#250 Georgia Tech-B (10-10)

avg: 627.86  •  sd: 63.66  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
268 Alabama-B Win 9-7 800.28 Jan 20th Starkville Qualifiers
368 Southern Mississippi** Win 15-1 438.74 Ignored Jan 20th Starkville Qualifiers
223 Mississippi State-C Loss 8-9 608.41 Jan 20th Starkville Qualifiers
268 Alabama-B Win 13-12 645.94 Jan 21st Starkville Qualifiers
192 Harding Loss 6-9 432.54 Jan 21st Starkville Qualifiers
368 Southern Mississippi** Win 15-1 438.74 Ignored Jan 21st Starkville Qualifiers
201 Alabama-Birmingham Win 10-6 1323.03 Feb 24th Joint Summit 2024
173 Clemson Loss 7-11 472.23 Feb 24th Joint Summit 2024
324 Coastal Carolina Win 12-9 571.52 Feb 24th Joint Summit 2024
296 South Carolina-B Win 9-5 898.18 Feb 24th Joint Summit 2024
201 Alabama-Birmingham Loss 1-7 226.87 Feb 25th Joint Summit 2024
324 Coastal Carolina Win 13-1 826.15 Feb 25th Joint Summit 2024
185 South Florida Loss 7-11 399.41 Feb 25th Joint Summit 2024
185 South Florida Loss 6-13 266.3 Feb 25th Joint Summit 2024
78 Carleton College-CHOP** Loss 3-13 748.94 Ignored Mar 16th Southerns 2024
322 Luther Win 13-6 848.06 Mar 16th Southerns 2024
95 Wisconsin-Eau Claire Loss 6-13 650.03 Mar 16th Southerns 2024
210 Charleston Loss 12-13 651.87 Mar 17th Southerns 2024
245 Georgia College Loss 4-15 47.36 Mar 17th Southerns 2024
244 Georgia Southern Win 14-11 963.88 Mar 17th Southerns 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)