#164 Pittsburgh-B (12-3)

avg: 779.09  •  sd: 78.5  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
239 Columbia-B Win 8-5 688.42 Feb 16th Cherry Blossom Classic 2019
184 Northeastern-B Loss 3-9 20.36 Feb 16th Cherry Blossom Classic 2019
184 Northeastern-B Win 7-5 948.51 Feb 17th Cherry Blossom Classic 2019
226 Brown-B Win 8-5 810.57 Feb 17th Cherry Blossom Classic 2019
240 Georgetown-B Win 13-1 832.56 Feb 17th Cherry Blossom Classic 2019
244 Allegheny Win 13-1 785.96 Mar 3rd Steel Spirit
- Franciscan** Win 11-1 531.19 Ignored Mar 3rd Steel Spirit
279 Carnegie Mellon-B** Win 13-0 247.08 Ignored Mar 3rd Steel Spirit
241 Cornell-B Win 9-4 828.44 Mar 30th I 85 Rodeo 2019
209 North Carolina-B Win 12-10 724.57 Mar 30th I 85 Rodeo 2019
179 Davidson Win 12-8 1086.73 Mar 30th I 85 Rodeo 2019
203 Wake Forest Win 9-6 927.07 Mar 30th I 85 Rodeo 2019
144 Tennessee Loss 9-14 422.19 Mar 31st I 85 Rodeo 2019
192 William & Mary-B Win 12-4 1177.01 Mar 31st I 85 Rodeo 2019
166 Richmond Loss 6-7 645.47 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)