#75 Virginia Tech (5-7)

avg: 990.56  •  sd: 70.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
34 Virginia Loss 0-13 926.64 Jan 27th Winta Binta Vinta 2024
55 Penn State Loss 4-9 694.64 Jan 27th Winta Binta Vinta 2024
50 William & Mary Loss 2-9 725.95 Jan 27th Winta Binta Vinta 2024
103 Liberty Win 7-5 1081.94 Jan 28th Winta Binta Vinta 2024
130 Virginia-B** Win 12-1 865.8 Ignored Jan 28th Winta Binta Vinta 2024
18 Ohio State** Loss 2-13 1245.53 Ignored Jan 28th Winta Binta Vinta 2024
119 Temple Win 9-7 767 Feb 24th Commonwealth Cup Weekend 2 2024
38 Brown Loss 8-15 934.27 Feb 24th Commonwealth Cup Weekend 2 2024
25 Ohio Loss 10-12 1440.34 Feb 24th Commonwealth Cup Weekend 2 2024
46 Purdue Loss 4-11 766.83 Feb 24th Commonwealth Cup Weekend 2 2024
107 MIT Win 8-6 1013.78 Feb 25th Commonwealth Cup Weekend 2 2024
96 Maryland Win 10-5 1393.67 Feb 25th Commonwealth Cup Weekend 2 2024
**Blowout Eligible

FAQ

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
  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
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)