#72 Auburn (3-19)

avg: 1497.8  •  sd: 55.01  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
2 Brigham Young Loss 7-13 1760.77 Feb 3rd Florida Warm Up 2023
39 Florida Win 13-10 2069.56 Feb 3rd Florida Warm Up 2023
34 Michigan Loss 7-13 1231.29 Feb 3rd Florida Warm Up 2023
79 Texas A&M Win 11-7 1940.58 Feb 4th Florida Warm Up 2023
12 Minnesota Loss 6-13 1470.91 Feb 4th Florida Warm Up 2023
104 Florida State Win 13-8 1841.17 Feb 4th Florida Warm Up 2023
4 Texas** Loss 5-13 1614.75 Ignored Feb 5th Florida Warm Up 2023
21 Northeastern Loss 6-13 1307.17 Feb 5th Florida Warm Up 2023
11 Brown Loss 11-13 1845.88 Mar 4th Smoky Mountain Invite
1 North Carolina Loss 6-13 1792.65 Mar 4th Smoky Mountain Invite
14 Carleton College Loss 5-13 1449.87 Mar 4th Smoky Mountain Invite
5 Vermont** Loss 2-13 1610.06 Ignored Mar 4th Smoky Mountain Invite
20 North Carolina State Loss 9-12 1600.03 Mar 5th Smoky Mountain Invite
30 Ohio State Loss 7-15 1235.97 Mar 5th Smoky Mountain Invite
107 Tennessee Loss 13-14 1216.72 Mar 5th Smoky Mountain Invite
3 Massachusetts** Loss 5-13 1711.41 Ignored Apr 1st Easterns 2023
18 California Loss 5-10 1387.67 Apr 1st Easterns 2023
8 Pittsburgh Loss 6-13 1555.18 Apr 1st Easterns 2023
20 North Carolina State Loss 7-12 1424.89 Apr 1st Easterns 2023
19 Georgia Loss 3-15 1350.85 Apr 2nd Easterns 2023
34 Michigan Loss 4-15 1188.82 Apr 2nd Easterns 2023
30 Ohio State Loss 3-15 1235.97 Apr 2nd Easterns 2023
**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)