#261 Georgia Tech-B (15-11)

avg: 904.28  •  sd: 56.4  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
303 Alabama-B Win 9-7 990.45 Jan 20th Starkville Qualifiers
403 Southern Mississippi** Win 15-1 636.96 Ignored Jan 20th Starkville Qualifiers
250 Mississippi State-C Loss 8-9 825.27 Jan 20th Starkville Qualifiers
303 Alabama-B Win 13-12 836.11 Jan 21st Starkville Qualifiers
231 Harding Loss 6-9 596.74 Jan 21st Starkville Qualifiers
403 Southern Mississippi** Win 15-1 636.96 Ignored Jan 21st Starkville Qualifiers
195 Alabama-Birmingham Win 10-6 1638.42 Feb 24th Joint Summit 2024
146 Clemson Loss 7-11 851.46 Feb 24th Joint Summit 2024
346 Coastal Carolina Win 12-9 876.39 Feb 24th Joint Summit 2024
325 South Carolina-B Win 9-5 1149.98 Feb 24th Joint Summit 2024
195 Alabama-Birmingham Loss 1-7 542.26 Feb 25th Joint Summit 2024
346 Coastal Carolina Win 13-1 1131.02 Feb 25th Joint Summit 2024
183 South Florida Loss 7-11 719.63 Feb 25th Joint Summit 2024
183 South Florida Loss 6-13 586.53 Feb 25th Joint Summit 2024
78 Carleton College-CHOP** Loss 3-13 1004.23 Ignored Mar 16th Southerns 2024
304 Luther College Win 13-6 1294.97 Mar 16th Southerns 2024
94 Wisconsin-Eau Claire Loss 6-13 934.71 Mar 16th Southerns 2024
196 Charleston Loss 12-13 1008.53 Mar 17th Southerns 2024
265 Georgia College Loss 4-15 289.6 Mar 17th Southerns 2024
246 Georgia Southern Win 14-11 1283.71 Mar 17th Southerns 2024
303 Alabama-B Win 11-8 1076.72 Apr 13th Southeast Dev Mens Conferences 2024
388 Ave Maria-B** Win 15-2 826.28 Ignored Apr 13th Southeast Dev Mens Conferences 2024
383 Florida State-B Win 15-7 878.94 Apr 13th Southeast Dev Mens Conferences 2024
388 Ave Maria-B Win 15-7 826.28 Apr 14th Southeast Dev Mens Conferences 2024
248 Florida-B Loss 7-10 574.2 Apr 14th Southeast Dev Mens Conferences 2024
406 South Florida-B** Win 15-1 549.16 Ignored Apr 14th Southeast Dev Mens Conferences 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)