#167 Columbia (8-13)

avg: 958.25  •  sd: 72.62  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
123 Pennsylvania Loss 6-11 600.79 Feb 5th New Jersey Warmup
60 Temple Loss 7-11 968.13 Feb 10th New Jersey Warmup
126 Lehigh Loss 8-11 779.78 Feb 10th New Jersey Warmup
196 NYU Loss 9-10 716.19 Feb 10th New Jersey Warmup
60 Temple Loss 9-14 961.16 Feb 11th New Jersey Warmup
169 Rutgers Win 14-11 1264.98 Feb 11th New Jersey Warmup
123 Pennsylvania Loss 8-15 582.67 Feb 11th New Jersey Warmup
146 Yale Win 10-7 1449.72 Mar 2nd No Sleep till Brooklyn 2024
282 Hofstra Win 9-6 868.03 Mar 2nd No Sleep till Brooklyn 2024
236 MIT Win 10-5 1254.47 Mar 2nd No Sleep till Brooklyn 2024
86 Bates Win 10-8 1579.73 Mar 3rd No Sleep till Brooklyn 2024
107 Princeton Win 11-10 1333.6 Mar 3rd No Sleep till Brooklyn 2024
96 Connecticut Loss 8-10 986.73 Mar 3rd No Sleep till Brooklyn 2024
113 Syracuse Loss 8-11 823.23 Mar 3rd No Sleep till Brooklyn 2024
101 Cornell Loss 9-10 1099.57 Mar 30th East Coast Invite 2024
123 Pennsylvania Loss 5-11 547.48 Mar 30th East Coast Invite 2024
154 Harvard Loss 5-13 423.19 Mar 30th East Coast Invite 2024
146 Yale Win 13-8 1556.21 Mar 30th East Coast Invite 2024
96 Connecticut Loss 7-9 970.06 Mar 31st East Coast Invite 2024
184 George Mason Loss 8-9 751.05 Mar 31st East Coast Invite 2024
278 SUNY-Stony Brook Win 9-8 622.13 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)