#72 Alabama-Huntsville (10-12)

avg: 1483.99  •  sd: 58.37  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
37 Illinois Loss 7-13 1162.86 Jan 26th T Town Throwdown
27 LSU Loss 5-13 1177.74 Jan 26th T Town Throwdown
132 Kentucky Win 11-7 1718.05 Jan 26th T Town Throwdown
160 Vanderbilt Loss 9-11 875.17 Jan 26th T Town Throwdown
36 Alabama Loss 10-15 1269.53 Jan 27th T Town Throwdown
24 Auburn Loss 11-14 1483.44 Jan 27th T Town Throwdown
106 Illinois State Win 14-12 1548.3 Jan 27th T Town Throwdown
25 South Carolina Loss 10-13 1458.55 Feb 8th Florida Warm Up 2019
29 Texas-Dallas Loss 7-13 1214.37 Feb 8th Florida Warm Up 2019
150 Cornell Win 11-7 1644.98 Feb 8th Florida Warm Up 2019
83 Rutgers Win 15-11 1814.14 Feb 9th Florida Warm Up 2019
68 Cincinnati Loss 9-11 1266.17 Feb 9th Florida Warm Up 2019
43 Harvard Win 9-8 1797.28 Feb 9th Florida Warm Up 2019
54 Virginia Tech Win 9-8 1744.44 Feb 9th Florida Warm Up 2019
80 Oklahoma Win 13-11 1680.81 Feb 10th Florida Warm Up 2019
13 Wisconsin Loss 7-15 1400.97 Feb 10th Florida Warm Up 2019
15 Central Florida Loss 9-12 1644.95 Mar 16th Tally Classic XIV
165 Georgia Southern Win 13-11 1320.75 Mar 16th Tally Classic XIV
52 Notre Dame Win 13-10 1954.81 Mar 16th Tally Classic XIV
61 Tennessee Win 15-14 1679.19 Mar 16th Tally Classic XIV
24 Auburn Loss 10-12 1558.65 Mar 17th Tally Classic XIV
55 Florida State Loss 8-15 1046.86 Mar 17th Tally Classic XIV
**Blowout Eligible

FAQ

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
  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
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)