#264 Oklahoma State (3-13)

avg: 681.23  •  sd: 77.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
116 John Brown Loss 7-13 748.44 Feb 5th JPC Just Plain Chilly
254 Oklahoma Loss 9-10 592.48 Feb 5th JPC Just Plain Chilly
28 Oklahoma Christian** Loss 2-13 1243.49 Ignored Feb 5th JPC Just Plain Chilly
35 Missouri** Loss 4-12 1186.83 Ignored Feb 25th Dust Bowl 2023
252 Texas-B Loss 7-10 340.33 Feb 25th Dust Bowl 2023
93 Iowa** Loss 4-11 826.29 Ignored Feb 25th Dust Bowl 2023
161 Rice Loss 7-10 722.14 Feb 25th Dust Bowl 2023
278 Baylor Loss 6-8 289.82 Feb 26th Dust Bowl 2023
254 Oklahoma Win 10-8 980.14 Feb 26th Dust Bowl 2023
269 Harding Loss 8-9 524.53 Feb 26th Dust Bowl 2023
93 Iowa Loss 7-13 868.76 Mar 11th Centex Tier 2
193 North Texas Win 10-8 1237.51 Mar 11th Centex Tier 2
336 Trinity Win 11-5 814.33 Mar 11th Centex Tier 2
252 Texas-B Loss 10-11 605 Mar 12th Centex Tier 2
217 Texas-Dallas Loss 14-15 747.96 Mar 12th Centex Tier 2
249 Texas-San Antonio Loss 7-11 269.9 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)