#236 MIT (2-8)

avg: 680.57  •  sd: 73.69  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
167 Columbia Loss 5-10 384.35 Mar 2nd No Sleep till Brooklyn 2024
282 Hofstra Win 11-10 574.47 Mar 2nd No Sleep till Brooklyn 2024
146 Yale Loss 5-12 460.05 Mar 2nd No Sleep till Brooklyn 2024
86 Bates** Loss 3-12 717.07 Ignored Mar 3rd No Sleep till Brooklyn 2024
198 Delaware Loss 9-11 585.47 Mar 3rd No Sleep till Brooklyn 2024
278 SUNY-Stony Brook Win 10-6 993.29 Mar 3rd No Sleep till Brooklyn 2024
111 SUNY-Binghamton Loss 8-11 826.11 Mar 23rd Carousel City Classic 2024
148 Rochester Loss 4-9 436.81 Mar 23rd Carousel City Classic 2024
113 Syracuse Loss 11-12 1063.84 Mar 23rd Carousel City Classic 2024
46 Williams** Loss 4-15 924.96 Ignored Mar 24th Carousel City Classic 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)