#298 Texas A&M-B (4-7)

avg: 683.95  •  sd: 83.28  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
322 Mississippi Loss 10-13 259.07 Mar 2nd Mardi Gras XXXII
159 Mississippi State Loss 4-11 525.81 Mar 2nd Mardi Gras XXXII
296 LSU-B Win 11-8 1061.42 Mar 2nd Mardi Gras XXXII
209 Trinity Loss 4-11 357.89 Mar 2nd Mardi Gras XXXII
296 LSU-B Win 8-7 820.81 Mar 3rd Mardi Gras XXXII
63 Rice** Loss 3-13 946.14 Ignored Mar 16th Centex 2019 Men
175 North Texas Loss 7-13 509.57 Mar 16th Centex 2019 Men
205 Wisconsin-B Loss 10-15 516.99 Mar 16th Centex 2019 Men
124 Wisconsin-Milwaukee Loss 3-13 678.72 Mar 16th Centex 2019 Men
296 LSU-B Win 14-10 1094.51 Mar 17th Centex 2019 Men
378 Houston Win 15-7 949.7 Mar 17th Centex 2019 Men
**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)