#43 Virginia Tech (14-8)

avg: 1612.2  •  sd: 47.68  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
4 Carleton College Loss 2-13 1603.31 Jan 31st Florida Warm Up 2025
47 McGill Win 10-9 1716.44 Jan 31st Florida Warm Up 2025
13 Texas Loss 6-13 1370.85 Jan 31st Florida Warm Up 2025
79 Florida Win 11-10 1472.88 Feb 1st Florida Warm Up 2025
134 South Florida Win 13-7 1669.94 Feb 1st Florida Warm Up 2025
1 Massachusetts** Loss 4-13 1659.1 Ignored Feb 1st Florida Warm Up 2025
119 Central Florida Win 13-7 1738.92 Feb 2nd Florida Warm Up 2025
56 Cornell Win 13-7 2081.47 Feb 2nd Florida Warm Up 2025
9 California-Santa Cruz Loss 7-13 1463.65 Mar 8th Stanford Invite 2025 Mens
85 Southern California Win 12-11 1442.15 Mar 8th Stanford Invite 2025 Mens
55 UCLA Loss 9-10 1410.24 Mar 8th Stanford Invite 2025 Mens
53 Whitman Win 13-9 1966.85 Mar 8th Stanford Invite 2025 Mens
12 British Columbia Loss 8-13 1475.81 Mar 9th Stanford Invite 2025 Mens
14 California Loss 9-10 1844.23 Mar 9th Stanford Invite 2025 Mens
42 Stanford Loss 7-9 1338.03 Mar 9th Stanford Invite 2025 Mens
247 George Washington** Win 15-4 1225.89 Ignored Mar 22nd Atlantic Coast Open 2025
113 Lehigh Win 14-8 1740.28 Mar 22nd Atlantic Coast Open 2025
138 RIT Win 13-9 1511.48 Mar 22nd Atlantic Coast Open 2025
255 Wake Forest** Win 15-1 1186.48 Ignored Mar 22nd Atlantic Coast Open 2025
96 Appalachian State Win 14-9 1748.29 Mar 23rd Atlantic Coast Open 2025
97 Duke Win 15-11 1654.59 Mar 23rd Atlantic Coast Open 2025
81 North Carolina-Charlotte Win 13-12 1447.26 Mar 23rd Atlantic Coast Open 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)