#57 Oklahoma Christian (19-7)

avg: 1656.39  •  sd: 58.64  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
324 Kansas State** Win 13-0 1187.15 Ignored Feb 22nd Dust Bowl 2025
171 Northern Iowa Win 13-9 1597.83 Feb 22nd Dust Bowl 2025
245 Texas-Dallas Win 9-7 1165.71 Feb 22nd Dust Bowl 2025
87 Missouri S&T Win 9-8 1616.08 Feb 23rd Dust Bowl 2025
133 North Texas Win 8-7 1438 Feb 23rd Dust Bowl 2025
139 Wichita State Win 12-3 1897.37 Feb 23rd Dust Bowl 2025
110 Iowa Win 11-4 2005.74 Mar 15th Mens Centex 2025
97 Missouri Win 12-10 1698.1 Mar 15th Mens Centex 2025
92 Texas A&M Win 12-10 1716 Mar 15th Mens Centex 2025
50 Tulane Win 8-7 1833.09 Mar 15th Mens Centex 2025
14 Texas Loss 11-15 1661.07 Mar 16th Mens Centex 2025
25 Utah Valley Loss 6-15 1344 Mar 16th Mens Centex 2025
42 Colorado State Loss 8-10 1503.98 Mar 29th Huck Finn 2025
47 Emory Win 14-10 2134.18 Mar 29th Huck Finn 2025
11 Washington University Loss 7-11 1615.67 Mar 29th Huck Finn 2025
53 Purdue Loss 9-11 1443.68 Mar 29th Huck Finn 2025
92 Texas A&M Loss 10-14 1079.18 Mar 30th Huck Finn 2025
95 Southern Illinois-Edwardsville Loss 8-13 974.8 Mar 30th Huck Finn 2025
288 Harding** Win 13-4 1345.34 Ignored Apr 12th Ozarks D III Mens Conferences 2025
195 John Brown Win 13-3 1669.63 Apr 12th Ozarks D III Mens Conferences 2025
87 Missouri S&T Win 13-8 1987.24 Apr 12th Ozarks D III Mens Conferences 2025
164 Truman State Win 13-4 1807.14 Apr 12th Ozarks D III Mens Conferences 2025
288 Harding** Win 15-3 1345.34 Ignored Apr 26th South Central D III College Mens Regionals 2025
164 Truman State Win 10-7 1596.8 Apr 26th South Central D III College Mens Regionals 2025
89 Colorado College Win 12-5 2085.28 Apr 27th South Central D III College Mens Regionals 2025
87 Missouri S&T Win 13-9 1909.65 Apr 27th South Central D III College Mens Regionals 2025
**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)