#12 Texas (14-6)

avg: 2009.9  •  sd: 72.93  •  top 16/20: 97%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
17 Minnesota Win 12-10 2189.17 Feb 8th Florida Warm Up 2019
6 Brigham Young Loss 12-13 2009.73 Feb 8th Florida Warm Up 2019
15 Central Florida Loss 7-13 1432.78 Feb 8th Florida Warm Up 2019
83 Rutgers Win 12-6 2012.28 Feb 9th Florida Warm Up 2019
4 Pittsburgh Win 15-10 2638.53 Feb 9th Florida Warm Up 2019
27 LSU Win 10-7 2167.4 Feb 9th Florida Warm Up 2019
20 Tufts Win 11-9 2113.35 Feb 9th Florida Warm Up 2019
15 Central Florida Loss 11-12 1865.32 Feb 10th Florida Warm Up 2019
22 Georgia Win 13-10 2162.64 Feb 10th Florida Warm Up 2019
17 Minnesota Win 13-12 2076.05 Mar 2nd Stanford Invite 2019
5 Cal Poly-SLO Win 13-10 2472.6 Mar 2nd Stanford Invite 2019
8 Colorado Win 10-7 2485.11 Mar 2nd Stanford Invite 2019
14 Ohio State Win 12-10 2230.19 Mar 3rd Stanford Invite 2019
1 North Carolina Loss 6-13 1631.92 Mar 3rd Stanford Invite 2019
37 Illinois Win 13-12 1845.39 Mar 16th Centex 2019 Men
13 Wisconsin Loss 9-13 1582.4 Mar 16th Centex 2019 Men
31 Texas A&M Win 13-5 2348.41 Mar 16th Centex 2019 Men
46 Iowa State Win 15-14 1784.23 Mar 17th Centex 2019 Men
27 LSU Win 13-12 1902.74 Mar 17th Centex 2019 Men
8 Colorado Loss 7-15 1495.44 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)