#52 Notre Dame (12-7)

avg: 1626.67  •  sd: 63.53  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
25 South Carolina Win 11-10 1911.69 Jan 26th Carolina Kickoff 2019
69 Emory Win 13-9 1927.03 Jan 26th Carolina Kickoff 2019
119 Clemson Win 9-6 1702.12 Jan 26th Carolina Kickoff 2019
55 Florida State Loss 11-15 1230.51 Jan 27th Carolina Kickoff 2019
26 North Carolina-Wilmington Loss 10-15 1327.37 Jan 27th Carolina Kickoff 2019
81 Georgia Tech Win 15-13 1661.5 Jan 27th Carolina Kickoff 2019
36 Alabama Loss 7-9 1443.8 Feb 9th Queen City Tune Up 2019 Men
57 Carnegie Mellon Loss 9-10 1462.38 Feb 9th Queen City Tune Up 2019 Men
11 North Carolina State Loss 8-13 1531.41 Feb 9th Queen City Tune Up 2019 Men
108 North Carolina-Charlotte Win 13-10 1653.21 Feb 9th Queen City Tune Up 2019 Men
94 Appalachian State Loss 14-15 1247.43 Feb 10th Queen City Tune Up 2019 Men
119 Clemson Win 14-8 1819.58 Feb 10th Queen City Tune Up 2019 Men
57 Carnegie Mellon Win 14-10 1986.08 Feb 10th Queen City Tune Up 2019 Men
72 Alabama-Huntsville Loss 10-13 1155.85 Mar 16th Tally Classic XIV
15 Central Florida Win 14-12 2211.27 Mar 16th Tally Classic XIV
119 Clemson Win 15-14 1408.55 Mar 16th Tally Classic XIV
165 Georgia Southern Win 13-6 1691.91 Mar 16th Tally Classic XIV
143 Minnesota-Duluth Win 15-10 1652.67 Mar 17th Tally Classic XIV
68 Cincinnati Win 15-12 1815.87 Mar 17th Tally Classic XIV
**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)