#99 MIT (4-6)

avg: 983.32  •  sd: 68.48  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
69 Case Western Reserve Loss 10-15 796.85 Feb 25th Commonwealth Cup Weekend2 2023
129 Maryland Win 15-8 1340.99 Feb 25th Commonwealth Cup Weekend2 2023
44 Pennsylvania Loss 3-15 884.31 Feb 25th Commonwealth Cup Weekend2 2023
47 Florida Loss 10-13 1139.75 Feb 26th Commonwealth Cup Weekend2 2023
95 Temple Win 9-8 1154.11 Feb 26th Commonwealth Cup Weekend2 2023
36 Brown Loss 0-11 980.07 Apr 1st Northeast Classic2
63 Haverford/Bryn Mawr Loss 3-9 676.27 Apr 1st Northeast Classic2
146 SUNY-Geneseo Win 13-3 1250.15 Apr 1st Northeast Classic2
76 Bates Loss 5-8 727.76 Apr 2nd Northeast Classic2
133 New Hampshire Win 7-6 873.55 Apr 2nd Northeast Classic2
**Blowout Eligible


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)