#8 West Chester (8-2)

avg: 2505.97  •  sd: 85.77  •  top 16/20: 99.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
13 Ohio State Loss 4-11 1804.92 Jan 13th Florida Winter Classic 2018
32 Florida Win 15-4 2680.24 Jan 13th Florida Winter Classic 2018
21 Michigan Win 13-6 2838.43 Jan 13th Florida Winter Classic 2018
13 Ohio State Win 13-9 2823.49 Jan 14th Florida Winter Classic 2018
57 Kansas** Win 15-4 2436.54 Ignored Jan 14th Florida Winter Classic 2018
48 Georgia Win 14-10 2354.28 Jan 14th Florida Winter Classic 2018
34 Northeastern Win 11-7 2521.79 Feb 3rd Queen City Tune Up 2018 College Women
31 Penn State Win 10-5 2660.09 Feb 3rd Queen City Tune Up 2018 College Women
32 Florida Win 12-7 2600.75 Feb 3rd Queen City Tune Up 2018 College Women
3 North Carolina Loss 8-13 2234.34 Feb 3rd Queen City Tune Up 2018 College Women
**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)