#37 Central Florida (10-15)

avg: 1634.75  •  sd: 50.53  •  top 16/20: 0.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
16 North Carolina-Wilmington Loss 7-12 1364 Jan 20th Carolina Kickoff 2018 NC Ultimate
91 Penn State Win 11-9 1623.11 Jan 20th Carolina Kickoff 2018 NC Ultimate
1 North Carolina Loss 7-13 1787.8 Jan 21st Carolina Kickoff 2018 NC Ultimate
12 North Carolina State Loss 12-13 1793.86 Jan 21st Carolina Kickoff 2018 NC Ultimate
69 Carleton College-GoP Loss 8-10 1186.79 Jan 21st Carolina Kickoff 2018 NC Ultimate
39 Northwestern Win 13-10 1956.84 Feb 16th Warm Up A Florida Affair 2018
29 Texas Win 11-9 1960.31 Feb 16th Warm Up A Florida Affair 2018
42 Connecticut Win 13-5 2195.56 Feb 16th Warm Up A Florida Affair 2018
2 Carleton College Loss 1-13 1628.2 Feb 16th Warm Up A Florida Affair 2018
45 Illinois State Win 13-7 2143.69 Feb 17th Warm Up A Florida Affair 2018
6 Brown Loss 7-13 1489.18 Feb 17th Warm Up A Florida Affair 2018
13 Wisconsin Loss 12-13 1792.12 Feb 17th Warm Up A Florida Affair 2018
30 Auburn Loss 10-13 1381.12 Feb 18th Warm Up A Florida Affair 2018
21 Texas A&M Loss 10-13 1493.92 Feb 18th Warm Up A Florida Affair 2018
97 Alabama Win 10-8 1610.59 Mar 10th Tally Classic XIII
168 South Florida Win 15-7 1663.99 Mar 10th Tally Classic XIII
28 Carnegie Mellon Loss 10-11 1593.65 Mar 10th Tally Classic XIII
140 Florida Tech Win 13-9 1586.04 Mar 10th Tally Classic XIII
8 Massachusetts Loss 9-13 1545.2 Mar 10th Tally Classic XIII
50 Notre Dame Win 15-14 1664.28 Mar 11th Tally Classic XIII
81 Florida State Loss 12-15 1108.23 Mar 11th Tally Classic XIII
7 Pittsburgh Loss 8-15 1422.65 Mar 31st Easterns 2018
16 North Carolina-Wilmington Loss 10-15 1430.91 Mar 31st Easterns 2018
65 California-Santa Barbara Win 13-11 1691.21 Mar 31st Easterns 2018
2 Carleton College Loss 11-15 1847.03 Mar 31st Easterns 2018
**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)