#219 Florida Tech (5-12)

avg: 216.79  •  sd: 79.13  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
248 South Florida Win 12-3 614.63 Feb 8th Tropical Toss up
169 Miami (Florida) Loss 5-7 280.9 Feb 8th Tropical Toss up
172 Florida State Win 6-5 724.77 Mar 15th Tally Classic XIX
189 LSU Loss 5-8 36.69 Mar 15th Tally Classic XIX
214 Minnesota-Duluth Win 10-5 842.07 Mar 15th Tally Classic XIX
177 Tulane Loss 4-8 7.37 Mar 15th Tally Classic XIX
78 Appalachian State** Loss 0-13 615.16 Ignored Mar 29th Needle in a Ho Stack 2025
155 Berry Loss 6-9 258.86 Mar 29th Needle in a Ho Stack 2025
258 Emory-B Win 9-1 286.02 Mar 29th Needle in a Ho Stack 2025
81 Tennessee** Loss 3-13 596.93 Ignored Mar 29th Needle in a Ho Stack 2025
83 Clemson** Loss 5-15 563.67 Ignored Mar 30th Needle in a Ho Stack 2025
226 South Carolina-B Loss 6-8 -113.03 Mar 30th Needle in a Ho Stack 2025
65 Florida** Loss 0-15 719.34 Ignored Apr 12th Florida D I Womens Conferences 2025
169 Miami (Florida) Loss 4-7 112.88 Apr 12th Florida D I Womens Conferences 2025
172 Florida State Loss 3-14 -0.23 Apr 13th Florida D I Womens Conferences 2025
236 Florida-B Loss 6-7 -41.3 Apr 13th Florida D I Womens Conferences 2025
248 South Florida Win 11-10 139.63 Apr 13th Florida D I Womens Conferences 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)