#62 Vermont (7-11)

avg: 1465.83  •  sd: 78.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
1 North Carolina Loss 6-11 1798.64 Feb 3rd Queen City Tune Up 2018 College Open
12 North Carolina State Loss 9-11 1669.65 Feb 3rd Queen City Tune Up 2018 College Open
28 Carnegie Mellon Loss 7-8 1593.65 Feb 3rd Queen City Tune Up 2018 College Open
9 Georgia Loss 5-11 1349.28 Feb 3rd Queen City Tune Up 2018 College Open
22 Tufts Loss 6-11 1203.48 Feb 3rd Queen City Tune Up 2018 College Open
151 George Mason Loss 5-8 663.24 Feb 4th Queen City Tune Up 2018 College Open
33 Maryland Win 12-10 1922.41 Feb 17th Easterns Qualifier 2018
51 Ohio State Loss 9-13 1119.13 Feb 17th Easterns Qualifier 2018
46 South Carolina Loss 10-11 1454.36 Feb 17th Easterns Qualifier 2018
75 Tennessee-Chattanooga Win 10-9 1540.67 Feb 17th Easterns Qualifier 2018
84 Virginia Loss 8-11 1036.53 Feb 17th Easterns Qualifier 2018
149 Davidson Loss 11-13 912.02 Feb 18th Easterns Qualifier 2018
73 Michigan State Loss 14-15 1294.54 Feb 18th Easterns Qualifier 2018
224 Georgia Southern** Win 15-5 1461.24 Ignored Feb 18th Easterns Qualifier 2018
44 Illinois Win 11-10 1714.03 Mar 31st Huck Finn 2018
70 Arkansas Win 12-9 1784.9 Mar 31st Huck Finn 2018
88 Alabama-Huntsville Win 15-11 1769.47 Mar 31st Huck Finn 2018
163 Wisconsin- La Crosse Win 15-10 1532.37 Mar 31st Huck Finn 2018
**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)