#188 Oklahoma State (13-8)

avg: 863.82  •  sd: 59.01  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
363 Dallas Win 12-7 498.76 Feb 22nd Dust Bowl 2025
90 Missouri S&T Loss 8-10 1032.65 Feb 22nd Dust Bowl 2025
203 Nebraska Win 11-10 911.47 Feb 22nd Dust Bowl 2025
136 North Texas Loss 4-8 545.66 Feb 22nd Dust Bowl 2025
168 Truman State Loss 8-11 599.01 Feb 22nd Dust Bowl 2025
239 Texas-Dallas Win 11-5 1246.77 Feb 23rd Dust Bowl 2025
168 Truman State Loss 4-13 364.62 Feb 23rd Dust Bowl 2025
93 Colorado-B Loss 3-13 680.27 Mar 15th Mens Centex 2025
319 Texas A&M-B Win 13-7 831.59 Mar 15th Mens Centex 2025
239 Texas-Dallas Win 10-9 771.77 Mar 15th Mens Centex 2025
270 Texas State Win 12-8 965.69 Mar 15th Mens Centex 2025
135 Mississippi State Loss 11-15 730.12 Mar 16th Mens Centex 2025
227 Tarleton State Win 15-13 909.47 Mar 16th Mens Centex 2025
103 Texas A&M Win 13-12 1365.66 Mar 16th Mens Centex 2025
261 Colorado State-B Win 15-5 1164.61 Mar 29th Free State Classic 2025
225 John Brown Win 14-8 1235.28 Mar 29th Free State Classic 2025
169 Kansas Loss 10-14 561.76 Mar 29th Free State Classic 2025
168 Truman State Win 15-13 1178.79 Mar 29th Free State Classic 2025
261 Colorado State-B Win 9-7 843.94 Mar 30th Free State Classic 2025
323 Kansas State Win 15-7 869.54 Mar 30th Free State Classic 2025
168 Truman State Loss 9-12 619.25 Mar 30th Free State Classic 2025
**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)