#25 Georgia Tech (10-5)

avg: 1776.75  •  sd: 98.4  •  top 16/20: 30.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
41 Alabama Win 11-6 2146.7 Jan 18th TTown Throwdown 2020 Open
114 Jacksonville State Win 9-7 1436.42 Jan 18th TTown Throwdown 2020 Open
276 Mississippi** Win 13-3 1077.49 Ignored Jan 18th TTown Throwdown 2020 Open
65 Tennessee-Chattanooga Win 12-11 1520.9 Jan 18th TTown Throwdown 2020 Open
62 Florida Win 15-9 1934.34 Jan 19th TTown Throwdown 2020 Open
57 Illinois Loss 11-14 1139.57 Jan 19th TTown Throwdown 2020 Open
141 Harvard** Win 13-4 1639.6 Ignored Feb 14th Florida Warm Up 2020 Weekend 1
18 Tufts Loss 9-10 1718.65 Feb 14th Florida Warm Up 2020 Weekend 1
31 Texas-Dallas Loss 7-10 1307.61 Feb 14th Florida Warm Up 2020 Weekend 1
69 Texas A&M Win 13-8 1880.74 Feb 14th Florida Warm Up 2020 Weekend 1
82 Cornell Win 13-5 1917.05 Feb 15th Florida Warm Up 2020 Weekend 1
33 Northwestern Win 13-7 2222.92 Feb 15th Florida Warm Up 2020 Weekend 1
18 Tufts Win 14-7 2426.54 Feb 15th Florida Warm Up 2020 Weekend 1
10 Carleton College-CUT Loss 10-13 1705.49 Feb 16th Florida Warm Up 2020 Weekend 1
30 Texas Loss 10-11 1573.42 Feb 16th Florida Warm Up 2020 Weekend 1
**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)