#233 Oklahoma (8-13)

avg: 1005.7  •  sd: 56.71  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
366 Dallas Win 15-11 806.38 Feb 10th Big D in Little D 2024
258 North Texas Win 10-9 1042.11 Feb 10th Big D in Little D 2024
253 Rice Win 11-10 1054.6 Feb 10th Big D in Little D 2024
153 Missouri S&T Loss 7-9 1025.81 Feb 17th Dust Bowl 2024
243 Nebraska Win 9-7 1262.78 Feb 17th Dust Bowl 2024
228 Oklahoma State Win 11-5 1622.23 Feb 17th Dust Bowl 2024
231 Harding Loss 12-15 714.82 Feb 18th Dust Bowl 2024
153 Missouri S&T Loss 11-15 923.99 Feb 18th Dust Bowl 2024
41 Oklahoma Christian** Loss 6-15 1252.87 Ignored Feb 18th Dust Bowl 2024
83 Indiana Loss 8-11 1215.71 Mar 30th Huck Finn 2024
107 Iowa State Loss 6-11 929.44 Mar 30th Huck Finn 2024
82 Mississippi State Loss 1-10 982.9 Mar 30th Huck Finn 2024
69 Central Florida Loss 9-11 1389.33 Mar 31st Huck Finn 2024
118 Kentucky Loss 8-13 919.76 Mar 31st Huck Finn 2024
194 Ohio Win 9-8 1267.62 Mar 31st Huck Finn 2024
95 Arkansas Loss 3-15 934.59 Apr 13th Ozarks D I Mens Conferences 2024
320 Washington University-B Win 11-10 776.34 Apr 13th Ozarks D I Mens Conferences 2024
20 Washington University** Loss 6-15 1486.16 Ignored Apr 13th Ozarks D I Mens Conferences 2024
356 Wichita State Win 14-6 1089.08 Apr 13th Ozarks D I Mens Conferences 2024
161 Saint Louis Loss 8-12 842.32 Apr 14th Ozarks D I Mens Conferences 2024
228 Oklahoma State Loss 8-12 581.08 Apr 14th Ozarks D I Mens Conferences 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)