#67 Virginia Tech (11-10)

avg: 1553.29  •  sd: 60.59  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
2 Brigham Young** Loss 4-13 1718.3 Ignored Feb 3rd Florida Warm Up 2023
88 Central Florida Loss 10-12 1196.1 Feb 3rd Florida Warm Up 2023
34 Michigan Loss 9-13 1370.26 Feb 3rd Florida Warm Up 2023
12 Minnesota Loss 5-13 1470.91 Feb 4th Florida Warm Up 2023
39 Florida Loss 9-13 1322.85 Feb 4th Florida Warm Up 2023
26 Georgia Tech Loss 11-13 1639.49 Feb 4th Florida Warm Up 2023
147 Connecticut Win 11-10 1287.48 Feb 5th Florida Warm Up 2023
201 South Florida Win 13-6 1537.69 Feb 5th Florida Warm Up 2023
150 George Washington Win 13-5 1748.25 Mar 4th Oak Creek Challenge 2023
248 Drexel** Win 13-3 1344.55 Ignored Mar 4th Oak Creek Challenge 2023
157 Yale Loss 7-8 1000.16 Mar 4th Oak Creek Challenge 2023
70 Lehigh Loss 8-13 1030.57 Mar 5th Oak Creek Challenge 2023
124 Towson Win 13-2 1869.13 Mar 5th Oak Creek Challenge 2023
157 Yale Win 13-2 1725.16 Mar 5th Oak Creek Challenge 2023
162 American Win 13-4 1704.41 Apr 1st Atlantic Coast Open 2023
190 MIT Win 13-7 1549.1 Apr 1st Atlantic Coast Open 2023
77 Temple Win 9-8 1605.32 Apr 1st Atlantic Coast Open 2023
33 Duke Loss 8-13 1294.5 Apr 1st Atlantic Coast Open 2023
45 Georgetown Win 15-11 2077.86 Apr 2nd Atlantic Coast Open 2023
36 Penn State Win 13-12 1902.62 Apr 2nd Atlantic Coast Open 2023
33 Duke Loss 12-13 1665.66 Apr 2nd Atlantic Coast Open 2023
**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)