#140 Florida Tech (6-7)

avg: 1167.47  •  sd: 97.13  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
66 Kennesaw State Loss 8-10 1195.35 Jan 27th Clutch Classic 2018
282 Wingate Win 12-9 1008.33 Jan 27th Clutch Classic 2018
272 Miami Win 13-8 1197.85 Jan 27th Clutch Classic 2018
381 Georgia Gwinnett** Win 13-2 815.23 Ignored Jan 27th Clutch Classic 2018
66 Kennesaw State Loss 7-15 858.01 Jan 28th Clutch Classic 2018
224 Georgia Southern Win 15-5 1461.24 Jan 28th Clutch Classic 2018
75 Tennessee-Chattanooga Win 9-5 1944.73 Jan 28th Clutch Classic 2018
97 Alabama Loss 11-12 1222.93 Mar 10th Tally Classic XIII
28 Carnegie Mellon Loss 9-13 1300.09 Mar 10th Tally Classic XIII
37 Central Florida Loss 9-13 1216.19 Mar 10th Tally Classic XIII
272 Miami Win 15-6 1301.69 Mar 10th Tally Classic XIII
8 Massachusetts** Loss 5-13 1363.77 Ignored Mar 10th Tally Classic XIII
224 Georgia Southern Loss 10-15 407.64 Mar 11th Tally Classic XIII
**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)