#118 MIT (9-6)

avg: 1287.73  •  sd: 59.49  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
252 SUNY-Cortland Win 13-2 1445.28 Mar 9th Atlantic City 9
122 Yale Loss 10-13 951.38 Mar 9th Atlantic City 9
335 College of New Jersey** Win 13-3 1141.21 Ignored Mar 9th Atlantic City 9
77 Colby Loss 9-12 1127.33 Mar 9th Atlantic City 9
372 Rutgers-B** Win 13-3 962.59 Ignored Mar 10th Atlantic City 9
153 SUNY-Albany Win 13-9 1569.58 Mar 10th Atlantic City 9
77 Colby Loss 7-11 1005.8 Mar 10th Atlantic City 9
122 Yale Win 9-7 1558.86 Mar 10th Atlantic City 9
345 American Win 13-7 1059.34 Mar 30th Atlantic Coast Open 2019
151 SUNY-Binghamton Win 13-10 1490.29 Mar 30th Atlantic Coast Open 2019
91 Mary Washington Loss 7-9 1103.17 Mar 30th Atlantic Coast Open 2019
101 Connecticut Win 13-11 1585.08 Mar 30th Atlantic Coast Open 2019
33 Johns Hopkins Loss 7-15 1131.17 Mar 31st Atlantic Coast Open 2019
137 North Carolina-B Win 13-11 1461.99 Mar 31st Atlantic Coast Open 2019
62 Duke Loss 11-14 1237.67 Mar 31st Atlantic Coast Open 2019
**Blowout Eligible

FAQ

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
  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
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)