#68 James Madison (11-10)

avg: 1376.89  •  sd: 51.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
224 American Win 13-7 1289.28 Jan 27th Mid Atlantic Warm Up
142 Boston University Win 11-7 1535.61 Jan 27th Mid Atlantic Warm Up
85 Carnegie Mellon Win 12-6 1897.64 Jan 27th Mid Atlantic Warm Up
70 Case Western Reserve Loss 10-12 1128.59 Jan 27th Mid Atlantic Warm Up
73 Richmond Win 12-10 1602.38 Jan 27th Mid Atlantic Warm Up
85 Carnegie Mellon Loss 7-8 1193.33 Jan 28th Mid Atlantic Warm Up
98 Dartmouth Win 13-11 1474.48 Jan 28th Mid Atlantic Warm Up
84 Appalachian State Win 13-10 1654.94 Feb 10th Queen City Tune Up 2024
106 Notre Dame Win 14-13 1335.32 Feb 10th Queen City Tune Up 2024
34 Ohio State Loss 7-12 1121.36 Feb 10th Queen City Tune Up 2024
13 North Carolina State Loss 9-15 1431.12 Feb 10th Queen City Tune Up 2024
36 North Carolina-Charlotte Loss 13-15 1404.08 Feb 11th Queen City Tune Up 2024
29 South Carolina Loss 10-12 1445.79 Feb 11th Queen City Tune Up 2024
57 Auburn Win 12-9 1792.56 Feb 24th Easterns Qualifier 2024
56 Emory Loss 9-10 1322.58 Feb 24th Easterns Qualifier 2024
28 North Carolina-Wilmington Loss 8-12 1293.35 Feb 24th Easterns Qualifier 2024
169 Rutgers Win 9-6 1370.21 Feb 24th Easterns Qualifier 2024
74 Cincinnati Loss 9-12 1015.83 Feb 25th Easterns Qualifier 2024
154 Harvard Win 12-7 1543.7 Feb 25th Easterns Qualifier 2024
76 Purdue Loss 10-15 903.61 Feb 25th Easterns Qualifier 2024
126 Lehigh Win 10-9 1270.39 Feb 25th Easterns Qualifier 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)