#66 Virginia (8-13)

avg: 1394.17  •  sd: 58.48  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
91 Indiana Loss 10-11 1145.81 Feb 10th Queen City Tune Up 2024
25 McGill Loss 7-11 1307.36 Feb 10th Queen City Tune Up 2024
1 North Carolina Loss 7-15 1688.56 Feb 10th Queen City Tune Up 2024
28 North Carolina-Wilmington Loss 10-13 1406.36 Feb 10th Queen City Tune Up 2024
84 Appalachian State Win 10-8 1589.47 Feb 11th Queen City Tune Up 2024
48 Missouri Loss 9-15 999.29 Feb 11th Queen City Tune Up 2024
27 Georgia Tech Loss 7-13 1182.61 Feb 24th Easterns Qualifier 2024
154 Harvard Win 13-5 1623.19 Feb 24th Easterns Qualifier 2024
106 Notre Dame Win 13-11 1439.16 Feb 24th Easterns Qualifier 2024
126 Lehigh Win 11-8 1511 Feb 24th Easterns Qualifier 2024
57 Auburn Loss 12-13 1322.19 Feb 25th Easterns Qualifier 2024
38 Duke Win 14-11 1904.09 Feb 25th Easterns Qualifier 2024
36 North Carolina-Charlotte Win 12-9 1963.62 Feb 25th Easterns Qualifier 2024
34 Ohio State Loss 11-14 1328.53 Feb 25th Easterns Qualifier 2024
50 Alabama Loss 7-10 1111.9 Mar 30th Huck Finn 2024
76 Purdue Win 11-10 1482.22 Mar 30th Huck Finn 2024
49 St Olaf Loss 7-10 1113.5 Mar 30th Huck Finn 2024
19 Washington University Loss 8-11 1499.57 Mar 30th Huck Finn 2024
50 Alabama Loss 9-12 1156.2 Mar 31st Huck Finn 2024
67 Chicago Win 9-7 1666.36 Mar 31st Huck Finn 2024
65 Stanford Loss 7-10 1015.27 Mar 31st Huck Finn 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)