#10 Northeastern (7-5)

avg: 2107.95  •  sd: 68.95  •  top 16/20: 99.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
9 Texas Loss 7-8 2019 Feb 16th Presidents Day Invite 2019
19 UCLA Win 8-5 2419.46 Feb 16th Presidents Day Invite 2019
34 Colorado College Win 8-1 2303.78 Feb 17th Presidents Day Invite 2019
12 Minnesota Loss 6-7 1944.71 Feb 17th Presidents Day Invite 2019
17 Vermont Loss 5-8 1559.59 Feb 17th Presidents Day Invite 2019
2 California-San Diego Loss 6-11 1872.37 Feb 17th Presidents Day Invite 2019
15 Wisconsin Loss 8-9 1896.96 Mar 23rd Womens College Centex 2019
19 UCLA Win 9-8 2090.86 Mar 23rd Womens College Centex 2019
42 Chicago Win 13-1 2164.5 Mar 23rd Womens College Centex 2019
9 Texas Win 11-9 2393.21 Mar 24th Womens College Centex 2019
12 Minnesota Win 14-13 2194.71 Mar 24th Womens College Centex 2019
30 Utah Win 13-8 2255.03 Mar 24th Womens College Centex 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)