#28 Georgia Tech (16-9)

avg: 1988.02  •  sd: 53.27  •  top 16/20: 5.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
11 Minnesota Loss 11-13 2010.71 Feb 2nd Florida Warm Up 2024
6 Pittsburgh Loss 10-13 2004.67 Feb 2nd Florida Warm Up 2024
38 Texas A&M Win 12-5 2485.46 Feb 2nd Florida Warm Up 2024
8 Brown Loss 8-13 1780.23 Feb 3rd Florida Warm Up 2024
46 Florida Win 15-11 2167.04 Feb 3rd Florida Warm Up 2024
89 Florida State Win 13-2 2151.28 Feb 3rd Florida Warm Up 2024
20 Washington University Loss 13-14 1961.16 Feb 4th Florida Warm Up 2024
57 Virginia Win 13-7 2296.36 Feb 24th Easterns Qualifier 2024
31 North Carolina-Charlotte Loss 8-11 1555.28 Feb 24th Easterns Qualifier 2024
96 Notre Dame Loss 10-11 1405.99 Feb 24th Easterns Qualifier 2024
152 Harvard** Win 13-5 1911.24 Ignored Feb 24th Easterns Qualifier 2024
47 Alabama Win 15-9 2300.29 Feb 25th Easterns Qualifier 2024
54 Emory Win 15-7 2355.32 Feb 25th Easterns Qualifier 2024
97 Lehigh Win 13-8 2022.5 Feb 25th Easterns Qualifier 2024
59 William & Mary Win 11-9 1976.51 Feb 25th Easterns Qualifier 2024
54 Emory Win 14-13 1880.32 Apr 13th Southern Appalachian D I Mens Conferences 2024
246 Georgia Southern** Win 15-3 1570.37 Ignored Apr 13th Southern Appalachian D I Mens Conferences 2024
99 Tennessee-Chattanooga Win 15-6 2118.93 Apr 13th Southern Appalachian D I Mens Conferences 2024
3 Georgia Loss 11-15 2075.74 Apr 14th Southern Appalachian D I Mens Conferences 2024
90 Tennessee Win 15-8 2108.65 Apr 14th Southern Appalachian D I Mens Conferences 2024
90 Tennessee Win 11-3 2143.84 Apr 14th Southern Appalachian D I Mens Conferences 2024
47 Alabama Loss 10-12 1546.68 Apr 27th Southeast D I College Mens Regionals 2024
48 Auburn Loss 10-11 1657.29 Apr 27th Southeast D I College Mens Regionals 2024
89 Florida State Win 13-8 2047.44 Apr 27th Southeast D I College Mens Regionals 2024
111 Vanderbilt Win 15-7 2057.16 Apr 27th Southeast D I College Mens Regionals 2024
**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)