#51 James Madison (11-7)

avg: 1437.21  •  sd: 44.32  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
135 Boston University Win 10-4 1621.33 Jan 28th Mid Atlantic Warmup
235 Drexel** Win 13-2 1170.94 Ignored Jan 28th Mid Atlantic Warmup
74 Binghamton Win 9-7 1597.95 Jan 28th Mid Atlantic Warmup
72 RIT Win 9-8 1475.15 Jan 28th Mid Atlantic Warmup
74 Binghamton Win 14-10 1717.31 Jan 29th Mid Atlantic Warmup
39 William & Mary Loss 9-10 1410.44 Jan 29th Mid Atlantic Warmup
34 McGill Win 13-12 1747.7 Feb 25th Easterns Qualifier 2023
99 Temple Win 13-11 1426.25 Feb 25th Easterns Qualifier 2023
17 South Carolina Loss 9-13 1355.31 Feb 25th Easterns Qualifier 2023
117 Georgia State Win 13-10 1438.71 Feb 25th Easterns Qualifier 2023
50 Virginia Loss 12-13 1318.6 Feb 26th Easterns Qualifier 2023
25 North Carolina-Wilmington Loss 10-12 1456.01 Feb 26th Easterns Qualifier 2023
40 Duke Loss 8-11 1164.98 Feb 26th Easterns Qualifier 2023
127 Connecticut Win 7-6 1171.34 Mar 4th Fish Bowl
46 Rutgers Loss 7-9 1182.13 Mar 4th Fish Bowl
83 Delaware Win 11-9 1522.16 Mar 4th Fish Bowl
122 Carnegie Mellon Win 12-5 1682.27 Mar 5th Fish Bowl
42 Penn State Loss 8-10 1263.05 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)