#124 Davidson (5-6)

avg: 931.22  •  sd: 67.42  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
76 Georgetown Loss 8-15 640.79 Jan 27th Carolina Kickoff 2024
21 North Carolina State Loss 8-15 1160.32 Jan 27th Carolina Kickoff 2024
15 Penn State Loss 7-15 1196.89 Jan 27th Carolina Kickoff 2024
74 Indiana Loss 6-14 611.97 Jan 28th Carolina Kickoff 2024
69 Carleton College-CHOP Loss 8-15 676.75 Jan 28th Carolina Kickoff 2024
145 Messiah Win 10-9 895.59 Feb 17th Commonwealth Cup Weekend 1 2024
227 South Carolina-B** Win 13-3 683.59 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
194 North Carolina State-B Win 10-5 955.36 Feb 17th Commonwealth Cup Weekend 1 2024
128 Cedarville Win 10-9 1037.6 Feb 18th Commonwealth Cup Weekend 1 2024
161 Pittsburgh-B Win 12-6 1238.22 Feb 18th Commonwealth Cup Weekend 1 2024
71 Maryland Loss 7-10 846.19 Feb 18th Commonwealth Cup Weekend 1 2024
**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)