#56 James Madison (11-7)

avg: 1599.64  •  sd: 42.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
163 Boston University Win 10-4 1701.13 Jan 28th Mid Atlantic Warmup
248 Drexel** Win 13-2 1344.55 Ignored Jan 28th Mid Atlantic Warmup
82 Binghamton Win 9-7 1740.87 Jan 28th Mid Atlantic Warmup
83 RIT Win 9-8 1575.36 Jan 28th Mid Atlantic Warmup
82 Binghamton Win 14-10 1860.24 Jan 29th Mid Atlantic Warmup
41 William & Mary Loss 9-10 1593.88 Jan 29th Mid Atlantic Warmup
37 McGill Win 13-12 1898.37 Feb 25th Easterns Qualifier 2023
77 Temple Win 13-11 1709.16 Feb 25th Easterns Qualifier 2023
27 South Carolina Loss 9-13 1429.61 Feb 25th Easterns Qualifier 2023
131 Georgia State Win 13-10 1570.72 Feb 25th Easterns Qualifier 2023
51 Virginia Loss 12-13 1510.46 Feb 26th Easterns Qualifier 2023
25 North Carolina-Wilmington Loss 10-12 1646.13 Feb 26th Easterns Qualifier 2023
33 Duke Loss 8-11 1425.05 Feb 26th Easterns Qualifier 2023
147 Connecticut Win 7-6 1287.48 Mar 4th Fish Bowl
63 Rutgers Loss 7-9 1289.45 Mar 4th Fish Bowl
97 Delaware Win 11-9 1668.21 Mar 4th Fish Bowl
134 Carnegie Mellon Win 12-5 1836.31 Mar 5th Fish Bowl
36 Penn State Loss 8-10 1514.95 Mar 5th Fish Bowl
**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)