#75 Boston University (7-7)

avg: 1221.28  •  sd: 59.78  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 Southern California Win 13-1 1686.29 Mar 18th Womens Centex1
91 Colorado College Win 13-11 1284.1 Mar 18th Womens Centex1
16 Middlebury Loss 7-13 1278.48 Mar 18th Womens Centex1
47 Florida Loss 8-13 971.97 Mar 19th Womens Centex1
23 Texas-Dallas Loss 6-15 1139.68 Mar 19th Womens Centex1
48 Texas Loss 7-15 860.19 Mar 19th Womens Centex1
44 Pennsylvania Loss 5-8 1030.95 Mar 19th Womens Centex1
32 SUNY-Binghamton Loss 7-9 1372.29 Mar 25th New England Open1
143 Connecticut Win 13-1 1264.75 Mar 25th New England Open1
177 Bowdoin** Win 9-0 976.84 Ignored Mar 25th New England Open1
120 Brandeis Win 9-8 965.29 Mar 25th New England Open1
118 Rhode Island Win 12-5 1459.74 Mar 26th New England Open1
22 Wellesley Loss 3-9 1147.61 Mar 26th New England Open1
67 Mount Holyoke Win 7-6 1392.65 Mar 26th New England Open1
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)