#68 Cincinnati (16-9)

avg: 1515.37  •  sd: 64.84  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
136 South Florida Win 13-10 1565.17 Feb 8th Florida Warm Up 2019
55 Florida State Loss 8-13 1115.51 Feb 8th Florida Warm Up 2019
150 Cornell Loss 10-13 849.94 Feb 8th Florida Warm Up 2019
98 Kansas Loss 7-8 1238.18 Feb 9th Florida Warm Up 2019
6 Brigham Young** Loss 5-13 1534.73 Ignored Feb 9th Florida Warm Up 2019
72 Alabama-Huntsville Win 11-9 1733.2 Feb 9th Florida Warm Up 2019
80 Oklahoma Loss 7-13 894.44 Feb 9th Florida Warm Up 2019
65 Florida Loss 12-13 1410.75 Feb 10th Florida Warm Up 2019
127 Boston College Win 10-3 1874.72 Feb 10th Florida Warm Up 2019
226 Miami (Ohio) Win 13-4 1516.45 Mar 9th Boogienights 2019
204 SUNY-Buffalo Win 13-8 1467.96 Mar 9th Boogienights 2019
368 Cleveland State** Win 13-2 987.5 Ignored Mar 9th Boogienights 2019
148 Michigan-B Win 15-9 1697.43 Mar 10th Boogienights 2019
204 SUNY-Buffalo Win 15-9 1487.29 Mar 10th Boogienights 2019
159 Mississippi State Win 13-3 1725.81 Mar 16th Tally Classic XIV
36 Alabama Win 13-10 2051.28 Mar 16th Tally Classic XIV
61 Tennessee Loss 6-13 954.19 Mar 16th Tally Classic XIV
88 Tennessee-Chattanooga Win 15-14 1544.19 Mar 16th Tally Classic XIV
165 Georgia Southern Win 15-8 1656.72 Mar 17th Tally Classic XIV
52 Notre Dame Loss 12-15 1326.18 Mar 17th Tally Classic XIV
106 Illinois State Win 10-8 1590 Mar 30th Huck Finn XXIII
46 Iowa State Win 11-7 2126.13 Mar 31st Huck Finn XXIII
37 Illinois Loss 6-10 1224.23 Mar 31st Huck Finn XXIII
152 Arkansas Win 9-5 1682.26 Mar 31st Huck Finn XXIII
86 Marquette Win 7-5 1754.22 Mar 31st Huck Finn XXIII
**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)