#31 Texas A&M (8-12)

avg: 1748.41  •  sd: 64.4  •  top 16/20: 2.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
25 South Carolina Loss 10-12 1548.57 Feb 8th Florida Warm Up 2019
22 Georgia Loss 12-13 1709.49 Feb 8th Florida Warm Up 2019
28 Northeastern Win 12-9 2121.2 Feb 8th Florida Warm Up 2019
18 Michigan Loss 8-12 1467.61 Feb 9th Florida Warm Up 2019
2 Brown Loss 9-15 1713.68 Feb 9th Florida Warm Up 2019
55 Florida State Win 13-7 2169.21 Feb 9th Florida Warm Up 2019
54 Virginia Tech Win 11-5 2219.44 Feb 9th Florida Warm Up 2019
4 Pittsburgh Loss 10-12 1946.8 Feb 10th Florida Warm Up 2019
18 Michigan Win 14-13 2033.77 Feb 10th Florida Warm Up 2019
37 Illinois Win 13-9 2138.96 Mar 16th Centex 2019 Men
67 Oklahoma State Loss 14-15 1408.96 Mar 16th Centex 2019 Men
13 Wisconsin Loss 5-13 1400.97 Mar 16th Centex 2019 Men
12 Texas Loss 5-13 1409.9 Mar 16th Centex 2019 Men
37 Illinois Loss 10-12 1482.27 Mar 17th Centex 2019 Men
76 Utah Win 15-9 1989.21 Mar 17th Centex 2019 Men
18 Michigan Loss 8-9 1783.77 Mar 30th Huck Finn XXIII
39 Vermont Loss 9-10 1580.77 Mar 30th Huck Finn XXIII
38 Purdue Loss 7-11 1240.15 Mar 31st Huck Finn XXIII
57 Carnegie Mellon Win 9-6 2005.94 Mar 31st Huck Finn XXIII
39 Vermont Win 9-7 1985.11 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)