#47 Florida (14-11)

avg: 1467.9  •  sd: 96.6  •  top 16/20: 0.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
82 Central Florida Loss 7-9 850.78 Jan 28th Florida Winter Classic 2023
203 Miami (Florida)** Win 13-1 645.72 Ignored Jan 28th Florida Winter Classic 2023
10 Northeastern Loss 6-9 1715.77 Jan 28th Florida Winter Classic 2023
216 Florida-B** Win 13-1 336.49 Ignored Jan 28th Florida Winter Classic 2023
82 Central Florida Win 9-3 1730.12 Jan 29th Florida Winter Classic 2023
210 Florida Tech** Win 13-0 512.58 Ignored Jan 29th Florida Winter Classic 2023
46 Florida State Loss 7-8 1348.72 Jan 29th Florida Winter Classic 2023
69 Case Western Reserve Loss 7-11 783.56 Feb 11th Queen City Tune Up1
21 North Carolina State Win 10-7 2145.97 Feb 11th Queen City Tune Up1
13 Pittsburgh Loss 10-13 1605.2 Feb 11th Queen City Tune Up1
5 Vermont** Loss 6-15 1773.3 Ignored Feb 11th Queen City Tune Up1
64 Appalachian State Loss 6-7 1149.41 Feb 12th Queen City Tune Up1
202 North Carolina-Wilmington** Win 13-2 645.79 Ignored Feb 12th Queen City Tune Up1
33 Ohio State Loss 8-12 1192.75 Feb 25th Commonwealth Cup Weekend2 2023
95 Temple Win 11-7 1496 Feb 25th Commonwealth Cup Weekend2 2023
71 Massachusetts Loss 11-13 1004.64 Feb 25th Commonwealth Cup Weekend2 2023
56 Tennessee Win 9-8 1465.42 Feb 26th Commonwealth Cup Weekend2 2023
99 MIT Win 13-10 1311.46 Feb 26th Commonwealth Cup Weekend2 2023
87 Southern California Win 13-3 1686.09 Mar 18th Womens Centex1
91 Colorado College Win 13-3 1655.06 Mar 18th Womens Centex1
16 Middlebury Loss 11-13 1607 Mar 18th Womens Centex1
75 Boston University Win 13-8 1717.73 Mar 19th Womens Centex1
44 Pennsylvania Win 13-10 1812.45 Mar 19th Womens Centex1
16 Middlebury Loss 9-15 1320.36 Mar 19th Womens Centex1
54 Georgia Tech Win 11-10 1477.86 Mar 19th Womens Centex1
**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)