#101 Connecticut (9-11)

avg: 1356.24  •  sd: 62.6  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
33 Johns Hopkins Loss 8-11 1365.56 Feb 16th Easterns Qualifier 2019
145 Dayton Win 12-9 1535.04 Feb 16th Easterns Qualifier 2019
88 Tennessee-Chattanooga Win 12-9 1764.55 Feb 16th Easterns Qualifier 2019
44 Virginia Loss 2-13 1071.41 Feb 16th Easterns Qualifier 2019
120 James Madison Loss 9-14 808.94 Feb 17th Easterns Qualifier 2019
87 Case Western Reserve Loss 10-15 968.96 Feb 17th Easterns Qualifier 2019
119 Clemson Loss 12-13 1158.55 Feb 17th Easterns Qualifier 2019
188 East Carolina Win 13-8 1526.52 Mar 16th Oak Creek Invite 2019
110 Williams Win 13-6 1915.82 Mar 16th Oak Creek Invite 2019
32 William & Mary Loss 9-11 1497.48 Mar 16th Oak Creek Invite 2019
102 Georgetown Win 13-10 1679.33 Mar 16th Oak Creek Invite 2019
33 Johns Hopkins Loss 13-15 1516.99 Mar 17th Oak Creek Invite 2019
66 Penn State Loss 11-15 1154.08 Mar 17th Oak Creek Invite 2019
163 SUNY-Geneseo Win 15-12 1407.07 Mar 17th Oak Creek Invite 2019
120 James Madison Win 10-6 1778.96 Mar 30th Atlantic Coast Open 2019
33 Johns Hopkins Win 9-8 1856.17 Mar 30th Atlantic Coast Open 2019
83 Rutgers Loss 6-12 853.66 Mar 30th Atlantic Coast Open 2019
118 MIT Loss 11-13 1058.89 Mar 30th Atlantic Coast Open 2019
102 Georgetown Loss 9-14 877.32 Mar 31st Atlantic Coast Open 2019
195 George Washington Win 10-8 1266.48 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)