#72 Georgetown (5-7)

avg: 1365.7  •  sd: 79.35  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
125 Davidson Win 15-8 1711.38 Jan 27th Carolina Kickoff 2024
13 North Carolina State Loss 4-15 1346.6 Jan 27th Carolina Kickoff 2024
16 Penn State Loss 9-15 1405.75 Jan 27th Carolina Kickoff 2024
78 Carleton College-CHOP Win 14-9 1822.81 Jan 28th Carolina Kickoff 2024
235 North Carolina-B Win 15-8 1252.27 Jan 28th Carolina Kickoff 2024
91 Indiana Loss 10-11 1145.81 Jan 28th Carolina Kickoff 2024
12 Alabama-Huntsville** Loss 5-15 1393.67 Ignored Feb 10th Queen City Tune Up 2024
92 Tennessee Win 15-11 1648.27 Feb 10th Queen City Tune Up 2024
29 South Carolina Loss 12-15 1383.42 Feb 10th Queen City Tune Up 2024
48 Missouri Loss 8-13 1018.61 Feb 10th Queen City Tune Up 2024
70 Case Western Reserve Loss 6-11 820.02 Feb 11th Queen City Tune Up 2024
91 Indiana Win 14-12 1491.76 Feb 11th Queen City Tune Up 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)