#96 Texas A&M (12-12)

avg: 1200.04  •  sd: 65.25  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
115 Florida State Loss 8-10 823.09 Feb 3rd Florida Warm Up 2023
11 Pittsburgh Loss 5-13 1196.38 Feb 3rd Florida Warm Up 2023
67 Auburn Loss 7-11 861.11 Feb 4th Florida Warm Up 2023
1 Massachusetts** Loss 4-13 1547.47 Ignored Feb 4th Florida Warm Up 2023
142 Connecticut Win 11-9 1227.3 Feb 4th Florida Warm Up 2023
204 South Florida Win 12-9 990.76 Feb 4th Florida Warm Up 2023
124 Illinois Win 13-12 1181.06 Feb 5th Florida Warm Up 2023
40 Florida Loss 9-13 1043.94 Feb 5th Florida Warm Up 2023
102 Central Florida Loss 7-12 633.98 Feb 25th Mardi Gras XXXV
303 Mississippi** Win 13-1 467.68 Ignored Feb 25th Mardi Gras XXXV
117 Mississippi State Win 11-6 1622.3 Feb 25th Mardi Gras XXXV
215 Tulane-B Win 11-6 1131.97 Feb 25th Mardi Gras XXXV
39 Alabama-Huntsville Loss 6-8 1165.8 Feb 26th Mardi Gras XXXV
117 Mississippi State Win 8-7 1200.61 Feb 26th Mardi Gras XXXV
175 Sam Houston Win 13-3 1418.3 Feb 26th Mardi Gras XXXV
71 Northwestern Loss 8-13 825.52 Mar 18th Centex 2023
104 Tulane Win 12-11 1266.68 Mar 18th Centex 2023
27 Georgia Tech Loss 3-13 1001.47 Mar 18th Centex 2023
52 Virginia Win 11-8 1743.61 Mar 18th Centex 2023
71 Northwestern Win 13-10 1649.82 Mar 18th Centex 2023
4 Texas Loss 8-15 1384.79 Mar 19th Centex 2023
40 Florida Win 15-14 1587.5 Mar 19th Centex 2023
28 Wisconsin Loss 6-15 995.9 Mar 19th Centex 2023
27 Georgia Tech Loss 4-7 1105.31 Mar 19th Centex 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)