#17 Pennsylvania (11-9)

avg: 2124.71  •  sd: 60.93  •  top 16/20: 78%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
16 Georgia Win 14-10 2553.45 Feb 10th Queen City Tune Up 2024
12 Michigan Loss 11-15 1930.44 Feb 10th Queen City Tune Up 2024
4 North Carolina Loss 7-12 2101.9 Feb 10th Queen City Tune Up 2024
21 Ohio State Loss 10-11 1957.16 Feb 10th Queen City Tune Up 2024
56 Florida Win 13-5 2149.25 Feb 11th Queen City Tune Up 2024
37 Washington University Loss 10-11 1643.65 Feb 11th Queen City Tune Up 2024
49 Georgia Tech Win 13-4 2232.2 Feb 24th Commonwealth Cup Weekend 2 2024
47 Connecticut Win 12-5 2255.8 Feb 24th Commonwealth Cup Weekend 2 2024
70 James Madison Win 9-4 2019.57 Feb 24th Commonwealth Cup Weekend 2 2024
134 MIT** Win 13-0 1512.25 Ignored Feb 24th Commonwealth Cup Weekend 2 2024
7 Tufts Loss 8-9 2395.18 Feb 25th Commonwealth Cup Weekend 2 2024
26 North Carolina State Win 10-7 2318.21 Feb 25th Commonwealth Cup Weekend 2 2024
22 Notre Dame Loss 7-10 1665.5 Feb 25th Commonwealth Cup Weekend 2 2024
21 Ohio State Win 10-8 2344.82 Feb 25th Commonwealth Cup Weekend 2 2024
75 Columbia Win 13-6 1974.91 Mar 30th East Coast Invite 2024
32 UCLA Win 13-7 2409.41 Mar 30th East Coast Invite 2024
2 Vermont Loss 9-15 2274.14 Mar 30th East Coast Invite 2024
52 Yale Win 15-4 2194.05 Mar 30th East Coast Invite 2024
12 Michigan Loss 8-10 2048.94 Mar 31st East Coast Invite 2024
20 Northeastern Loss 8-10 1839 Mar 31st East Coast Invite 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)