#47 Maryland (10-11)

avg: 1656.33  •  sd: 40.11  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
66 Penn State Win 10-8 1797.91 Feb 9th Queen City Tune Up 2019 Men
119 Clemson Win 12-10 1521.67 Feb 9th Queen City Tune Up 2019 Men
79 Tulane Win 12-4 2056.42 Feb 9th Queen City Tune Up 2019 Men
9 Massachusetts Loss 8-13 1569.34 Feb 9th Queen City Tune Up 2019 Men
36 Alabama Loss 12-15 1422.64 Feb 10th Queen City Tune Up 2019 Men
26 North Carolina-Wilmington Loss 14-15 1655.98 Feb 10th Queen City Tune Up 2019 Men
64 Ohio Win 15-12 1839.89 Feb 10th Queen City Tune Up 2019 Men
197 George Mason** Win 13-1 1601.39 Ignored Mar 16th Oak Creek Invite 2019
150 Cornell Win 13-9 1596.65 Mar 16th Oak Creek Invite 2019
157 Drexel Win 13-3 1729.41 Mar 16th Oak Creek Invite 2019
73 Temple Win 12-9 1826.24 Mar 16th Oak Creek Invite 2019
33 Johns Hopkins Loss 12-15 1430.67 Mar 17th Oak Creek Invite 2019
163 SUNY-Geneseo Win 15-7 1706.58 Mar 17th Oak Creek Invite 2019
32 William & Mary Loss 11-12 1621.68 Mar 17th Oak Creek Invite 2019
2 Brown Loss 9-13 1810.59 Mar 30th Easterns 2019 Men
24 Auburn Loss 11-13 1567.94 Mar 30th Easterns 2019 Men
28 Northeastern Win 12-10 2013.95 Mar 30th Easterns 2019 Men
11 North Carolina State Loss 8-13 1531.41 Mar 30th Easterns 2019 Men
22 Georgia Loss 13-14 1709.49 Mar 31st Easterns 2019 Men
32 William & Mary Loss 10-12 1508.56 Mar 31st Easterns 2019 Men
49 Northwestern Loss 10-13 1309.55 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)