#230 West Virginia (12-4)

avg: 685.38  •  sd: 77.01  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
11 Davenport** Loss 0-6 1380.95 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
105 Liberty Loss 5-10 659.01 Feb 15th 2025 Commonwealth Cup Weekend 1
199 North Carolina State-B Win 7-4 1313.23 Feb 16th 2025 Commonwealth Cup Weekend 1
344 South Carolina-B Win 8-2 729.6 Feb 16th 2025 Commonwealth Cup Weekend 1
200 Akron Loss 3-9 213.12 Mar 1st Huckin in the Hills XI
375 Ohio-B** Win 11-2 486.01 Ignored Mar 1st Huckin in the Hills XI
378 Maryland-B** Win 13-1 448.78 Ignored Mar 1st Huckin in the Hills XI
259 Drexel Win 9-8 703.78 Mar 2nd Huckin in the Hills XI
210 Kent State Loss 8-11 392.73 Mar 2nd Huckin in the Hills XI
357 George Washington-B** Win 13-5 628.03 Ignored Mar 29th Fishbowl 2025
351 James Madison-B Win 13-2 705.59 Mar 29th Fishbowl 2025
378 Maryland-B Win 13-6 448.78 Mar 29th Fishbowl 2025
379 William & Mary-B** Win 12-5 445.4 Ignored Mar 29th Fishbowl 2025
357 George Washington-B** Win 15-5 628.03 Ignored Mar 30th Fishbowl 2025
291 Virginia-B Win 13-5 1012.37 Mar 30th Fishbowl 2025
291 Virginia-B Win 12-9 757.74 Mar 30th Fishbowl 2025
**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)