#342 Oklahoma-B (2-8)

avg: 524.79  •  sd: 108.45  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
144 Colorado College** Loss 2-13 591.77 Ignored Feb 2nd Big D in Little d Open 2019
82 Texas State** Loss 3-13 842.65 Ignored Feb 2nd Big D in Little d Open 2019
409 Texas-Dallas-B Win 12-5 763.11 Feb 2nd Big D in Little d Open 2019
277 Texas-San Antonio Loss 4-13 169.49 Feb 2nd Big D in Little d Open 2019
350 Sam Houston State Loss 3-6 -64.21 Feb 3rd Big D in Little d Open 2019
23 Texas Tech** Loss 3-15 1231.13 Ignored Feb 3rd Big D in Little d Open 2019
175 North Texas Loss 6-15 467.1 Feb 3rd Big D in Little d Open 2019
92 John Brown** Loss 4-15 777.68 Ignored Mar 10th Dust Bowl 2019
284 Dallas Baptist Loss 10-12 497.02 Mar 10th Dust Bowl 2019
- Tulsa Win 15-9 994.29 Mar 10th Dust Bowl 2019
**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)