#364 SUNY-Buffalo-B (0-9)

avg: -434.09  •  sd: 211.83  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
319 Akron** Loss 3-13 -284.8 Mar 4th Huckin in the Hills IX
139 Dayton** Loss 0-13 598.09 Ignored Mar 4th Huckin in the Hills IX
272 Ohio** Loss 2-13 19.98 Ignored Mar 4th Huckin in the Hills IX
181 West Virginia** Loss 1-13 414.57 Ignored Mar 4th Huckin in the Hills IX
355 Case Western Reserve-B Loss 5-14 -648.02 Mar 5th Huckin in the Hills IX
327 Kent State Loss 8-12 -159.73 Mar 5th Huckin in the Hills IX
355 Case Western Reserve-B Loss 3-8 -648.02 Apr 1st 2023 B team Brodown
130 Messiah** Loss 0-13 652.29 Ignored Apr 1st 2023 B team Brodown
261 Ohio State** Loss 2-11 88.78 Ignored Apr 1st 2023 B team Brodown
**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)