#206 West Chester (5-6)

avg: 966.25  •  sd: 74.25  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
171 RIT Loss 10-11 956.65 Feb 23rd Oak Creek Challenge 2019
188 East Carolina Win 9-7 1309.7 Feb 23rd Oak Creek Challenge 2019
278 Christopher Newport Win 11-3 1364.63 Feb 23rd Oak Creek Challenge 2019
166 Virginia Commonwealth Win 10-9 1216.83 Feb 23rd Oak Creek Challenge 2019
83 Rutgers Loss 6-13 832.97 Feb 24th Oak Creek Challenge 2019
84 Brandeis Loss 8-12 990.73 Feb 24th Oak Creek Challenge 2019
157 Drexel Loss 6-11 582.71 Feb 24th Oak Creek Challenge 2019
405 Jefferson Win 13-7 746.76 Mar 24th Hucktastic Spring 2019
147 Delaware Loss 8-9 1062.94 Mar 24th Hucktastic Spring 2019
359 Villanova-B Win 13-4 1042.64 Mar 24th Hucktastic Spring 2019
228 Swarthmore Loss 8-10 652.2 Mar 24th Hucktastic Spring 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)