#49 Notre Dame (18-10)

avg: 1643.26  •  sd: 58.11  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
107 Tennessee Win 15-8 1906.53 Feb 11th Queen City Tune Up1
41 William & Mary Loss 9-15 1203.4 Feb 11th Queen City Tune Up1
51 Virginia Loss 11-12 1510.46 Feb 11th Queen City Tune Up1
30 Ohio State Loss 7-15 1235.97 Feb 11th Queen City Tune Up1
52 Appalachian State Loss 10-11 1509.05 Feb 12th Queen City Tune Up1
38 Purdue Win 12-6 2352.41 Feb 12th Queen City Tune Up1
69 Maryland Loss 12-13 1414.96 Feb 25th Easterns Qualifier 2023
104 Florida State Win 13-10 1673.15 Feb 25th Easterns Qualifier 2023
33 Duke Loss 9-13 1372.1 Feb 25th Easterns Qualifier 2023
25 North Carolina-Wilmington Win 12-11 2009.26 Feb 25th Easterns Qualifier 2023
85 Alabama Win 15-11 1828.16 Feb 26th Easterns Qualifier 2023
50 Case Western Reserve Loss 11-15 1258.85 Feb 26th Easterns Qualifier 2023
45 Georgetown Loss 7-11 1229.8 Feb 26th Easterns Qualifier 2023
141 LSU Win 10-9 1308.87 Mar 11th Tally Classic XVII
104 Florida State Win 10-6 1841.17 Mar 11th Tally Classic XVII
268 Georgia Southern** Win 13-5 1251.41 Ignored Mar 11th Tally Classic XVII
201 South Florida Win 11-5 1537.69 Mar 11th Tally Classic XVII
90 Chicago Win 13-12 1558.78 Mar 12th Tally Classic XVII
148 Minnesota-Duluth Win 15-14 1286.01 Mar 12th Tally Classic XVII
91 Tulane Win 13-10 1758.33 Mar 12th Tally Classic XVII
59 Cincinnati Win 7-5 1906.85 Apr 1st Huck Finn1
61 Emory Win 5-3 1995.55 Apr 1st Huck Finn1
108 Vanderbilt Win 11-6 1874.31 Apr 1st Huck Finn1
22 Washington University Loss 7-10 1515.66 Apr 1st Huck Finn1
59 Cincinnati Win 11-7 2045.6 Apr 2nd Huck Finn1
118 Marquette Win 14-8 1836.81 Apr 2nd Huck Finn1
48 Iowa State Loss 7-9 1367.01 Apr 2nd Huck Finn1
64 St. Olaf Win 13-10 1896.14 Apr 2nd Huck Finn1
**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)