#41 James Madison (15-2)

avg: 1524.02  •  sd: 54.27  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
51 Georgetown Win 10-7 1811.49 Jan 28th Winta Binta Vinta
103 Virginia Commonwealth Win 8-5 1426.3 Jan 28th Winta Binta Vinta
57 Virginia Tech Win 8-6 1637.64 Jan 28th Winta Binta Vinta
14 Virginia Loss 6-10 1434.5 Jan 28th Winta Binta Vinta
80 American Win 8-6 1474.32 Jan 29th Winta Binta Vinta
62 William & Mary Loss 9-10 1152.48 Jan 29th Winta Binta Vinta
103 Virginia Commonwealth Win 9-6 1391.26 Jan 29th Winta Binta Vinta
57 Virginia Tech Win 9-4 1937.15 Feb 18th Commonwealth Cup Weekend1 2023
130 Liberty** Win 13-5 1373.74 Ignored Feb 18th Commonwealth Cup Weekend1 2023
145 Virginia-B Win 11-6 1202.64 Feb 19th Commonwealth Cup Weekend1 2023
135 Mary Washington Win 9-4 1326.75 Feb 19th Commonwealth Cup Weekend1 2023
51 Georgetown Win 8-7 1546.83 Mar 25th Bonanza 2023
135 Mary Washington Win 9-5 1255.81 Mar 25th Bonanza 2023
103 Virginia Commonwealth Win 12-2 1572.7 Mar 25th Bonanza 2023
57 Virginia Tech Win 9-8 1462.15 Mar 25th Bonanza 2023
85 Catholic Win 11-5 1704.85 Mar 26th Bonanza 2023
51 Georgetown Win 11-7 1888.72 Mar 26th Bonanza 2023
**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)