#313 Harvard-B (1-11)

avg: 85.19  •  sd: 83.4  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
195 Amherst** Loss 2-11 155.98 Ignored Feb 11th UMass Invite 2023
71 Connecticut College** Loss 1-10 751.59 Ignored Feb 11th UMass Invite 2023
92 Vermont-B** Loss 3-12 641.57 Ignored Feb 11th UMass Invite 2023
139 Wesleyan** Loss 2-9 414.46 Ignored Feb 11th UMass Invite 2023
165 Tufts-B** Loss 4-15 314.62 Ignored Feb 12th UMass Invite 2023
199 Northeastern-B** Loss 3-15 137.94 Ignored Feb 12th UMass Invite 2023
195 Amherst** Loss 4-13 155.98 Ignored Mar 25th New England Open1
191 Bryant** Loss 4-10 175.52 Ignored Mar 25th New England Open1
199 Northeastern-B** Loss 2-13 137.94 Ignored Mar 25th New England Open1
157 Massachusetts-Lowell** Loss 2-13 333.51 Ignored Mar 26th New England Open1
293 Connecticut-B Loss 8-9 110.77 Mar 26th New England Open1
325 Northeastern-C Win 8-7 57.95 Mar 26th New England Open1
**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)