#84 Appalachian State (12-11)

avg: 1326.8  •  sd: 47.94  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
235 North Carolina-B Win 15-10 1141.07 Jan 27th Carolina Kickoff 2024
1 North Carolina** Loss 3-15 1688.56 Ignored Jan 27th Carolina Kickoff 2024
13 North Carolina State Loss 10-15 1493 Jan 28th Carolina Kickoff 2024
28 North Carolina-Wilmington Loss 8-15 1169.7 Jan 28th Carolina Kickoff 2024
38 Duke Loss 11-14 1277.42 Jan 28th Carolina Kickoff 2024
34 Ohio State Loss 8-15 1077.06 Feb 10th Queen City Tune Up 2024
13 North Carolina State Loss 7-15 1346.6 Feb 10th Queen City Tune Up 2024
68 James Madison Loss 10-13 1048.75 Feb 10th Queen City Tune Up 2024
106 Notre Dame Loss 10-13 882.17 Feb 10th Queen City Tune Up 2024
61 William & Mary Loss 12-13 1307.01 Feb 11th Queen City Tune Up 2024
66 Virginia Loss 8-10 1131.5 Feb 11th Queen City Tune Up 2024
165 RIT Win 13-10 1293.43 Mar 2nd Oak Creek Challenge 2024
206 George Washington Win 9-6 1221.87 Mar 2nd Oak Creek Challenge 2024
130 Towson Win 13-12 1241.83 Mar 2nd Oak Creek Challenge 2024
169 Rutgers Win 11-10 1076.64 Mar 3rd Oak Creek Challenge 2024
101 Cornell Win 13-9 1643.14 Mar 3rd Oak Creek Challenge 2024
90 SUNY-Buffalo Win 13-9 1693.58 Mar 3rd Oak Creek Challenge 2024
73 Richmond Win 14-12 1585.21 Mar 30th Atlantic Coast Open 2024
87 Tennessee-Chattanooga Win 13-12 1434.98 Mar 30th Atlantic Coast Open 2024
144 Pittsburgh-B Win 15-11 1443.18 Mar 30th Atlantic Coast Open 2024
61 William & Mary Loss 9-12 1086.64 Mar 30th Atlantic Coast Open 2024
126 Lehigh Win 15-12 1445.89 Mar 31st Atlantic Coast Open 2024
85 Carnegie Mellon Win 15-11 1699.49 Mar 31st Atlantic Coast Open 2024
**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)