#21 Georgia Tech (12-8)

avg: 1854.79  •  sd: 58.88  •  top 16/20: 49.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
45 Elon Win 13-9 2023.61 Feb 1st Carolina Kickoff mens 2025
88 Georgetown Win 12-7 1829.18 Feb 1st Carolina Kickoff mens 2025
32 Virginia Loss 10-13 1351.73 Feb 1st Carolina Kickoff mens 2025
3 North Carolina Win 15-14 2331.12 Feb 2nd Carolina Kickoff mens 2025
49 North Carolina State Win 15-13 1778.98 Feb 2nd Carolina Kickoff mens 2025
27 South Carolina Win 15-11 2160.76 Feb 2nd Carolina Kickoff mens 2025
13 Texas Win 13-11 2199.69 Mar 1st Smoky Mountain Invite 2025
1 Massachusetts Loss 9-11 2009.89 Mar 1st Smoky Mountain Invite 2025
31 Minnesota Loss 12-13 1588.47 Mar 1st Smoky Mountain Invite 2025
5 Oregon Loss 9-15 1678.15 Mar 1st Smoky Mountain Invite 2025
19 Georgia Win 14-13 2017.49 Mar 2nd Smoky Mountain Invite 2025
25 Penn State Loss 10-14 1428.68 Mar 2nd Smoky Mountain Invite 2025
31 Minnesota Win 15-13 1927.65 Mar 2nd Smoky Mountain Invite 2025
6 Cal Poly-SLO Loss 8-13 1631.46 Mar 29th Easterns 2025
4 Carleton College Loss 9-13 1784.74 Mar 29th Easterns 2025
28 Pittsburgh Win 13-11 1993.57 Mar 29th Easterns 2025
37 North Carolina-Wilmington Win 11-7 2101.96 Mar 29th Easterns 2025
19 Georgia Loss 11-15 1511.33 Mar 30th Easterns 2025
31 Minnesota Win 15-11 2094.64 Mar 30th Easterns 2025
37 North Carolina-Wilmington Win 10-9 1760.07 Mar 30th Easterns 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)