#186 Texas State (6-6)

avg: 997.85  •  sd: 84.4  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
43 Alabama-Huntsville Loss 8-13 1206.97 Feb 25th Mardi Gras XXXV
289 Miami (Florida) Win 13-10 863.49 Feb 25th Mardi Gras XXXV
304 Mississippi State-B Win 11-9 681.23 Feb 25th Mardi Gras XXXV
102 Kennesaw State Loss 9-10 1233.44 Feb 25th Mardi Gras XXXV
87 Tennessee-Chattanooga Loss 6-12 857.29 Feb 26th Mardi Gras XXXV
174 Sam Houston Loss 6-13 454.09 Feb 26th Mardi Gras XXXV
259 Jacksonville State Loss 9-10 571.22 Feb 26th Mardi Gras XXXV
271 Texas A&M-B Win 13-6 1232.68 Mar 11th Centex Tier 2
302 Houston Win 13-8 937.82 Mar 11th Centex Tier 2
161 Rice Win 12-10 1349.93 Mar 11th Centex Tier 2
252 Texas-B Win 15-7 1330 Mar 12th Centex Tier 2
48 Iowa State Loss 9-15 1130.86 Mar 12th Centex Tier 2
**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)