#10 Pittsburgh (8-4)

avg: 2481.75  •  sd: 79.34  •  top 16/20: 99.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
42 Wisconsin Win 10-5 2577.6 Feb 3rd Queen City Tune Up 2018 College Women
7 Tufts Loss 8-10 2246.13 Feb 3rd Queen City Tune Up 2018 College Women
15 North Carolina State Win 11-3 2953.08 Feb 3rd Queen City Tune Up 2018 College Women
40 Kennesaw State Win 13-2 2617.59 Feb 3rd Queen City Tune Up 2018 College Women
14 Whitman Win 13-10 2714.28 Mar 3rd Stanford Invite 2018
5 Oregon Win 13-11 2839.36 Mar 3rd Stanford Invite 2018
9 Colorado Win 13-12 2624.69 Mar 3rd Stanford Invite 2018
13 Ohio State Loss 9-11 2155.72 Mar 4th Stanford Invite 2018
4 Stanford Loss 3-8 2095.52 Mar 4th Stanford Invite 2018
11 Texas Loss 6-9 2053.18 Mar 4th Stanford Invite 2018
30 Williams Win 13-10 2429.32 Mar 24th I 85 Rodeo 2018
88 Georgetown** Win 15-5 2178.62 Ignored Mar 24th I 85 Rodeo 2018
**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)