#37 Oklahoma State (11-5)

avg: 1620.27  •  sd: 65.55  •  top 16/20: 0.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
4 Cal Poly-SLO Loss 8-14 1642.15 Feb 15th Presidents Day Invite 2020
54 California-Davis Win 15-7 2078.06 Feb 15th Presidents Day Invite 2020
57 Illinois Win 11-9 1702.12 Feb 15th Presidents Day Invite 2020
5 Colorado Loss 6-14 1553.58 Feb 16th Presidents Day Invite 2020
36 California-Santa Cruz Win 12-9 1974.21 Feb 16th Presidents Day Invite 2020
2 Washington Loss 8-15 1711.64 Feb 16th Presidents Day Invite 2020
42 Utah Loss 8-11 1234.23 Feb 16th Presidents Day Invite 2020
39 California-San Diego Win 14-8 2153.39 Feb 17th Presidents Day Invite 2020
28 California-Santa Barbara Loss 7-11 1249.6 Feb 17th Presidents Day Invite 2020
208 Colorado School of Mines Win 15-7 1414.15 Feb 22nd Dust Bowl 2020
100 Truman State Win 15-10 1663.33 Feb 22nd Dust Bowl 2020
199 Texas Christian Win 15-7 1435.08 Feb 22nd Dust Bowl 2020
133 Missouri Win 12-10 1330.79 Feb 23rd Dust Bowl 2020
136 Oklahoma Win 15-10 1516.66 Feb 23rd Dust Bowl 2020
94 Denver Win 14-10 1654.55 Feb 23rd Dust Bowl 2020
61 Washington University Win 15-13 1638.58 Feb 23rd Dust Bowl 2020
**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)