#142 Boston University (5-8)

avg: 1068.71  •  sd: 73.06  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
224 American Win 11-8 1097.36 Jan 27th Mid Atlantic Warm Up
68 James Madison Loss 7-11 910 Jan 27th Mid Atlantic Warm Up
73 Richmond Win 9-8 1489.26 Jan 27th Mid Atlantic Warm Up
156 Johns Hopkins Win 9-8 1144.03 Jan 27th Mid Atlantic Warm Up
111 SUNY-Binghamton Win 11-8 1557.33 Jan 28th Mid Atlantic Warm Up
70 Case Western Reserve Loss 5-13 766.71 Jan 28th Mid Atlantic Warm Up
61 William & Mary Loss 6-13 832.01 Jan 28th Mid Atlantic Warm Up
113 Syracuse Loss 8-10 926.17 Mar 2nd No Sleep till Brooklyn 2024
96 Connecticut Loss 8-10 986.73 Mar 2nd No Sleep till Brooklyn 2024
107 Princeton Loss 8-9 1083.6 Mar 2nd No Sleep till Brooklyn 2024
196 NYU Win 13-2 1441.19 Mar 3rd No Sleep till Brooklyn 2024
113 Syracuse Loss 8-13 692.68 Mar 3rd No Sleep till Brooklyn 2024
31 Middlebury Loss 3-13 1057.12 Mar 3rd No Sleep till Brooklyn 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)