#350 SUNY-Buffalo-B (2-9)

avg: 25.44  •  sd: 78.89  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
228 Akron** Loss 4-13 119.45 Mar 9th Spring Spook 2024
149 Miami (Ohio)** Loss 5-13 435.96 Ignored Mar 9th Spring Spook 2024
303 Wright State Loss 6-13 -261.53 Mar 9th Spring Spook 2024
248 Carthage Loss 6-10 134.63 Mar 10th Spring Spook 2024
304 Kent State Loss 6-12 -247.67 Mar 10th Spring Spook 2024
233 Skidmore** Loss 5-12 104.52 Ignored Mar 30th Northeast Classic 2024
213 SUNY-Albany** Loss 4-13 166.14 Ignored Mar 30th Northeast Classic 2024
357 SUNY-Albany-B Win 13-12 108.16 Mar 30th Northeast Classic 2024
352 Rensselaer Polytech Win 11-6 563.6 Mar 31st Northeast Classic 2024
321 SUNY-Binghamton-B Loss 7-12 -264.82 Mar 31st Northeast Classic 2024
247 SUNY-Geneseo** Loss 4-11 37.5 Ignored Mar 31st Northeast Classic 2024
**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)