#108 Columbia (10-8)

avg: 1219.19  •  sd: 58.26  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
113 Lehigh Win 11-10 1329.24 Feb 8th NJ Warmup 2025
192 Princeton Win 11-7 1321.3 Feb 8th NJ Warmup 2025
132 Rutgers Win 11-8 1482.16 Feb 8th NJ Warmup 2025
120 Connecticut Loss 8-13 682.86 Feb 8th NJ Warmup 2025
122 Boston University Win 10-7 1557.45 Mar 1st UMass Invite 2025
73 Williams Loss 9-10 1260.1 Mar 1st UMass Invite 2025
170 Massachusetts -B Win 8-5 1413.46 Mar 1st UMass Invite 2025
126 Maine Loss 6-7 1013.09 Mar 1st UMass Invite 2025
115 Vermont-B Loss 8-9 1069.71 Mar 2nd UMass Invite 2025
170 Massachusetts -B Win 9-8 1084.86 Mar 2nd UMass Invite 2025
68 Wesleyan Loss 8-11 1065.7 Mar 2nd UMass Invite 2025
131 Pittsburgh-B Win 12-11 1246.3 Mar 29th East Coast Invite 2025
174 Delaware Loss 10-11 813.01 Mar 29th East Coast Invite 2025
48 Maryland Loss 10-11 1440.66 Mar 29th East Coast Invite 2025
56 Cornell Loss 8-15 959.13 Mar 29th East Coast Invite 2025
192 Princeton Win 12-9 1199.77 Mar 30th East Coast Invite 2025
157 Johns Hopkins Win 12-7 1526.35 Mar 30th East Coast Invite 2025
120 Connecticut Win 7-5 1507.17 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)