#129 Maryland (2-7)

avg: 776.19  •  sd: 87.6  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
69 Case Western Reserve Loss 5-14 650.46 Feb 25th Commonwealth Cup Weekend2 2023
44 Pennsylvania** Loss 2-15 884.31 Ignored Feb 25th Commonwealth Cup Weekend2 2023
99 MIT Loss 8-15 418.51 Feb 25th Commonwealth Cup Weekend2 2023
65 Carnegie Mellon Loss 5-13 673.97 Feb 26th Commonwealth Cup Weekend2 2023
56 Tennessee Loss 7-12 819.91 Feb 26th Commonwealth Cup Weekend2 2023
85 Catholic Win 6-5 1229.85 Apr 2nd Kernel Kup
134 Johns Hopkins Loss 4-5 610.89 Apr 2nd Kernel Kup
19 Yale** Loss 2-8 1186.06 Ignored Apr 2nd Kernel Kup
160 Towson Win 7-4 1005.58 Apr 2nd Kernel Kup
**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)