#72 James Madison (10-10)

avg: 1229.16  •  sd: 51.09  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
129 Boston University Win 11-7 1374.64 Jan 27th Mid Atlantic Warm Up
83 Carnegie Mellon Win 12-6 1723.64 Jan 27th Mid Atlantic Warm Up
80 Case Western Reserve Loss 10-12 950.95 Jan 27th Mid Atlantic Warm Up
184 American Win 13-7 1045.83 Jan 27th Mid Atlantic Warm Up
87 Richmond Win 12-10 1364.71 Jan 27th Mid Atlantic Warm Up
83 Carnegie Mellon Loss 7-8 1019.33 Jan 28th Mid Atlantic Warm Up
85 Dartmouth Win 13-11 1360 Jan 28th Mid Atlantic Warm Up
88 Notre Dame Win 14-13 1237.7 Feb 10th Queen City Tune Up 2024
29 Ohio State Loss 7-12 1074.54 Feb 10th Queen City Tune Up 2024
100 Appalachian State Win 13-10 1354.89 Feb 10th Queen City Tune Up 2024
21 North Carolina State Loss 9-15 1209.65 Feb 10th Queen City Tune Up 2024
34 South Carolina Loss 10-12 1322.63 Feb 11th Queen City Tune Up 2024
42 North Carolina-Charlotte Loss 13-15 1253.06 Feb 11th Queen City Tune Up 2024
66 Emory Loss 9-10 1130.29 Feb 24th Easterns Qualifier 2024
25 North Carolina-Wilmington Loss 8-12 1225.99 Feb 24th Easterns Qualifier 2024
61 Auburn Win 12-9 1636.96 Feb 24th Easterns Qualifier 2024
148 Rutgers Win 9-6 1149.16 Feb 24th Easterns Qualifier 2024
114 Harvard Win 12-7 1487.3 Feb 25th Easterns Qualifier 2024
68 Cincinnati Loss 9-12 904.79 Feb 25th Easterns Qualifier 2024
67 Purdue Loss 10-15 800.15 Feb 25th Easterns Qualifier 2024
**Blowout Eligible

FAQ

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
  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
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)