#67 Oklahoma State (13-5)

avg: 1533.96  •  sd: 79.16  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
92 John Brown Win 11-10 1502.68 Feb 2nd Big D in Little d Open 2019
179 Nebraska Win 13-4 1658.84 Feb 2nd Big D in Little d Open 2019
194 Kansas State Win 13-2 1607.61 Feb 2nd Big D in Little d Open 2019
394 North Texas-B** Win 13-0 860.18 Ignored Feb 2nd Big D in Little d Open 2019
63 Rice Loss 11-12 1421.14 Feb 3rd Big D in Little d Open 2019
350 Sam Houston State** Win 15-0 1082.48 Ignored Feb 3rd Big D in Little d Open 2019
194 Kansas State Win 11-1 1607.61 Feb 3rd Big D in Little d Open 2019
130 Baylor Win 15-10 1724.54 Feb 3rd Big D in Little d Open 2019
179 Nebraska Win 15-6 1658.84 Mar 10th Dust Bowl 2019
144 Colorado College Win 15-8 1756.57 Mar 10th Dust Bowl 2019
406 Colorado School of Mines - B** Win 15-1 787.01 Ignored Mar 10th Dust Bowl 2019
80 Oklahoma Loss 10-15 998.36 Mar 10th Dust Bowl 2019
98 Kansas Win 12-9 1708.55 Mar 16th Centex 2019 Men
152 Arkansas Win 13-8 1649.36 Mar 16th Centex 2019 Men
40 Dartmouth Loss 4-13 1086.47 Mar 16th Centex 2019 Men
31 Texas A&M Win 15-14 1873.41 Mar 16th Centex 2019 Men
27 LSU Loss 11-15 1396.57 Mar 17th Centex 2019 Men
19 Colorado State Loss 10-15 1445.94 Mar 17th Centex 2019 Men
**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)