#241 Florida-B (1-17)

avg: -164.95  •  sd: 173.74  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
32 Central Florida** Loss 0-11 1217.19 Ignored Mar 2nd Florida Spring Showcase
61 Florida** Loss 0-6 893.44 Ignored Mar 2nd Florida Spring Showcase
194 Georgia College** Loss 1-11 -80.8 Ignored Mar 2nd Florida Spring Showcase
221 LSU Loss 2-11 -343.36 Mar 2nd Florida Spring Showcase
202 Alabama Loss 3-10 -165.93 Mar 3rd Florida Spring Showcase
234 Florida Tech Win 7-3 661.95 Mar 3rd Florida Spring Showcase
221 LSU Loss 1-4 -343.36 Mar 3rd Florida Spring Showcase
37 Carleton College-Eclipse** Loss 0-13 1149.41 Ignored Mar 16th Southerns 2024
120 Charleston** Loss 1-13 482.15 Ignored Mar 16th Southerns 2024
225 North Carolina-Wilmington Loss 3-13 -382.84 Mar 16th Southerns 2024
226 Georgia Southern Loss 5-7 -129.28 Mar 16th Southerns 2024
218 Georgia-B Loss 4-9 -315.64 Mar 17th Southerns 2024
225 North Carolina-Wilmington Loss 4-13 -382.84 Mar 17th Southerns 2024
32 Central Florida** Loss 0-15 1217.19 Ignored Apr 13th Florida D I Womens Conferences 2024
61 Florida** Loss 1-15 893.44 Ignored Apr 13th Florida D I Womens Conferences 2024
214 Miami (Florida) Loss 3-12 -282.76 Apr 13th Florida D I Womens Conferences 2024
107 Florida State** Loss 0-15 582.08 Ignored Apr 14th Florida D I Womens Conferences 2024
214 Miami (Florida) Loss 6-7 192.24 Apr 14th Florida D I Womens Conferences 2024
**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)