#203 Wake Forest (6-4)

avg: 508.51  •  sd: 58.38  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
223 Elon Win 13-12 508.22 Feb 9th Ultimate Galentines Celebration 2019
71 William & Mary** Loss 2-13 726.62 Ignored Feb 9th Ultimate Galentines Celebration 2019
274 Wooster** Win 13-4 385.82 Ignored Feb 9th Ultimate Galentines Celebration 2019
259 East Carolina Win 12-4 673.16 Feb 10th Ultimate Galentines Celebration 2019
153 Virginia Tech Loss 5-11 260.33 Feb 10th Ultimate Galentines Celebration 2019
241 Cornell-B Win 9-7 507.78 Mar 30th I 85 Rodeo 2019
179 Davidson Loss 8-11 279.97 Mar 30th I 85 Rodeo 2019
164 Pittsburgh-B Loss 6-9 360.52 Mar 30th I 85 Rodeo 2019
241 Cornell-B Win 11-4 828.44 Mar 31st I 85 Rodeo 2019
263 George Washington-B Win 13-2 622.34 Mar 31st I 85 Rodeo 2019
**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)