#65 Florida (15-7)

avg: 1319.34  •  sd: 73.15  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
59 Central Florida Win 11-5 1997.07 Jan 25th Florida Winter Classic 2025
229 Georgia Tech-B** Win 12-3 763.98 Ignored Jan 25th Florida Winter Classic 2025
156 Georgia Southern** Win 11-3 1273.6 Ignored Jan 25th Florida Winter Classic 2025
236 Florida-B** Win 12-3 683.7 Ignored Jan 26th Florida Winter Classic 2025
172 Florida State Win 9-5 1128.82 Jan 26th Florida Winter Classic 2025
33 Georgia Tech Loss 5-10 1077.71 Jan 26th Florida Winter Classic 2025
2 Carleton College** Loss 0-13 1968.81 Ignored Feb 15th Queen City Tune Up 2025
60 South Carolina Loss 8-9 1259.98 Feb 15th Queen City Tune Up 2025
25 Northeastern Loss 4-10 1202.53 Feb 15th Queen City Tune Up 2025
80 Case Western Reserve Win 5-4 1326.55 Feb 16th Queen City Tune Up 2025
31 Pittsburgh Loss 6-10 1198.06 Feb 16th Queen City Tune Up 2025
59 Central Florida Win 7-6 1522.07 Mar 15th Tally Classic XIX
189 LSU** Win 12-3 1090.29 Ignored Mar 15th Tally Classic XIX
124 Jacksonville State Win 12-4 1485.74 Mar 15th Tally Classic XIX
219 Florida Tech** Win 15-0 816.79 Ignored Apr 12th Florida D I Womens Conferences 2025
169 Miami (Florida)** Win 13-5 1209.04 Ignored Apr 12th Florida D I Womens Conferences 2025
59 Central Florida Loss 10-13 1068.93 Apr 13th Florida D I Womens Conferences 2025
233 Auburn** Win 15-0 724.76 Ignored Apr 26th Southeast D I College Womens Regionals 2025
169 Miami (Florida)** Win 15-4 1209.04 Ignored Apr 26th Southeast D I College Womens Regionals 2025
81 Tennessee Win 14-11 1510.27 Apr 26th Southeast D I College Womens Regionals 2025
99 Emory Win 13-12 1200.33 Apr 27th Southeast D I College Womens Regionals 2025
29 Georgia Loss 10-15 1272.51 Apr 27th Southeast D I College Womens Regionals 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)