#106 Notre Dame (8-13)

avg: 1210.32  •  sd: 62.62  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
84 Appalachian State Win 13-10 1654.94 Feb 10th Queen City Tune Up 2024
68 James Madison Loss 13-14 1251.89 Feb 10th Queen City Tune Up 2024
13 North Carolina State** Loss 5-15 1346.6 Ignored Feb 10th Queen City Tune Up 2024
34 Ohio State Loss 3-15 1041.87 Feb 10th Queen City Tune Up 2024
70 Case Western Reserve Loss 12-15 1066.22 Feb 11th Queen City Tune Up 2024
91 Indiana Loss 8-13 774.65 Feb 11th Queen City Tune Up 2024
27 Georgia Tech Win 11-10 1865.14 Feb 24th Easterns Qualifier 2024
36 North Carolina-Charlotte Loss 5-13 1018.26 Feb 24th Easterns Qualifier 2024
126 Lehigh Win 13-8 1641.55 Feb 24th Easterns Qualifier 2024
66 Virginia Loss 11-13 1165.33 Feb 24th Easterns Qualifier 2024
74 Cincinnati Loss 12-15 1060.7 Feb 25th Easterns Qualifier 2024
154 Harvard Win 12-9 1368.55 Feb 25th Easterns Qualifier 2024
60 Temple Loss 11-12 1310.02 Feb 25th Easterns Qualifier 2024
52 Virginia Tech Loss 9-13 1056.96 Feb 25th Easterns Qualifier 2024
75 Ave Maria Win 9-8 1484.58 Mar 16th Tally Classic XVIII
200 Spring Hill Win 11-9 1077.44 Mar 16th Tally Classic XVIII
185 South Florida Win 11-6 1413 Mar 16th Tally Classic XVIII
57 Auburn Loss 8-15 882.38 Mar 17th Tally Classic XVIII
82 Central Florida Loss 7-13 779.74 Mar 17th Tally Classic XVIII
82 Central Florida Loss 11-15 956.11 Mar 17th Tally Classic XVIII
105 Mississippi State Win 15-13 1424.96 Mar 17th Tally Classic XVIII
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)