#65 Florida (11-10)

avg: 1535.75  •  sd: 55.04  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
83 Rutgers Loss 9-11 1183.76 Feb 8th Florida Warm Up 2019
18 Michigan Loss 6-13 1308.77 Feb 8th Florida Warm Up 2019
13 Wisconsin Loss 11-13 1772.13 Feb 8th Florida Warm Up 2019
98 Kansas Win 13-5 1963.18 Feb 9th Florida Warm Up 2019
49 Northwestern Win 11-10 1762.69 Feb 9th Florida Warm Up 2019
150 Cornell Loss 12-13 1053.08 Feb 9th Florida Warm Up 2019
73 Temple Loss 9-15 965.39 Feb 9th Florida Warm Up 2019
136 South Florida Win 10-7 1626.7 Feb 10th Florida Warm Up 2019
68 Cincinnati Win 13-12 1640.37 Feb 10th Florida Warm Up 2019
82 Texas State Win 13-10 1770.79 Mar 2nd Mardi Gras XXXII
27 LSU Loss 10-13 1449.6 Mar 2nd Mardi Gras XXXII
23 Texas Tech Loss 9-13 1412.57 Mar 2nd Mardi Gras XXXII
212 Texas Christian Win 13-1 1550.62 Mar 2nd Mardi Gras XXXII
185 Alabama-Birmingham Win 11-1 1631.96 Mar 3rd Mardi Gras XXXII
82 Texas State Loss 12-13 1317.65 Mar 3rd Mardi Gras XXXII
16 Southern California Loss 7-13 1418.62 Mar 23rd Trouble in Vegas 2019
116 Nevada-Reno Win 13-11 1522.56 Mar 23rd Trouble in Vegas 2019
16 Southern California Loss 8-13 1479.99 Mar 24th Trouble in Vegas 2019
169 Chico State Win 13-4 1684.3 Mar 24th Trouble in Vegas 2019
125 Colorado School of Mines Win 13-10 1606.47 Mar 24th Trouble in Vegas 2019
34 UCLA Win 12-10 1966.85 Mar 24th Trouble in Vegas 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)