#44 Virginia (10-11)

avg: 1671.41  •  sd: 65.85  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
131 Chicago Win 12-8 1707.65 Feb 9th Queen City Tune Up 2019 Men
24 Auburn Loss 6-12 1217.47 Feb 9th Queen City Tune Up 2019 Men
64 Ohio Loss 10-13 1211.26 Feb 9th Queen City Tune Up 2019 Men
26 North Carolina-Wilmington Loss 7-9 1501.64 Feb 9th Queen City Tune Up 2019 Men
24 Auburn Loss 9-15 1281.3 Feb 10th Queen City Tune Up 2019 Men
40 Dartmouth Win 15-9 2201.95 Feb 10th Queen City Tune Up 2019 Men
79 Tulane Win 15-9 1971.9 Feb 10th Queen City Tune Up 2019 Men
33 Johns Hopkins Loss 11-12 1606.17 Feb 16th Easterns Qualifier 2019
101 Connecticut Win 13-2 1956.24 Feb 16th Easterns Qualifier 2019
145 Dayton Win 13-6 1789.68 Feb 16th Easterns Qualifier 2019
88 Tennessee-Chattanooga Win 12-11 1544.19 Feb 16th Easterns Qualifier 2019
53 Indiana Win 14-13 1751.62 Feb 17th Easterns Qualifier 2019
33 Johns Hopkins Win 15-9 2246.65 Feb 17th Easterns Qualifier 2019
39 Vermont Win 15-10 2159.37 Feb 17th Easterns Qualifier 2019
17 Minnesota Loss 9-13 1532.48 Mar 30th Easterns 2019 Men
45 California-Santa Barbara Loss 10-13 1335.11 Mar 30th Easterns 2019 Men
9 Massachusetts Loss 6-13 1465.5 Mar 30th Easterns 2019 Men
3 Oregon Loss 8-13 1692.83 Mar 30th Easterns 2019 Men
43 Harvard Win 12-10 1910.4 Mar 31st Easterns 2019 Men
26 North Carolina-Wilmington Loss 10-12 1542.85 Mar 31st Easterns 2019 Men
28 Northeastern Loss 11-12 1650.83 Mar 31st Easterns 2019 Men
**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)