#11 Pittsburgh (14-6)

avg: 2083.27  •  sd: 45.63  •  top 16/20: 99.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
25 Clemson Win 8-3 2472.28 Feb 9th Queen City Tune Up 2019 Women
38 Florida Win 9-6 2029.68 Feb 9th Queen City Tune Up 2019 Women
58 Penn State** Win 12-2 2051.04 Ignored Feb 9th Queen City Tune Up 2019 Women
41 Harvard Win 13-3 2167.65 Feb 9th Queen City Tune Up 2019 Women
1 North Carolina Loss 10-15 2076.46 Feb 10th Queen City Tune Up 2019 Women
26 Georgia Win 15-10 2301.9 Feb 10th Queen City Tune Up 2019 Women
22 Tufts Win 15-9 2450.09 Feb 10th Queen City Tune Up 2019 Women
3 Ohio State Loss 4-9 1773 Feb 23rd Commonwealth Cup 2019
40 Michigan Win 9-2 2169.43 Feb 23rd Commonwealth Cup 2019
20 North Carolina-Wilmington Win 10-7 2349.84 Feb 23rd Commonwealth Cup 2019
52 Columbia Win 13-8 1999.43 Feb 24th Commonwealth Cup 2019
28 North Carolina State Win 12-9 2119.02 Feb 24th Commonwealth Cup 2019
8 Dartmouth Loss 6-8 1858.36 Feb 24th Commonwealth Cup 2019
13 Stanford Loss 12-13 1930.63 Mar 29th NW Challenge Tier 1 Womens
24 Washington Win 15-12 2173.08 Mar 29th NW Challenge Tier 1 Womens
16 Oregon Loss 12-15 1717.24 Mar 30th NW Challenge Tier 1 Womens
32 Brigham Young Win 12-8 2151.64 Mar 30th NW Challenge Tier 1 Womens
7 Western Washington Loss 11-15 1818.5 Mar 30th NW Challenge Tier 1 Womens
50 Whitman Win 15-6 2108.9 Mar 31st NW Challenge Tier 1 Womens
24 Washington Win 13-10 2200.73 Mar 31st NW Challenge Tier 1 Womens
**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)