#199 Stephen F Austin (19-10)

avg: 934.47  •  sd: 52.15  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
344 Dallas Win 10-5 967.29 Feb 3rd Big D in Little d Open 2018
160 Oklahoma Loss 8-9 967.6 Feb 3rd Big D in Little d Open 2018
200 Rice Loss 13-14 807.65 Feb 3rd Big D in Little d Open 2018
217 Texas Christian Win 13-5 1488.31 Feb 3rd Big D in Little d Open 2018
112 Texas Tech Loss 8-12 843.93 Feb 3rd Big D in Little d Open 2018
130 North Texas Win 14-13 1317.13 Feb 4th Big D in Little d Open 2018
351 Texas-Arlington Win 14-10 762.18 Feb 4th Big D in Little d Open 2018
373 Creighton Win 10-6 787.84 Feb 24th Dust Bowl 2018
391 Kansas B-B** Win 15-5 747.05 Ignored Feb 24th Dust Bowl 2018
305 Oklahoma-B Win 12-9 906.58 Feb 24th Dust Bowl 2018
387 North Texas-B** Win 9-1 783.67 Ignored Feb 24th Dust Bowl 2018
128 Colorado School of Mines Loss 2-11 603.86 Feb 24th Dust Bowl 2018
336 Texas-Dallas-B Win 11-3 1024.3 Feb 24th Dust Bowl 2018
152 Denver Loss 4-15 516.31 Feb 25th Dust Bowl 2018
176 Colorado State-B Loss 9-12 681.25 Feb 25th Dust Bowl 2018
160 Oklahoma Win 11-9 1341.81 Mar 10th Mens Centex 2018
200 Rice Win 13-6 1532.65 Mar 10th Mens Centex 2018
63 Tulane Loss 6-12 884.37 Mar 10th Mens Centex 2018
184 Texas-San Antonio Loss 7-13 426.57 Mar 10th Mens Centex 2018
264 LSU-B Win 15-8 1299.61 Mar 11th Mens Centex 2018
176 Colorado State-B Loss 11-13 797.78 Mar 11th Mens Centex 2018
187 Texas A&M-B Win 13-12 1106.47 Mar 11th Mens Centex 2018
387 North Texas-B** Win 13-5 783.67 Ignored Mar 24th Greatest Crusade IV
379 Southern Methodist** Win 13-4 837.65 Ignored Mar 24th Greatest Crusade IV
411 Texas State -B** Win 13-1 524.42 Ignored Mar 24th Greatest Crusade IV
297 Trinity University Win 13-9 1012.12 Mar 24th Greatest Crusade IV
212 Baylor University-B Loss 11-15 525.12 Mar 25th Greatest Crusade IV
241 Harding Win 12-11 912.38 Mar 25th Greatest Crusade IV
351 Texas-Arlington Win 15-5 963.48 Mar 25th Greatest Crusade IV
**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)