#102 Georgetown (9-15)

avg: 1351.18  •  sd: 69.91  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
25 South Carolina Loss 9-12 1441.32 Jan 25th Carolina Kickoff 2019
61 Tennessee Win 11-7 2021.08 Jan 26th Carolina Kickoff 2019
81 Georgia Tech Loss 10-11 1322.32 Jan 26th Carolina Kickoff 2019
26 North Carolina-Wilmington Loss 5-10 1207.08 Jan 26th Carolina Kickoff 2019
25 South Carolina Loss 13-14 1661.69 Jan 27th Carolina Kickoff 2019
73 Temple Win 14-11 1794.21 Jan 27th Carolina Kickoff 2019
155 Elon Win 13-8 1645.74 Feb 16th Easterns Qualifier 2019
87 Case Western Reserve Win 8-7 1547.56 Feb 16th Easterns Qualifier 2019
61 Tennessee Loss 8-13 1058.03 Feb 16th Easterns Qualifier 2019
53 Indiana Win 15-11 2007.79 Feb 17th Easterns Qualifier 2019
39 Vermont Loss 7-15 1105.77 Feb 17th Easterns Qualifier 2019
61 Tennessee Win 15-13 1768.37 Feb 17th Easterns Qualifier 2019
188 East Carolina Loss 12-13 905.36 Mar 16th Oak Creek Invite 2019
101 Connecticut Loss 10-13 1028.1 Mar 16th Oak Creek Invite 2019
110 Williams Win 14-12 1536.78 Mar 16th Oak Creek Invite 2019
32 William & Mary Loss 6-13 1146.68 Mar 16th Oak Creek Invite 2019
150 Cornell Win 15-10 1631.69 Mar 17th Oak Creek Invite 2019
54 Virginia Tech Loss 11-15 1238.28 Mar 17th Oak Creek Invite 2019
66 Penn State Loss 8-13 1039.08 Mar 30th Atlantic Coast Open 2019
35 Middlebury Loss 6-13 1126.5 Mar 30th Atlantic Coast Open 2019
62 Duke Loss 9-13 1132.44 Mar 30th Atlantic Coast Open 2019
137 North Carolina-B Loss 8-11 867.54 Mar 30th Atlantic Coast Open 2019
120 James Madison Loss 11-12 1157.8 Mar 31st Atlantic Coast Open 2019
101 Connecticut Win 14-9 1830.11 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)