#107 Princeton (12-7)

avg: 1208.6  •  sd: 48.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
60 Temple Loss 8-12 993.87 Feb 5th New Jersey Warmup
123 Pennsylvania Win 12-11 1272.48 Feb 10th New Jersey Warmup
113 Syracuse Win 10-9 1313.84 Feb 10th New Jersey Warmup
169 Rutgers Win 11-9 1200.85 Feb 10th New Jersey Warmup
123 Pennsylvania Win 14-13 1272.48 Feb 11th New Jersey Warmup
113 Syracuse Loss 10-11 1063.84 Feb 11th New Jersey Warmup
126 Lehigh Win 13-12 1270.39 Feb 11th New Jersey Warmup
142 Boston University Win 9-8 1193.71 Mar 2nd No Sleep till Brooklyn 2024
198 Delaware Win 9-8 959.68 Mar 2nd No Sleep till Brooklyn 2024
31 Middlebury Loss 10-11 1532.12 Mar 2nd No Sleep till Brooklyn 2024
167 Columbia Loss 10-11 833.25 Mar 3rd No Sleep till Brooklyn 2024
198 Delaware Win 11-10 959.68 Mar 3rd No Sleep till Brooklyn 2024
70 Case Western Reserve Win 12-9 1712.08 Mar 30th East Coast Invite 2024
98 Dartmouth Loss 9-10 1120.64 Mar 30th East Coast Invite 2024
184 George Mason Win 13-9 1294.61 Mar 30th East Coast Invite 2024
169 Rutgers Win 10-9 1076.64 Mar 30th East Coast Invite 2024
96 Connecticut Loss 6-7 1124.4 Mar 31st East Coast Invite 2024
101 Cornell Loss 10-11 1099.57 Mar 31st East Coast Invite 2024
98 Dartmouth Win 11-9 1494.84 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)