#71 Florida (6-5)

avg: 1377.42  •  sd: 142.97  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
84 Central Florida Win 11-5 1818.43 Jan 25th Florida Winter Classic 2025
196 Georgia Tech-B** Win 12-3 503.4 Ignored Jan 25th Florida Winter Classic 2025
190 Georgia Southern** Win 11-3 715.6 Ignored Jan 25th Florida Winter Classic 2025
191 Florida-B** Win 12-3 711.08 Ignored Jan 26th Florida Winter Classic 2025
150 Florida State Win 9-5 1224.77 Jan 26th Florida Winter Classic 2025
38 Georgia Tech Loss 5-10 1154.48 Jan 26th Florida Winter Classic 2025
3 Carleton College** Loss 0-13 2108.16 Ignored Feb 15th Queen City Tune Up 2025
57 South Carolina Loss 8-9 1380.46 Feb 15th Queen City Tune Up 2025
18 Northeastern** Loss 4-10 1522.77 Ignored Feb 15th Queen City Tune Up 2025
81 Case Western Reserve Win 5-4 1372.55 Feb 16th Queen City Tune Up 2025
29 Pittsburgh Loss 6-10 1308.69 Feb 16th Queen City Tune Up 2025
**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)