#160 Wesleyan (6-6)

avg: 1112.09  •  sd: 52.7  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
195 Amherst Win 9-7 1237.24 Feb 11th UMass Invite 2023
80 Connecticut College Loss 8-10 1209.6 Feb 11th UMass Invite 2023
315 Harvard-B** Win 9-2 943.5 Ignored Feb 11th UMass Invite 2023
100 Vermont-B Loss 6-7 1268.49 Feb 11th UMass Invite 2023
80 Connecticut College Loss 8-15 907.46 Feb 12th UMass Invite 2023
203 Northeastern-B Win 13-10 1264.4 Feb 12th UMass Invite 2023
66 Bowdoin Loss 4-13 964.7 Apr 1st Fuego2
223 SUNY-Stony Brook Win 9-8 967.57 Apr 1st Fuego2
119 College of New Jersey Loss 5-9 768.19 Apr 1st Fuego2
282 New Hampshire Win 8-3 1167.47 Apr 1st Fuego2
224 Haverford Win 13-5 1413.68 Apr 2nd Fuego2
100 Vermont-B Loss 10-12 1155.37 Apr 2nd Fuego2
**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)