#50 Notre Dame (11-9)

avg: 1539.28  •  sd: 57.02  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
61 James Madison Loss 8-9 1347.52 Feb 3rd Queen City Tune Up 2018 College Open
116 Appalachian State Loss 9-10 1149.46 Feb 3rd Queen City Tune Up 2018 College Open
41 Northeastern Loss 9-10 1478.36 Feb 3rd Queen City Tune Up 2018 College Open
42 Connecticut Loss 8-11 1229.95 Feb 3rd Queen City Tune Up 2018 College Open
36 Michigan Loss 5-11 1038.31 Feb 3rd Queen City Tune Up 2018 College Open
150 North Carolina-Asheville Win 9-3 1731.08 Feb 4th Queen City Tune Up 2018 College Open
231 Alabama-Birmingham** Win 13-4 1421.08 Ignored Mar 10th Tally Classic XIII
98 Clemson Win 14-12 1559 Mar 10th Tally Classic XIII
81 Florida State Loss 11-13 1179.88 Mar 10th Tally Classic XIII
46 South Carolina Loss 11-13 1350.52 Mar 10th Tally Classic XIII
23 Georgia Tech Loss 10-13 1415.82 Mar 10th Tally Classic XIII
97 Alabama Win 11-8 1713.54 Mar 11th Tally Classic XIII
37 Central Florida Loss 14-15 1509.75 Mar 11th Tally Classic XIII
105 Wisconsin-Milwaukee Win 13-2 1917.43 Mar 24th CWRUL Memorial 2018
203 Rochester** Win 13-4 1530.73 Ignored Mar 24th CWRUL Memorial 2018
133 Case Western Reserve Win 13-8 1671.93 Mar 24th CWRUL Memorial 2018
277 Eastern Michigan** Win 15-6 1280.74 Ignored Mar 24th CWRUL Memorial 2018
180 Pittsburgh-B Win 12-5 1611.89 Mar 25th CWRUL Memorial 2018
93 Cincinnati Win 12-4 1963.42 Mar 25th CWRUL Memorial 2018
105 Wisconsin-Milwaukee Win 15-9 1832.91 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)