#47 Oklahoma Christian (12-6)

avg: 1520.17  •  sd: 75.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
24 British Columbia Loss 12-13 1675.53 Jan 27th Santa Barbara Invite 2024
5 Cal Poly-SLO Loss 7-15 1574.71 Jan 27th Santa Barbara Invite 2024
67 Chicago Win 11-7 1853.91 Jan 27th Santa Barbara Invite 2024
79 Grand Canyon Win 11-8 1706.31 Jan 27th Santa Barbara Invite 2024
43 California-San Diego Win 12-11 1687.26 Jan 28th Santa Barbara Invite 2024
35 California-Santa Cruz Loss 7-11 1170.31 Jan 28th Santa Barbara Invite 2024
30 Utah Loss 9-11 1427.79 Jan 28th Santa Barbara Invite 2024
229 Northern Iowa Win 11-6 1262.49 Feb 17th Dust Bowl 2024
335 Wichita State** Win 13-0 721.02 Ignored Feb 17th Dust Bowl 2024
218 Texas-Dallas** Win 12-2 1357.85 Ignored Feb 17th Dust Bowl 2024
209 Oklahoma** Win 15-6 1384 Ignored Feb 18th Dust Bowl 2024
108 Wisconsin-Milwaukee Win 11-7 1666.59 Feb 18th Dust Bowl 2024
218 Texas-Dallas Win 12-8 1199.01 Feb 18th Dust Bowl 2024
53 Colorado State Win 12-9 1815.92 Mar 16th College Mens Centex Tier 1
55 Michigan State Loss 8-12 1024.6 Mar 16th College Mens Centex Tier 1
48 Missouri Win 8-5 1968.37 Mar 16th College Mens Centex Tier 1
44 Tulane Win 11-10 1666.68 Mar 16th College Mens Centex Tier 1
40 Illinois Loss 9-12 1234.32 Mar 17th College Mens Centex Tier 1
**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)