#298 Hofstra (3-12)

avg: 472.11  •  sd: 76.13  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
95 Massachusetts-B** Loss 3-11 822.22 Ignored Feb 11th UMass Invite 2023
154 Massachusetts-Lowell** Loss 1-13 536.83 Ignored Feb 11th UMass Invite 2023
203 Northeastern-B Loss 6-8 635.76 Feb 11th UMass Invite 2023
158 Tufts-B** Loss 3-11 518.81 Ignored Feb 11th UMass Invite 2023
195 Amherst Loss 5-14 357.9 Feb 12th UMass Invite 2023
154 Massachusetts-Lowell** Loss 5-15 536.83 Ignored Feb 12th UMass Invite 2023
176 Syracuse Loss 2-13 447.79 Mar 4th No Sleep Till Brooklyn 2023
169 NYU Loss 6-13 483.8 Mar 4th No Sleep Till Brooklyn 2023
348 Cornell-B Loss 7-8 -48.17 Mar 4th No Sleep Till Brooklyn 2023
225 Brown-B Loss 5-10 237.22 Mar 5th No Sleep Till Brooklyn 2023
348 Cornell-B Win 12-3 676.83 Mar 5th No Sleep Till Brooklyn 2023
285 Villanova Win 11-3 1149.69 Apr 1st Fuego2
307 West Chester-B Loss 8-10 141.63 Apr 1st Fuego2
224 Haverford Loss 3-13 213.68 Apr 1st Fuego2
285 Villanova Win 9-7 829.03 Apr 2nd Fuego2
**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)