#54 Virginia Tech (11-11)

avg: 1619.44  •  sd: 60.95  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
55 Florida State Loss 9-13 1193.11 Feb 8th Florida Warm Up 2019
15 Central Florida Loss 6-13 1390.32 Feb 8th Florida Warm Up 2019
13 Wisconsin Win 13-12 2125.97 Feb 8th Florida Warm Up 2019
136 South Florida Win 11-8 1602.64 Feb 9th Florida Warm Up 2019
72 Alabama-Huntsville Loss 8-9 1358.99 Feb 9th Florida Warm Up 2019
127 Boston College Win 13-7 1832.25 Feb 9th Florida Warm Up 2019
31 Texas A&M Loss 5-11 1148.41 Feb 9th Florida Warm Up 2019
98 Kansas Win 12-11 1488.18 Feb 10th Florida Warm Up 2019
73 Temple Win 12-10 1718.99 Feb 10th Florida Warm Up 2019
33 Johns Hopkins Loss 8-13 1235.01 Mar 16th Oak Creek Invite 2019
66 Penn State Loss 11-13 1306.4 Mar 16th Oak Creek Invite 2019
108 North Carolina-Charlotte Win 13-3 1925.07 Mar 16th Oak Creek Invite 2019
204 SUNY-Buffalo** Win 13-4 1571.8 Ignored Mar 16th Oak Creek Invite 2019
147 Delaware Win 15-8 1752.75 Mar 17th Oak Creek Invite 2019
102 Georgetown Win 15-11 1732.35 Mar 17th Oak Creek Invite 2019
7 Carleton College-CUT Loss 7-13 1561.11 Mar 30th Easterns 2019 Men
43 Harvard Win 13-9 2090.84 Mar 30th Easterns 2019 Men
22 Georgia Loss 10-12 1596.37 Mar 30th Easterns 2019 Men
1 North Carolina Loss 11-13 2003.08 Mar 30th Easterns 2019 Men
24 Auburn Loss 12-13 1671.78 Mar 31st Easterns 2019 Men
49 Northwestern Win 12-11 1762.69 Mar 31st Easterns 2019 Men
32 William & Mary Loss 6-11 1199.99 Mar 31st Easterns 2019 Men
**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)