#51 Florida State (15-12)

avg: 1508.35  •  sd: 50.3  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
20 North Carolina-Wilmington Loss 5-10 1386.28 Jan 19th Florida Winter Classic 2019
8 Dartmouth** Loss 3-10 1558.85 Ignored Jan 19th Florida Winter Classic 2019
26 Georgia Loss 6-10 1352.14 Jan 19th Florida Winter Classic 2019
43 Georgia Tech Win 6-5 1680.59 Jan 19th Florida Winter Classic 2019
20 North Carolina-Wilmington Loss 7-9 1680.84 Jan 20th Florida Winter Classic 2019
26 Georgia Loss 11-12 1723.3 Jan 20th Florida Winter Classic 2019
112 Central Florida Win 13-1 1654.26 Jan 20th Florida Winter Classic 2019
81 Ohio Win 13-8 1764.46 Feb 23rd Commonwealth Cup 2019
27 Delaware Loss 8-9 1689.9 Feb 23rd Commonwealth Cup 2019
223 Elon** Win 13-1 983.22 Ignored Feb 23rd Commonwealth Cup 2019
231 Pennsylvania-B** Win 9-0 917.18 Ignored Feb 23rd Commonwealth Cup 2019
59 Duke Loss 6-8 1145.47 Feb 24th Commonwealth Cup 2019
117 Catholic Win 12-7 1564.31 Feb 24th Commonwealth Cup 2019
25 Clemson Loss 4-13 1272.28 Mar 16th Tally Classic XIV
38 Florida Win 11-10 1736.11 Mar 16th Tally Classic XIV
189 Tulane** Win 13-1 1193.98 Ignored Mar 16th Tally Classic XIV
93 Kennesaw State Win 11-6 1734.74 Mar 16th Tally Classic XIV
69 Notre Dame Loss 11-12 1203.85 Mar 17th Tally Classic XIV
115 South Florida Win 11-10 1175.61 Mar 17th Tally Classic XIV
93 Kennesaw State Win 15-10 1641.64 Mar 17th Tally Classic XIV
29 Northwestern Loss 6-10 1271.46 Mar 23rd Womens College Centex 2019
89 Iowa State Win 8-4 1787.91 Mar 23rd Womens College Centex 2019
37 Washington University Loss 7-8 1545.56 Mar 23rd Womens College Centex 2019
34 Colorado College Loss 4-14 1103.78 Mar 24th Womens College Centex 2019
132 Boston University Win 10-4 1579.62 Mar 24th Womens College Centex 2019
59 Duke Win 9-8 1570.96 Mar 24th Womens College Centex 2019
43 Georgia Tech Win 8-7 1680.59 Mar 24th Womens College Centex 2019
**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)