#43 Georgia Tech (8-14)

avg: 1555.59  •  sd: 54.7  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
3 Ohio State Loss 10-12 2134.88 Jan 19th Florida Winter Classic 2019
38 Florida Loss 7-8 1486.11 Jan 19th Florida Winter Classic 2019
51 Florida State Loss 5-6 1383.35 Jan 19th Florida Winter Classic 2019
112 Central Florida Win 9-2 1654.26 Jan 19th Florida Winter Classic 2019
3 Ohio State Loss 6-12 1793.69 Jan 20th Florida Winter Classic 2019
20 North Carolina-Wilmington Loss 8-9 1835.18 Jan 20th Florida Winter Classic 2019
49 Emory Win 8-5 1972.03 Jan 20th Florida Winter Classic 2019
112 Central Florida Win 13-2 1654.26 Jan 20th Florida Winter Classic 2019
1 North Carolina** Loss 3-13 1930.07 Ignored Feb 9th Queen City Tune Up 2019 Women
20 North Carolina-Wilmington Loss 5-13 1360.18 Feb 9th Queen City Tune Up 2019 Women
32 Brigham Young Loss 8-10 1447.82 Feb 9th Queen City Tune Up 2019 Women
91 Case Western Reserve Win 11-6 1749.36 Feb 9th Queen City Tune Up 2019 Women
69 Notre Dame Win 15-7 1928.85 Feb 10th Queen City Tune Up 2019 Women
45 Virginia Loss 10-11 1420.7 Feb 10th Queen City Tune Up 2019 Women
41 Harvard Loss 7-9 1288.32 Feb 10th Queen City Tune Up 2019 Women
90 Colorado State Win 8-5 1670.73 Mar 23rd Womens College Centex 2019
59 Duke Loss 7-8 1320.96 Mar 23rd Womens College Centex 2019
30 Utah Loss 4-10 1158.87 Mar 23rd Womens College Centex 2019
113 Oklahoma Win 15-4 1652.65 Mar 24th Womens College Centex 2019
51 Florida State Loss 7-8 1383.35 Mar 24th Womens College Centex 2019
45 Virginia Loss 9-10 1420.7 Mar 24th Womens College Centex 2019
72 Texas-Dallas Win 9-8 1451.4 Mar 24th Womens College Centex 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)