#25 Notre Dame (13-4)

avg: 2139.71  •  sd: 104.01  •  top 16/20: 12.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
66 Virginia Loss 8-10 1509.04 Feb 3rd Queen City Tune Up 2018 College Women
1 Dartmouth** Loss 3-13 2297.25 Ignored Feb 3rd Queen City Tune Up 2018 College Women
93 Cornell Win 9-8 1665.61 Feb 3rd Queen City Tune Up 2018 College Women
48 Georgia Loss 5-7 1627.44 Feb 3rd Queen City Tune Up 2018 College Women
40 Kennesaw State Win 11-9 2266.79 Mar 10th Tally Classic XIII
47 Harvard Win 11-7 2444.86 Mar 10th Tally Classic XIII
243 Georgia State** Win 11-0 1105.1 Ignored Mar 10th Tally Classic XIII
32 Florida Win 15-9 2595.72 Mar 10th Tally Classic XIII
19 Vermont Loss 10-15 1809.88 Mar 11th Tally Classic XIII
41 Georgia Tech Win 14-12 2230.37 Mar 11th Tally Classic XIII
121 Dayton** Win 13-4 1963.47 Ignored Mar 24th CWRUL Memorial 2018
117 Carnegie Mellon** Win 12-2 2000.61 Ignored Mar 24th CWRUL Memorial 2018
133 Indiana** Win 13-2 1878.7 Ignored Mar 24th CWRUL Memorial 2018
204 Kentucky** Win 15-2 1440.5 Ignored Mar 24th CWRUL Memorial 2018
102 Ball State** Win 14-2 2104.81 Ignored Mar 25th CWRUL Memorial 2018
58 Oberlin Win 13-3 2429.76 Mar 25th CWRUL Memorial 2018
45 Case Western Reserve Win 8-6 2279.07 Mar 25th CWRUL Memorial 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)