#61 James Madison (15-8)

avg: 1435.16  •  sd: 72.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
271 Virginia-B** Win 13-0 416.54 Ignored Jan 26th Winta Binta Vinta Fest 2019
82 Georgetown Win 10-6 1762.62 Jan 26th Winta Binta Vinta Fest 2019
40 Michigan Loss 4-8 1004.63 Jan 26th Winta Binta Vinta Fest 2019
157 Virginia Commonwealth Win 11-7 1308.8 Jan 26th Winta Binta Vinta Fest 2019
45 Virginia Loss 6-8 1245.2 Jan 27th Winta Binta Vinta Fest 2019
59 Duke Win 8-6 1746.45 Jan 27th Winta Binta Vinta Fest 2019
231 Pennsylvania-B** Win 12-0 917.18 Ignored Feb 23rd Commonwealth Cup 2019
135 Princeton Win 12-3 1542.48 Feb 23rd Commonwealth Cup 2019
91 Case Western Reserve Win 13-9 1621.23 Feb 23rd Commonwealth Cup 2019
70 Maryland Loss 8-9 1203.26 Feb 24th Commonwealth Cup 2019
27 Delaware Loss 2-9 1214.9 Feb 24th Commonwealth Cup 2019
27 Delaware Loss 9-13 1396.33 Mar 16th Bonanza 2019
56 Pennsylvania Loss 8-10 1224.7 Mar 16th Bonanza 2019
157 Virginia Commonwealth Win 15-4 1441.9 Mar 16th Bonanza 2019
82 Georgetown Win 13-7 1823.99 Mar 17th Bonanza 2019
27 Delaware Loss 8-12 1373.74 Mar 17th Bonanza 2019
47 Williams Loss 9-11 1277.21 Mar 17th Bonanza 2019
157 Virginia Commonwealth Win 13-3 1441.9 Mar 30th Atlantic Coast Open 2019
259 East Carolina** Win 13-5 673.16 Ignored Mar 30th Atlantic Coast Open 2019
130 Connecticut Win 12-2 1592.45 Mar 30th Atlantic Coast Open 2019
182 George Mason** Win 12-3 1228.31 Ignored Mar 30th Atlantic Coast Open 2019
157 Virginia Commonwealth Win 11-3 1441.9 Mar 31st Atlantic Coast Open 2019
71 William & Mary Win 13-12 1451.62 Mar 31st Atlantic Coast Open 2019
**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)