#41 Florida (10-9)

avg: 1571.02  •  sd: 65.31  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
9 Brown Loss 8-13 1528.91 Feb 2nd Florida Warm Up 2024
10 Carleton College Loss 11-13 1781.5 Feb 2nd Florida Warm Up 2024
37 Texas A&M Win 13-5 2190.94 Feb 2nd Florida Warm Up 2024
12 Alabama-Huntsville Loss 3-13 1393.67 Feb 3rd Florida Warm Up 2024
17 Brigham Young Loss 9-13 1456.87 Feb 3rd Florida Warm Up 2024
27 Georgia Tech Loss 11-15 1358.97 Feb 3rd Florida Warm Up 2024
101 Cornell Win 13-12 1349.57 Feb 4th Florida Warm Up 2024
139 LSU Win 9-6 1503.17 Feb 24th Mardi Gras XXXVI college
261 Texas Tech** Win 13-5 1155.11 Ignored Feb 24th Mardi Gras XXXVI college
230 Texas State** Win 13-1 1314.51 Ignored Feb 24th Mardi Gras XXXVI college
91 Indiana Win 12-9 1616.17 Feb 24th Mardi Gras XXXVI college
110 Arizona State Win 12-7 1713.1 Feb 25th Mardi Gras XXXVI college
82 Central Florida Loss 9-10 1212.27 Feb 25th Mardi Gras XXXVI college
37 Texas A&M Loss 6-11 1044.25 Feb 25th Mardi Gras XXXVI college
17 Brigham Young Loss 9-13 1456.87 Mar 16th College Mens Centex Tier 1
67 Chicago Win 8-6 1687.51 Mar 16th College Mens Centex Tier 1
40 Illinois Loss 11-12 1454.68 Mar 16th College Mens Centex Tier 1
20 Northeastern Win 11-10 1955.32 Mar 16th College Mens Centex Tier 1
48 Missouri Win 10-7 1904.43 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)