queen City Tune Up 2025
25 Penn State Loss 5-13 1227.38 Feb 15th Queen City Tune Up 2025
51 Purdue Loss 12-13 1430.86 Feb 15th Queen City Tune Up 2025
69 Auburn Win 11-10 1553.39 Feb 22nd Easterns Qualifier 2025
63 Notre Dame Win 13-11 1688.3 Feb 22nd Easterns Qualifier 2025
27 South Carolina Loss 10-13 1451.46 Feb 22nd Easterns Qualifier 2025
128 SUNY-Binghamton Win 13-6 1728.07 Feb 22nd Easterns Qualifier 2025
67 Indiana Win 13-12 1568.25 Feb 23rd Easterns Qualifier 2025
32 Virginia Win 14-13 1804.87 Feb 23rd Easterns Qualifier 2025
37 North Carolina-Wilmington Loss 6-15 1035.07 Feb 23rd Easterns Qualifier 2025
16 Brown Loss 8-13 1423.57 Mar 29th Easterns 2025
2 Colorado** Loss 2-13 1632.88 Ignored Mar 29th Easterns 2025
19 Georgia Loss 8-13 1396.33 Mar 29th Easterns 2025
5 Oregon Loss 7-13 1636.1 Mar 29th Easterns 2025
49 North Carolina State Loss 11-13 1335.96 Mar 30th Easterns 2025
36 Michigan Win 13-11 1881.62 Mar 30th Easterns 2025
28 Pittsburgh Loss 7-13 1207.2 Mar 30th Easterns 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)