#88 Virginia Tech (8-10)

avg: 1297.26  •  sd: 57.57  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
39 Virginia Loss 0-13 1138.89 Jan 27th Winta Binta Vinta 2024
61 Penn State Loss 4-9 903.49 Jan 27th Winta Binta Vinta 2024
57 William & Mary Loss 2-9 944.4 Jan 27th Winta Binta Vinta 2024
123 Liberty Win 7-5 1306.66 Jan 28th Winta Binta Vinta 2024
181 Virginia-B** Win 12-1 1061.64 Ignored Jan 28th Winta Binta Vinta 2024
21 Ohio State** Loss 2-13 1482.16 Ignored Jan 28th Winta Binta Vinta 2024
31 Brown Loss 8-15 1309.38 Feb 24th Commonwealth Cup Weekend 2 2024
35 Ohio Loss 10-12 1560.01 Feb 24th Commonwealth Cup Weekend 2 2024
42 Purdue Loss 4-11 1100.79 Feb 24th Commonwealth Cup Weekend 2 2024
163 Temple Win 9-7 913.91 Feb 24th Commonwealth Cup Weekend 2 2024
134 MIT Win 8-6 1212.74 Feb 25th Commonwealth Cup Weekend 2 2024
112 Maryland Win 10-5 1641.24 Feb 25th Commonwealth Cup Weekend 2 2024
43 Alabama-Huntsville Loss 2-13 1100.69 Mar 23rd Needle in a Ho Stack 2024
102 East Carolina Win 6-3 1719.22 Mar 23rd Needle in a Ho Stack 2024
162 South Carolina-B** Win 11-2 1240.77 Ignored Mar 24th Needle in a Ho Stack 2024
28 St Olaf Loss 5-8 1467.74 Mar 24th Needle in a Ho Stack 2024
137 Case Western Reserve Win 10-1 1499.44 Mar 24th Needle in a Ho Stack 2024
59 Georgetown Loss 8-9 1394.65 Mar 24th Needle in a Ho Stack 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)