#203 West Virginia (10-10)

avg: 819.37  •  sd: 59.68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
104 Dayton Loss 5-10 640.71 Feb 3rd Huckin in the Hills X
51 Franciscan** Loss 5-13 896.28 Ignored Feb 3rd Huckin in the Hills X
304 Kent State Win 13-5 931.64 Feb 3rd Huckin in the Hills X
204 Ohio Win 8-7 934.59 Feb 3rd Huckin in the Hills X
104 Dayton Loss 4-15 614.61 Feb 4th Huckin in the Hills X
304 Kent State Win 15-8 896.45 Feb 4th Huckin in the Hills X
130 Towson Loss 9-12 771.46 Feb 4th Huckin in the Hills X
144 Pittsburgh-B Loss 6-13 462.02 Feb 17th Commonwealth Cup Weekend 1 2024
301 Virginia-B Win 13-0 949.24 Feb 17th Commonwealth Cup Weekend 1 2024
58 Maryland** Loss 4-13 842.96 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
290 Michigan-B Win 9-4 1007.14 Feb 18th Commonwealth Cup Weekend 1 2024
216 North Carolina State-B Loss 5-9 231.26 Feb 18th Commonwealth Cup Weekend 1 2024
235 North Carolina-B Loss 11-12 562.46 Feb 18th Commonwealth Cup Weekend 1 2024
166 Villanova Win 9-8 1083.54 Mar 23rd Garden State 2024
152 West Chester Loss 7-8 901.57 Mar 23rd Garden State 2024
198 Delaware Win 7-6 959.68 Mar 24th Garden State 2024
281 Edinboro Win 10-4 1050.84 Mar 24th Garden State 2024
197 Haverford Loss 6-13 236.18 Mar 24th Garden State 2024
199 Messiah Win 11-8 1197.85 Mar 24th Garden State 2024
199 Messiah Win 9-7 1111.58 Mar 24th Garden State 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)