#254 Oklahoma (5-10)

avg: 717.48  •  sd: 90.3  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
116 John Brown Loss 8-13 809.82 Feb 5th JPC Just Plain Chilly
264 Oklahoma State Win 10-9 806.23 Feb 5th JPC Just Plain Chilly
28 Oklahoma Christian** Loss 3-13 1243.49 Ignored Feb 5th JPC Just Plain Chilly
146 Kansas Loss 6-9 750.32 Feb 25th Dust Bowl 2023
271 Texas A&M-B Loss 8-11 267.07 Feb 25th Dust Bowl 2023
207 Illinois State Loss 4-10 310.86 Feb 25th Dust Bowl 2023
28 Oklahoma Christian** Loss 5-12 1243.49 Ignored Feb 25th Dust Bowl 2023
189 Luther Loss 9-10 870.04 Feb 26th Dust Bowl 2023
271 Texas A&M-B Loss 8-11 267.07 Feb 26th Dust Bowl 2023
264 Oklahoma State Loss 8-10 418.57 Feb 26th Dust Bowl 2023
278 Baylor Win 13-9 1008.88 Apr 1st April Fools Showdown
- Tarleton State Loss 10-12 419.29 Apr 1st April Fools Showdown
249 Texas-San Antonio Win 13-8 1232.95 Apr 1st April Fools Showdown
278 Baylor Win 10-6 1086.47 Apr 2nd April Fools Showdown
- Tarleton State Win 9-8 782.41 Apr 2nd April Fools Showdown
**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)