#283 Hofstra (7-16)

avg: 809.27  •  sd: 60.16  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
163 Columbia Loss 6-9 856.86 Mar 2nd No Sleep till Brooklyn 2024
201 MIT Loss 10-11 991.71 Mar 2nd No Sleep till Brooklyn 2024
131 Yale Loss 0-6 779.37 Mar 2nd No Sleep till Brooklyn 2024
300 SUNY-Stony Brook Loss 7-10 325.16 Mar 3rd No Sleep till Brooklyn 2024
191 NYU Loss 3-13 556.8 Mar 3rd No Sleep till Brooklyn 2024
397 SUNY-Albany-B** Win 11-3 692.79 Ignored Mar 30th Northeast Classic 2024
218 Middlebury-B Loss 6-11 508.01 Mar 30th Northeast Classic 2024
245 Skidmore Loss 8-10 715.41 Mar 30th Northeast Classic 2024
327 SUNY-Binghamton-B Win 13-2 1214.21 Mar 31st Northeast Classic 2024
267 SUNY-Geneseo Loss 8-9 746.49 Mar 31st Northeast Classic 2024
163 Columbia Loss 3-12 675.42 Apr 13th Metro NY D I Mens Conferences 2024
93 Princeton Loss 8-11 1172.42 Apr 13th Metro NY D I Mens Conferences 2024
191 NYU Loss 10-11 1031.8 Apr 13th Metro NY D I Mens Conferences 2024
300 SUNY-Stony Brook Win 10-6 1210.99 Apr 13th Metro NY D I Mens Conferences 2024
374 New Jersey Tech Win 15-6 923.7 Apr 14th Metro NY D I Mens Conferences 2024
272 Rowan Loss 7-15 256.78 Apr 14th Metro NY D I Mens Conferences 2024
300 SUNY-Stony Brook Win 11-8 1080.44 Apr 14th Metro NY D I Mens Conferences 2024
85 Cornell** Loss 3-15 975.01 Ignored Apr 27th Metro East D I College Mens Regionals 2024
103 SUNY-Binghamton** Loss 5-15 891.64 Ignored Apr 27th Metro East D I College Mens Regionals 2024
162 Rutgers Loss 5-15 682.55 Apr 27th Metro East D I College Mens Regionals 2024
275 Central Connecticut State Win 14-13 973.66 Apr 28th Metro East D I College Mens Regionals 2024
272 Rowan Loss 4-15 256.78 Apr 28th Metro East D I College Mens Regionals 2024
327 SUNY-Binghamton-B Win 6-1 1214.21 Apr 28th Metro East D I College Mens Regionals 2024
**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)