#212 Texas Christian (8-4)

avg: 950.62  •  sd: 74  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
322 Mississippi Win 11-7 1054.11 Mar 2nd Mardi Gras XXXII
159 Mississippi State Loss 5-11 525.81 Mar 2nd Mardi Gras XXXII
65 Florida Loss 1-13 935.75 Mar 2nd Mardi Gras XXXII
207 North Florida Loss 7-11 498.62 Mar 2nd Mardi Gras XXXII
363 Texas State -B Win 13-4 1015.34 Mar 2nd Mardi Gras XXXII
322 Mississippi Win 12-6 1166.52 Mar 3rd Mardi Gras XXXII
167 Minnesota State-Mankato Loss 9-12 743.92 Mar 3rd Mardi Gras XXXII
381 Southern Methodist Win 12-6 905.96 Mar 24th Greatest Crusade V
287 Abilene Christian Win 10-5 1292.4 Mar 24th Greatest Crusade V
232 Lamar Win 13-7 1466.57 Mar 24th Greatest Crusade V
284 Dallas Baptist Win 9-8 860.15 Mar 24th Greatest Crusade V
398 Texas-Arlington Win 13-6 828.31 Mar 24th Greatest Crusade V
**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)