#37 Texas A&M (11-8)

avg: 1590.94  •  sd: 109.83  •  top 16/20: 0.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
41 Florida Loss 5-13 971.02 Feb 2nd Florida Warm Up 2024
27 Georgia Tech Loss 5-12 1140.14 Feb 2nd Florida Warm Up 2024
11 Minnesota Win 11-10 2127.24 Feb 2nd Florida Warm Up 2024
17 Brigham Young Loss 8-13 1379.28 Feb 3rd Florida Warm Up 2024
9 Brown Loss 8-13 1528.91 Feb 3rd Florida Warm Up 2024
42 Michigan Win 14-10 1965.12 Feb 4th Florida Warm Up 2024
33 Wisconsin Loss 11-15 1264.34 Feb 4th Florida Warm Up 2024
82 Central Florida Win 13-9 1755.84 Feb 24th Mardi Gras XXXVI college
220 Sam Houston** Win 13-0 1343.05 Ignored Feb 24th Mardi Gras XXXVI college
274 Trinity** Win 13-3 1103.96 Ignored Feb 24th Mardi Gras XXXVI college
379 Tulane-B** Win 13-0 172.13 Ignored Feb 24th Mardi Gras XXXVI college
132 Arkansas Win 13-3 1711.85 Feb 25th Mardi Gras XXXVI college
41 Florida Win 11-6 2117.72 Feb 25th Mardi Gras XXXVI college
91 Indiana Loss 9-10 1145.81 Feb 25th Mardi Gras XXXVI college
17 Brigham Young Loss 6-13 1275.44 Mar 15th College Mens Centex Tier 1
128 Colorado College Win 12-9 1481.37 Mar 16th College Mens Centex Tier 1
55 Michigan State Win 13-6 2065.76 Mar 16th College Mens Centex Tier 1
139 LSU Win 13-5 1684.6 Mar 16th College Mens Centex Tier 1
14 Texas Loss 9-11 1687.43 Mar 17th College Mens Centex Tier 1
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)