#305 Oklahoma-B (5-12)

avg: 561.22  •  sd: 113.06  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
170 Kansas State Loss 3-12 459.64 Feb 3rd Big D in Little d Open 2018
82 Oklahoma State** Loss 0-13 807.19 Ignored Feb 3rd Big D in Little d Open 2018
336 Texas-Dallas-B Loss 3-12 -175.7 Feb 3rd Big D in Little d Open 2018
287 Central Arkansas Win 10-9 766.28 Feb 3rd Big D in Little d Open 2018
27 Texas State** Loss 5-13 1121.16 Ignored Feb 3rd Big D in Little d Open 2018
344 Dallas Loss 7-15 -206.61 Feb 4th Big D in Little d Open 2018
387 North Texas-B Win 15-3 783.67 Feb 4th Big D in Little d Open 2018
336 Texas-Dallas-B Loss 10-15 -29.3 Feb 4th Big D in Little d Open 2018
379 Southern Methodist Loss 6-8 -62.84 Feb 4th Big D in Little d Open 2018
99 Missouri S&T Loss 6-7 1212.88 Feb 24th Dust Bowl 2018
96 Missouri State** Loss 1-11 750.5 Ignored Feb 24th Dust Bowl 2018
284 Tulsa Loss 10-11 521.62 Feb 24th Dust Bowl 2018
176 Colorado State-B Win 7-6 1151.62 Feb 24th Dust Bowl 2018
342 Washington University-B Win 13-9 814.93 Feb 24th Dust Bowl 2018
199 Stephen F Austin Loss 9-12 589.11 Feb 24th Dust Bowl 2018
287 Central Arkansas Loss 10-11 516.28 Feb 25th Dust Bowl 2018
284 Tulsa Win 15-5 1246.62 Feb 25th Dust Bowl 2018
**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)