#258 Emory-B (0-17)

avg: -313.57  •  sd: 206.75  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
80 Appalachian State** Loss 0-15 614.74 Ignored Jan 25th Carolina Kickoff 2025
9 North Carolina** Loss 0-15 1518.27 Ignored Jan 25th Carolina Kickoff 2025
152 North Carolina-B** Loss 0-15 89.66 Ignored Jan 25th Carolina Kickoff 2025
43 Duke** Loss 2-15 964.4 Ignored Jan 26th Carolina Kickoff 2025
100 Emory** Loss 2-13 475.12 Ignored Jan 26th Carolina Kickoff 2025
69 North Carolina State** Loss 0-15 693.24 Ignored Jan 26th Carolina Kickoff 2025
80 Appalachian State** Loss 0-13 614.74 Ignored Mar 29th Needle in a Ho Stack 2025
156 Berry** Loss 1-13 77.7 Ignored Mar 29th Needle in a Ho Stack 2025
221 Florida Tech Loss 1-9 -382.64 Mar 29th Needle in a Ho Stack 2025
82 Tennessee** Loss 0-13 596.68 Ignored Mar 29th Needle in a Ho Stack 2025
229 Elon Loss 6-9 -253.19 Mar 30th Needle in a Ho Stack 2025
213 Georgia-B Loss 4-8 -291.84 Mar 30th Needle in a Ho Stack 2025
82 Tennessee** Loss 0-15 596.68 Ignored Apr 12th Southern Appalachian D I Womens Conferences 2025
33 Georgia Tech** Loss 0-15 1050.8 Ignored Apr 12th Southern Appalachian D I Womens Conferences 2025
157 Georgia Southern** Loss 2-15 74.02 Ignored Apr 12th Southern Appalachian D I Womens Conferences 2025
203 Tennessee-Chattanooga** Loss 1-15 -204.54 Ignored Apr 13th Southern Appalachian D I Womens Conferences 2025
213 Georgia-B Loss 5-11 -327.03 Apr 13th Southern Appalachian D I Womens Conferences 2025
**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)