#81 Georgia Tech (7-12)

avg: 1447.32  •  sd: 67.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
102 Georgetown Win 11-10 1476.18 Jan 26th Carolina Kickoff 2019
26 North Carolina-Wilmington Win 9-8 1905.98 Jan 26th Carolina Kickoff 2019
61 Tennessee Win 11-8 1919.8 Jan 26th Carolina Kickoff 2019
69 Emory Loss 10-13 1180.32 Jan 27th Carolina Kickoff 2019
78 Carleton College-GoP Win 13-9 1876.28 Jan 27th Carolina Kickoff 2019
52 Notre Dame Loss 13-15 1412.49 Jan 27th Carolina Kickoff 2019
53 Indiana Loss 9-12 1281.26 Feb 16th Easterns Qualifier 2019
119 Clemson Win 13-9 1702.12 Feb 16th Easterns Qualifier 2019
64 Ohio Win 12-8 1980.55 Feb 16th Easterns Qualifier 2019
197 George Mason Win 13-10 1329.54 Feb 16th Easterns Qualifier 2019
53 Indiana Loss 10-11 1501.62 Feb 17th Easterns Qualifier 2019
33 Johns Hopkins Loss 12-15 1430.67 Feb 17th Easterns Qualifier 2019
61 Tennessee Loss 6-15 954.19 Feb 17th Easterns Qualifier 2019
57 Carnegie Mellon Loss 8-12 1146.22 Mar 9th Classic City Invite 2019
22 Georgia Loss 10-11 1709.49 Mar 9th Classic City Invite 2019
28 Northeastern Loss 7-13 1218.3 Mar 9th Classic City Invite 2019
26 North Carolina-Wilmington Loss 8-13 1284.82 Mar 9th Classic City Invite 2019
48 Kennesaw State Loss 9-13 1227.92 Mar 10th Classic City Invite 2019
61 Tennessee Loss 9-11 1304.98 Mar 10th Classic City Invite 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)