#254 East Tennessee State (7-11)

avg: 854.19  •  sd: 67.14  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
128 Central Florida Loss 4-13 727.67 Mar 1st Joint Summit 2025
124 Clemson Loss 6-15 743.98 Mar 1st Joint Summit 2025
346 Clemson-B Win 9-8 588.85 Mar 1st Joint Summit 2025
188 Georgia Tech-B Loss 9-12 755.85 Mar 1st Joint Summit 2025
334 South Carolina-B Win 15-10 1014.05 Mar 2nd Joint Summit 2025
188 Georgia Tech-B Loss 10-15 647.61 Mar 2nd Joint Summit 2025
168 Charleston Loss 4-13 591.69 Mar 29th Needle in a Ho Stack 2025
371 Morehouse Win 13-4 899 Mar 29th Needle in a Ho Stack 2025
198 North Carolina-B Loss 8-13 565.48 Mar 29th Needle in a Ho Stack 2025
239 Wake Forest Win 15-11 1285.93 Mar 29th Needle in a Ho Stack 2025
82 Tennessee** Loss 0-15 925.87 Ignored Mar 30th Needle in a Ho Stack 2025
198 North Carolina-B Win 13-11 1290.48 Mar 30th Needle in a Ho Stack 2025
233 Georgia Southern Loss 13-15 704.42 Mar 30th Needle in a Ho Stack 2025
21 Georgia Tech** Loss 5-13 1376.19 Ignored Apr 12th Southern Appalachian D I Mens Conferences 2025
233 Georgia Southern Loss 7-11 451.71 Apr 12th Southern Appalachian D I Mens Conferences 2025
47 Emory** Loss 5-13 1135.48 Ignored Apr 12th Southern Appalachian D I Mens Conferences 2025
197 Kennesaw State Win 13-12 1189.17 Apr 12th Southern Appalachian D I Mens Conferences 2025
306 Tennessee Tech Win 15-9 1169.66 Apr 13th Southern Appalachian D I Mens 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)