#272 Miami (6-17)

avg: 701.69  •  sd: 68.15  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
66 Kennesaw State Loss 7-13 900.48 Jan 27th Clutch Classic 2018
140 Florida Tech Loss 8-13 671.31 Jan 27th Clutch Classic 2018
282 Wingate Win 13-8 1159.12 Jan 27th Clutch Classic 2018
381 Georgia Gwinnett Win 13-6 815.23 Jan 27th Clutch Classic 2018
244 Berry Loss 7-12 263.13 Jan 28th Clutch Classic 2018
347 Radford Loss 8-9 253.28 Feb 17th Chucktown Throwdown XV
249 North Greenville Win 10-9 894.37 Feb 17th Chucktown Throwdown XV
303 Charleston Loss 4-7 75.32 Feb 17th Chucktown Throwdown XV
122 Tennessee Loss 1-13 655.55 Feb 17th Chucktown Throwdown XV
282 Wingate Win 11-10 787.96 Feb 18th Chucktown Throwdown XV
252 Western Carolina Loss 12-13 637.3 Feb 18th Chucktown Throwdown XV
52 Harvard** Loss 5-13 936.01 Ignored Mar 10th Tally Classic XIII
120 Mississippi State Loss 11-13 1032.45 Mar 10th Tally Classic XIII
140 Florida Tech Loss 6-15 567.47 Mar 10th Tally Classic XIII
9 Georgia** Loss 2-13 1349.28 Ignored Mar 10th Tally Classic XIII
98 Clemson Loss 7-13 780.51 Mar 10th Tally Classic XIII
231 Alabama-Birmingham Win 15-11 1202.24 Mar 11th Tally Classic XIII
235 Arizona State-B Loss 8-9 677.66 Mar 24th Trouble in Vegas 2018
156 Colorado-Denver Loss 3-13 506.91 Mar 24th Trouble in Vegas 2018
53 UCLA** Loss 4-13 934.42 Ignored Mar 24th Trouble in Vegas 2018
211 Utah State Win 13-12 1032.84 Mar 24th Trouble in Vegas 2018
263 Sacramento State Loss 6-12 162.68 Mar 25th Trouble in Vegas 2018
214 California-Santa Cruz Loss 9-10 780.57 Mar 25th Trouble in Vegas 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)