#347 Rensselaer Polytech (5-18)

avg: 461.28  •  sd: 56.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
160 Ithaca** Loss 2-13 630.7 Ignored Mar 22nd Salt City Classic
76 Williams** Loss 4-13 992.52 Ignored Mar 22nd Salt City Classic
146 SUNY-Binghamton** Loss 4-13 683.61 Ignored Mar 22nd Salt City Classic
81 Rochester** Loss 2-12 955.12 Ignored Mar 22nd Salt City Classic
160 Ithaca** Loss 6-15 630.7 Ignored Mar 23rd Salt City Classic
152 Rhode Island Loss 7-12 747.91 Mar 23rd Salt City Classic
152 Rhode Island** Loss 4-10 668.42 Ignored Mar 29th Ocean State Invite 2025
134 Maine** Loss 4-10 724.57 Ignored Mar 29th Ocean State Invite 2025
287 Roger Williams Loss 4-5 623.81 Mar 29th Ocean State Invite 2025
411 Boston University-B** Win 10-2 389.91 Ignored Mar 30th Ocean State Invite 2025
247 Connecticut College Loss 6-11 342.86 Mar 30th Ocean State Invite 2025
387 Providence Win 13-6 792.91 Mar 30th Ocean State Invite 2025
247 Connecticut College Loss 3-11 289.56 Apr 12th Hudson Valley D III Mens Conferences 2025
192 Vassar Loss 6-11 546.37 Apr 12th Hudson Valley D III Mens Conferences 2025
420 Union (New York)** Win 13-5 -4.31 Ignored Apr 12th Hudson Valley D III Mens Conferences 2025
311 Hartford Loss 4-10 31.65 Apr 13th Hudson Valley D III Mens Conferences 2025
415 New Haven** Win 14-6 258.52 Ignored Apr 13th Hudson Valley D III Mens Conferences 2025
415 New Haven** Win 15-5 258.52 Ignored Apr 13th Hudson Valley D III Mens Conferences 2025
235 Skidmore Loss 5-14 327 Apr 13th Hudson Valley D III Mens Conferences 2025
244 College of New Jersey Loss 5-12 302.61 Apr 26th Metro East D III College Mens Regionals 2025
192 Vassar Loss 4-8 528.26 Apr 26th Metro East D III College Mens Regionals 2025
235 Skidmore Loss 7-8 802 Apr 26th Metro East D III College Mens Regionals 2025
244 College of New Jersey Loss 5-14 302.61 Apr 27th Metro East D III College Mens Regionals 2025
**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)