#13 Pittsburgh (10-8)

avg: 1994.81  •  sd: 78.93  •  top 16/20: 93%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
6 Georgia Tech Loss 5-9 1603.25 Feb 8th Queen City Tune Up 2020 Women
105 Appalachian State** Win 13-3 1643.87 Ignored Feb 8th Queen City Tune Up 2020 Women
2 North Carolina Loss 5-11 1886.51 Feb 8th Queen City Tune Up 2020 Women
32 William & Mary Win 11-3 2240.57 Feb 8th Queen City Tune Up 2020 Women
33 North Carolina State Win 10-4 2238.31 Feb 9th Queen City Tune Up 2020 Women
21 Vermont Win 12-5 2525.53 Feb 22nd Commonwealth Cup 2020 Weekend 2
25 Georgia Loss 8-13 1279.1 Feb 22nd Commonwealth Cup 2020 Weekend 2
7 Ohio State Win 11-10 2231.77 Feb 22nd Commonwealth Cup 2020 Weekend 2
2 North Carolina Loss 9-11 2237.3 Feb 22nd Commonwealth Cup 2020 Weekend 2
2 North Carolina Loss 8-15 1921.7 Feb 23rd Commonwealth Cup 2020 Weekend 2
8 Northeastern Win 14-12 2327.6 Feb 23rd Commonwealth Cup 2020 Weekend 2
55 Columbia** Win 12-4 1990.42 Ignored Feb 23rd Commonwealth Cup 2020 Weekend 2
14 UCLA Loss 4-10 1388.29 Mar 7th Stanford Invite 2020
4 California-San Diego Loss 5-9 1708.59 Mar 7th Stanford Invite 2020
58 California-Santa Cruz Win 10-6 1862.81 Mar 7th Stanford Invite 2020
1 Carleton College Loss 6-9 2096.34 Mar 7th Stanford Invite 2020
17 British Columbia Win 11-8 2320.19 Mar 8th Stanford Invite 2020
60 Oregon** Win 13-5 1946.76 Ignored Mar 8th Stanford Invite 2020
**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)