#22 Tufts (10-9)

avg: 1934.61  •  sd: 61.89  •  top 16/20: 46%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
69 Notre Dame** Win 13-5 1928.85 Ignored Feb 9th Queen City Tune Up 2019 Women
3 Ohio State Win 8-7 2498 Feb 9th Queen City Tune Up 2019 Women
57 Cornell Win 13-4 2060.62 Feb 9th Queen City Tune Up 2019 Women
45 Virginia Win 10-3 2145.7 Feb 9th Queen City Tune Up 2019 Women
20 North Carolina-Wilmington Win 11-10 2085.18 Feb 10th Queen City Tune Up 2019 Women
11 Pittsburgh Loss 9-15 1567.78 Feb 10th Queen City Tune Up 2019 Women
5 Carleton College-Syzygy Loss 3-15 1665.5 Feb 10th Queen City Tune Up 2019 Women
1 North Carolina Loss 8-12 2088.91 Feb 23rd Commonwealth Cup 2019
28 North Carolina State Loss 10-11 1648.66 Feb 23rd Commonwealth Cup 2019
58 Penn State Win 13-4 2051.04 Feb 23rd Commonwealth Cup 2019
62 Oberlin Win 13-6 2017.04 Feb 24th Commonwealth Cup 2019
20 North Carolina-Wilmington Loss 7-13 1402.64 Feb 24th Commonwealth Cup 2019
9 Texas Loss 7-13 1586.47 Mar 23rd Womens College Centex 2019
14 Colorado Loss 10-11 1921.85 Mar 23rd Womens College Centex 2019
12 Minnesota Loss 7-8 1944.71 Mar 23rd Womens College Centex 2019
12 Minnesota Loss 11-14 1756.37 Mar 24th Womens College Centex 2019
15 Wisconsin Win 12-11 2146.96 Mar 24th Womens College Centex 2019
37 Washington University Win 14-11 1983.89 Mar 24th Womens College Centex 2019
30 Utah Win 14-7 2341.75 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)