#190 MIT (9-8)

avg: 991.57  •  sd: 73.5  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
225 Brown-B Win 8-7 936.12 Mar 4th No Sleep Till Brooklyn 2023
158 Tufts-B Win 10-9 1243.81 Mar 4th No Sleep Till Brooklyn 2023
122 Williams Loss 6-11 746.43 Mar 4th No Sleep Till Brooklyn 2023
169 NYU Win 11-5 1683.8 Mar 5th No Sleep Till Brooklyn 2023
176 Syracuse Win 11-9 1296.99 Mar 5th No Sleep Till Brooklyn 2023
122 Williams Win 10-9 1418.13 Mar 5th No Sleep Till Brooklyn 2023
227 Temple-B Loss 10-11 674.6 Mar 25th Strong Island 2023
208 Rhode Island Win 10-9 1030.27 Mar 25th Strong Island 2023
314 SUNY-Stony Brook-B** Win 13-5 952.99 Ignored Mar 25th Strong Island 2023
128 SUNY-Buffalo Loss 7-12 738.9 Mar 25th Strong Island 2023
305 SUNY-Binghamton-B Win 13-4 1031.96 Mar 26th Strong Island 2023
239 Stevens Tech Loss 7-9 482.41 Mar 26th Strong Island 2023
162 American Loss 9-12 759.05 Apr 1st Atlantic Coast Open 2023
77 Temple Loss 9-13 1061.75 Apr 1st Atlantic Coast Open 2023
67 Virginia Tech Loss 7-13 995.76 Apr 1st Atlantic Coast Open 2023
33 Duke** Loss 4-13 1190.66 Ignored Apr 1st Atlantic Coast Open 2023
369 George Mason** Win 15-4 600 Ignored Apr 2nd Atlantic Coast Open 2023
**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)