#122 Georgia College (7-4)

avg: 1023.21  •  sd: 119.63  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
284 Miami (Ohio)** Win 11-4 7.25 Ignored Mar 9th Mash Up 2019
63 New Hampshire Loss 5-8 961.27 Mar 9th Mash Up 2019
223 Elon Win 11-7 850.12 Mar 9th Mash Up 2019
170 Vermont-B Win 11-3 1335.6 Mar 10th Mash Up 2019
154 Smith Win 12-6 1436.35 Mar 10th Mash Up 2019
87 Auburn Loss 6-13 635.26 Mar 23rd College Southerns XVIII
261 Emory-B** Win 13-2 657.5 Ignored Mar 23rd College Southerns XVIII
204 Georgia Southern Win 10-4 1106.6 Mar 23rd College Southerns XVIII
46 Middlebury Loss 10-14 1139.77 Mar 24th College Southerns XVIII
207 Wisconsin-Eau Claire Win 14-3 1098.84 Mar 24th College Southerns XVIII
70 Maryland Loss 7-15 728.26 Mar 24th College Southerns XVIII
**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)