#52 Virginia Tech (10-10)

avg: 1475.52  •  sd: 51.87  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
56 Emory Loss 10-12 1209.45 Feb 2nd Florida Warm Up 2024
97 Florida State Loss 11-12 1122.77 Feb 2nd Florida Warm Up 2024
8 Vermont Loss 7-13 1481.07 Feb 2nd Florida Warm Up 2024
82 Central Florida Win 15-13 1551.45 Feb 3rd Florida Warm Up 2024
7 Pittsburgh Loss 7-12 1572.63 Feb 3rd Florida Warm Up 2024
21 Tufts Loss 4-13 1228.7 Feb 3rd Florida Warm Up 2024
50 Alabama Win 13-10 1829.71 Feb 24th Easterns Qualifier 2024
16 Penn State Loss 5-13 1321.23 Feb 24th Easterns Qualifier 2024
76 Purdue Win 12-11 1482.22 Feb 24th Easterns Qualifier 2024
58 Maryland Win 10-9 1567.96 Feb 24th Easterns Qualifier 2024
38 Duke Loss 11-12 1465.76 Feb 25th Easterns Qualifier 2024
56 Emory Loss 9-10 1322.58 Feb 25th Easterns Qualifier 2024
106 Notre Dame Win 13-9 1628.88 Feb 25th Easterns Qualifier 2024
61 William & Mary Loss 11-12 1307.01 Feb 25th Easterns Qualifier 2024
90 SUNY-Buffalo Win 12-11 1400.01 Mar 30th Atlantic Coast Open 2024
90 SUNY-Buffalo Win 13-7 1832.55 Mar 30th Atlantic Coast Open 2024
60 Temple Win 13-12 1560.02 Mar 30th Atlantic Coast Open 2024
126 Lehigh Loss 14-15 1020.39 Mar 30th Atlantic Coast Open 2024
62 Massachusetts -B Win 15-9 1947.26 Mar 31st Atlantic Coast Open 2024
87 Tennessee-Chattanooga Win 14-13 1434.98 Mar 31st Atlantic Coast Open 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)