#141 Boston College (6-5)

avg: 1166.16  •  sd: 86.6  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
129 Claremont Loss 11-12 1070.86 Feb 10th Stanford Open 2018
121 Puget Sound Win 10-8 1519.58 Feb 10th Stanford Open 2018
26 Texas-Dallas Loss 8-9 1604.02 Feb 10th Stanford Open 2018
165 Humboldt State Loss 10-12 828.56 Feb 11th Stanford Open 2018
186 Cal Poly-Pomona Win 12-11 1108.1 Feb 11th Stanford Open 2018
146 Nevada-Reno Win 11-9 1398.51 Feb 11th Stanford Open 2018
65 California-Santa Barbara Loss 8-11 1096.76 Feb 11th Stanford Open 2018
119 Bates Loss 6-13 664.63 Mar 31st New England Open 2018
- Massachusetts-C Win 13-9 1124.68 Mar 31st New England Open 2018
- Worcester Polytech Win 12-9 1088.08 Mar 31st New England Open 2018
198 Wesleyan Win 10-0 1548.52 Mar 31st New England Open 2018
**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)