#7 Tufts (14-3)

avg: 2508.79  •  sd: 44.71  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
42 Wisconsin Win 10-6 2499.86 Feb 3rd Queen City Tune Up 2018 College Women
15 North Carolina State Loss 10-11 2228.08 Feb 3rd Queen City Tune Up 2018 College Women
40 Kennesaw State Win 11-3 2617.59 Feb 3rd Queen City Tune Up 2018 College Women
10 Pittsburgh Win 10-8 2744.42 Feb 3rd Queen City Tune Up 2018 College Women
48 Georgia Win 15-6 2555.58 Feb 24th Commonwealth Cup 2018
66 Virginia** Win 15-2 2371.7 Ignored Feb 24th Commonwealth Cup 2018
46 North Carolina-Wilmington Win 14-3 2578.21 Feb 24th Commonwealth Cup 2018
62 Central Florida** Win 13-1 2398.88 Ignored Feb 25th Commonwealth Cup 2018
49 Duke Win 13-6 2551.48 Feb 25th Commonwealth Cup 2018
15 North Carolina State Win 11-10 2478.08 Feb 25th Commonwealth Cup 2018
3 North Carolina Loss 10-12 2492.38 Feb 25th Commonwealth Cup 2018
42 Wisconsin Win 12-6 2583.01 Mar 24th Womens Centex 2018
44 Colorado State Win 13-6 2581.6 Mar 24th Womens Centex 2018
66 Virginia** Win 13-2 2371.7 Ignored Mar 24th Womens Centex 2018
28 Washington University Win 11-10 2238.89 Mar 25th Womens Centex 2018
11 Texas Loss 12-13 2346.75 Mar 25th Womens Centex 2018
32 Florida Win 15-3 2680.24 Mar 25th Womens Centex 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)