#192 Princeton (3-7)

avg: 854.4  •  sd: 91.64  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
108 Columbia Loss 7-11 752.3 Feb 8th NJ Warmup 2025
113 Lehigh Loss 8-11 838.63 Feb 8th NJ Warmup 2025
132 Rutgers Win 12-11 1241.55 Feb 8th NJ Warmup 2025
102 Syracuse Win 12-10 1498.51 Feb 8th NJ Warmup 2025
88 Georgetown Loss 6-13 708.67 Mar 29th East Coast Invite 2025
222 Harvard Loss 7-8 584.9 Mar 29th East Coast Invite 2025
131 Pittsburgh-B Loss 7-13 563.77 Mar 29th East Coast Invite 2025
101 Yale Loss 3-11 662.48 Mar 29th East Coast Invite 2025
108 Columbia Loss 9-12 873.83 Mar 30th East Coast Invite 2025
222 Harvard Win 11-8 1075.51 Mar 30th East Coast Invite 2025
**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)