#364 Rensselaer Polytech (1-17)

avg: 432.24  •  sd: 71.41  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
139 Army** Loss 4-10 748.35 Ignored Mar 16th Free Tournament
130 Penn State-B** Loss 5-13 779.42 Ignored Mar 16th Free Tournament
171 Scranton** Loss 3-13 638.19 Ignored Mar 16th Free Tournament
126 Towson Loss 7-12 868.58 Mar 16th Free Tournament
171 Scranton** Loss 1-15 638.19 Ignored Mar 17th Free Tournament
170 Villanova Loss 7-11 785.07 Mar 17th Free Tournament
315 Vermont-C Loss 4-13 60.56 Mar 30th Northeast Classic 2024
267 SUNY-Geneseo Loss 6-12 292.18 Mar 30th Northeast Classic 2024
318 Swarthmore Loss 8-13 159.31 Mar 30th Northeast Classic 2024
378 SUNY-Buffalo-B Loss 6-11 -245 Mar 31st Northeast Classic 2024
139 Army** Loss 5-13 748.35 Ignored Apr 13th Hudson Valley D III Mens Conferences 2024
260 Hartford Loss 9-13 491.04 Apr 13th Hudson Valley D III Mens Conferences 2024
199 Connecticut College** Loss 4-13 525.75 Ignored Apr 14th Hudson Valley D III Mens Conferences 2024
245 Skidmore Loss 4-15 378.07 Apr 14th Hudson Valley D III Mens Conferences 2024
187 College of New Jersey Loss 7-14 580.54 Apr 27th Metro East D III College Mens Regionals 2024
199 Connecticut College** Loss 1-15 525.75 Ignored Apr 27th Metro East D III College Mens Regionals 2024
252 Hamilton Loss 9-15 415.36 Apr 27th Metro East D III College Mens Regionals 2024
376 SUNY-Fredonia Win 15-8 873.7 Apr 28th Metro East D III 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)