#88 Alabama-Huntsville (12-8)

avg: 1388.31  •  sd: 73.79  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
241 Harding** Win 13-4 1387.38 Ignored Jan 20th T Town Throwdown XIV Open
120 Mississippi State Win 9-6 1679.86 Jan 20th T Town Throwdown XIV Open
66 Kennesaw State Win 13-6 2058.01 Jan 20th T Town Throwdown XIV Open
264 LSU-B** Win 13-3 1334.8 Ignored Jan 20th T Town Throwdown XIV Open
45 Illinois State Win 15-13 1800.33 Jan 21st T Town Throwdown XIV Open
66 Kennesaw State Loss 8-15 893.2 Jan 21st T Town Throwdown XIV Open
23 Georgia Tech Win 13-12 1868.96 Jan 21st T Town Throwdown XIV Open
181 Ball State Loss 7-9 731.42 Feb 24th Music City Tune Up 2018
155 Vanderbilt Loss 10-11 987.8 Feb 24th Music City Tune Up 2018
153 Xavier Win 10-9 1240.49 Feb 24th Music City Tune Up 2018
236 Middle Tennessee State Win 11-7 1267.14 Feb 24th Music City Tune Up 2018
124 Indiana Win 13-9 1644.83 Mar 10th Tally Classic XIII
16 North Carolina-Wilmington Loss 6-13 1284.51 Mar 10th Tally Classic XIII
224 Georgia Southern Win 13-7 1418.77 Mar 10th Tally Classic XIII
8 Massachusetts Loss 10-15 1510.16 Mar 10th Tally Classic XIII
168 South Florida Win 13-1 1663.99 Mar 10th Tally Classic XIII
46 South Carolina Loss 11-15 1198.2 Mar 11th Tally Classic XIII
70 Arkansas Loss 11-12 1314.53 Mar 31st Huck Finn 2018
163 Wisconsin- La Crosse Win 15-8 1643.58 Mar 31st Huck Finn 2018
62 Vermont Loss 11-15 1084.67 Mar 31st Huck Finn 2018
**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)