#69 Notre Dame (11-10)

avg: 1328.85  •  sd: 72.52  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
45 Virginia Loss 7-10 1156.03 Feb 9th Queen City Tune Up 2019 Women
3 Ohio State** Loss 2-12 1773 Ignored Feb 9th Queen City Tune Up 2019 Women
22 Tufts** Loss 5-13 1334.61 Ignored Feb 9th Queen City Tune Up 2019 Women
57 Cornell Win 10-8 1723.28 Feb 9th Queen City Tune Up 2019 Women
43 Georgia Tech Loss 7-15 955.59 Feb 10th Queen City Tune Up 2019 Women
28 North Carolina State Loss 4-15 1173.66 Feb 10th Queen City Tune Up 2019 Women
91 Case Western Reserve Loss 12-15 902.18 Feb 10th Queen City Tune Up 2019 Women
38 Florida Win 10-9 1736.11 Mar 16th Tally Classic XIV
41 Harvard Loss 12-13 1442.65 Mar 16th Tally Classic XIV
115 South Florida Win 13-5 1650.61 Mar 16th Tally Classic XIV
93 Kennesaw State Win 13-9 1606.61 Mar 16th Tally Classic XIV
51 Florida State Win 12-11 1633.35 Mar 17th Tally Classic XIV
41 Harvard Loss 9-11 1318.45 Mar 17th Tally Classic XIV
38 Florida Win 11-8 1976.72 Mar 17th Tally Classic XIV
141 Iowa Win 9-8 1031.31 Mar 30th Old Capitol Open 2019
140 Cincinnati Loss 7-8 787.84 Mar 30th Old Capitol Open 2019
175 Kansas Win 9-7 996.58 Mar 30th Old Capitol Open 2019
219 Cornell College** Win 5-0 1005.19 Ignored Mar 30th Old Capitol Open 2019
172 Northern Iowa Win 15-7 1324.75 Mar 31st Old Capitol Open 2019
162 Nebraska Win 13-5 1396.75 Mar 31st Old Capitol Open 2019
89 Iowa State Loss 10-12 984.98 Mar 31st Old Capitol Open 2019
**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)