#33 Maryland (14-10)

avg: 1684.28  •  sd: 50.61  •  top 16/20: 0.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
7 Pittsburgh Win 10-9 2112.46 Feb 3rd Queen City Tune Up 2018 College Open
150 North Carolina-Asheville Win 11-7 1597.98 Feb 3rd Queen City Tune Up 2018 College Open
10 Virginia Tech Loss 10-11 1798.3 Feb 3rd Queen City Tune Up 2018 College Open
39 Northwestern Win 11-7 2095.59 Feb 3rd Queen City Tune Up 2018 College Open
8 Massachusetts Loss 9-11 1714.56 Feb 3rd Queen City Tune Up 2018 College Open
84 Virginia Win 13-9 1820.71 Feb 17th Easterns Qualifier 2018
51 Ohio State Win 12-11 1662.69 Feb 17th Easterns Qualifier 2018
75 Tennessee-Chattanooga Win 13-8 1911.83 Feb 17th Easterns Qualifier 2018
62 Vermont Loss 10-12 1227.71 Feb 17th Easterns Qualifier 2018
46 South Carolina Loss 9-10 1454.36 Feb 17th Easterns Qualifier 2018
124 Indiana Win 15-6 1826.26 Feb 18th Easterns Qualifier 2018
12 North Carolina State Loss 8-15 1354.05 Feb 18th Easterns Qualifier 2018
23 Georgia Tech Win 16-14 1952.26 Feb 18th Easterns Qualifier 2018
103 Delaware Win 13-10 1652.33 Mar 17th Oak Creek Invite 2018
54 Mary Washington Win 13-5 2124.23 Mar 17th Oak Creek Invite 2018
179 SUNY-Binghamton** Win 13-5 1618.07 Ignored Mar 17th Oak Creek Invite 2018
209 SUNY-Buffalo** Win 13-2 1520.42 Ignored Mar 17th Oak Creek Invite 2018
78 Georgetown Win 15-13 1629.25 Mar 18th Oak Creek Invite 2018
34 William & Mary Loss 10-13 1320.06 Mar 18th Oak Creek Invite 2018
42 Connecticut Win 15-12 1896.05 Mar 18th Oak Creek Invite 2018
1 North Carolina** Loss 6-15 1745.34 Ignored Mar 31st Easterns 2018
36 Michigan Loss 12-13 1513.31 Mar 31st Easterns 2018
13 Wisconsin Loss 12-15 1616.63 Mar 31st Easterns 2018
14 Florida Loss 9-15 1371.33 Mar 31st Easterns 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)