#115 Villanova (10-2)

avg: 1296.39  •  sd: 114.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
- New Jersey Tech** Win 13-3 741.94 Ignored Mar 24th Hucktastic Spring 2019
282 Catholic Win 12-7 1262.46 Mar 24th Hucktastic Spring 2019
243 Haverford Win 10-3 1482.39 Mar 24th Hucktastic Spring 2019
147 Delaware Win 9-7 1467.28 Mar 24th Hucktastic Spring 2019
- Franklin & Marshall** Win 13-0 162.63 Ignored Mar 24th Hucktastic Spring 2019
120 James Madison Win 12-10 1520.93 Mar 30th Atlantic Coast Open 2019
171 RIT Win 13-10 1409.79 Mar 30th Atlantic Coast Open 2019
242 Rowan Win 13-12 1011.46 Mar 30th Atlantic Coast Open 2019
197 George Mason Win 13-11 1230.23 Mar 30th Atlantic Coast Open 2019
62 Duke Loss 11-14 1237.67 Mar 31st Atlantic Coast Open 2019
91 Mary Washington Loss 9-12 1037.14 Mar 31st Atlantic Coast Open 2019
137 North Carolina-B Win 14-13 1358.15 Mar 31st Atlantic Coast Open 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)