#131 Georgia State (7-14)

avg: 1242.58  •  sd: 71.77  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
88 Central Florida Loss 7-13 876.69 Jan 28th T Town Throwdown1
141 LSU Loss 7-8 1058.87 Jan 28th T Town Throwdown1
61 Emory Loss 4-12 976.98 Jan 28th T Town Throwdown1
108 Vanderbilt Loss 11-12 1202.62 Jan 28th T Town Throwdown1
251 Alabama-B Win 13-5 1332.69 Jan 29th T Town Throwdown1
259 Jacksonville State Win 13-5 1296.22 Jan 29th T Town Throwdown1
368 North Florida** Win 13-0 600 Ignored Jan 29th T Town Throwdown1
37 McGill Loss 7-13 1215.84 Feb 25th Easterns Qualifier 2023
77 Temple Win 11-7 1947.21 Feb 25th Easterns Qualifier 2023
56 James Madison Loss 10-13 1271.5 Feb 25th Easterns Qualifier 2023
27 South Carolina** Loss 4-13 1248.18 Ignored Feb 25th Easterns Qualifier 2023
69 Maryland Loss 10-12 1301.84 Feb 26th Easterns Qualifier 2023
37 McGill Loss 6-13 1173.37 Feb 26th Easterns Qualifier 2023
26 Georgia Tech** Loss 5-15 1268.33 Ignored Feb 26th Easterns Qualifier 2023
351 Central Michigan** Win 13-0 628.27 Ignored Apr 1st Huck Finn1
118 Marquette Loss 7-9 1021.44 Apr 1st Huck Finn1
207 Illinois State Win 7-2 1510.86 Apr 1st Huck Finn1
94 Saint Louis Loss 4-8 859.99 Apr 1st Huck Finn1
104 Florida State Loss 7-8 1220.01 Apr 2nd Huck Finn1
112 Illinois Win 9-5 1845.04 Apr 2nd Huck Finn1
108 Vanderbilt Loss 7-10 937.95 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)