#38 Florida (11-16)

avg: 1611.11  •  sd: 66.52  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
3 Ohio State Loss 8-10 2110.33 Jan 19th Florida Winter Classic 2019
8 Dartmouth Loss 4-12 1558.85 Jan 19th Florida Winter Classic 2019
112 Central Florida Win 10-5 1628.16 Jan 19th Florida Winter Classic 2019
43 Georgia Tech Win 8-7 1680.59 Jan 19th Florida Winter Classic 2019
3 Ohio State** Loss 4-10 1773 Ignored Jan 20th Florida Winter Classic 2019
20 North Carolina-Wilmington Win 12-11 2085.18 Jan 20th Florida Winter Classic 2019
26 Georgia Loss 8-14 1312.26 Jan 20th Florida Winter Classic 2019
25 Clemson Win 9-7 2151.62 Feb 9th Queen City Tune Up 2019 Women
41 Harvard Win 8-2 2167.65 Feb 9th Queen City Tune Up 2019 Women
58 Penn State Loss 7-9 1171.71 Feb 9th Queen City Tune Up 2019 Women
11 Pittsburgh Loss 6-9 1664.7 Feb 9th Queen City Tune Up 2019 Women
3 Ohio State** Loss 4-15 1773 Ignored Feb 10th Queen City Tune Up 2019 Women
5 Carleton College-Syzygy** Loss 6-15 1665.5 Ignored Feb 10th Queen City Tune Up 2019 Women
26 Georgia Win 11-6 2394.99 Feb 10th Queen City Tune Up 2019 Women
32 Brigham Young Loss 7-11 1243.59 Mar 2nd Stanford Invite 2019
6 British Columbia** Loss 0-13 1631.77 Ignored Mar 2nd Stanford Invite 2019
21 Cal Poly-SLO Loss 6-12 1364.28 Mar 2nd Stanford Invite 2019
23 California Loss 9-11 1668.71 Mar 2nd Stanford Invite 2019
39 California-Davis Win 8-7 1718.28 Mar 3rd Stanford Invite 2019
50 Whitman Win 9-5 2037.96 Mar 3rd Stanford Invite 2019
69 Notre Dame Loss 9-10 1203.85 Mar 16th Tally Classic XIV
115 South Florida Win 11-3 1650.61 Mar 16th Tally Classic XIV
41 Harvard Win 12-10 1805.78 Mar 16th Tally Classic XIV
51 Florida State Loss 10-11 1383.35 Mar 16th Tally Classic XIV
69 Notre Dame Loss 8-11 963.25 Mar 17th Tally Classic XIV
25 Clemson Loss 3-14 1272.28 Mar 17th Tally Classic XIV
93 Kennesaw State Win 11-7 1654.93 Mar 17th Tally Classic XIV
**Blowout Eligible

FAQ

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
  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
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)