#247 SUNY-Geneseo (6-7)

avg: 637.5  •  sd: 52.55  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
120 Army Loss 4-9 560.57 Feb 24th Bring The Huckus 2024
207 Colby Loss 9-10 670.22 Feb 24th Bring The Huckus 2024
197 Haverford Win 9-7 1115.52 Feb 24th Bring The Huckus 2024
100 Vermont-B Loss 4-13 635.55 Feb 24th Bring The Huckus 2024
197 Haverford Loss 12-13 711.18 Feb 25th Bring The Huckus 2024
277 Stevens Tech Loss 12-14 276.49 Feb 25th Bring The Huckus 2024
352 Rensselaer Polytech Win 12-6 596.21 Mar 30th Northeast Classic 2024
153 Rhode Island Loss 5-11 426.41 Mar 30th Northeast Classic 2024
302 Vermont-C Win 13-9 765.44 Mar 30th Northeast Classic 2024
282 Hofstra Win 9-8 574.47 Mar 31st Northeast Classic 2024
240 Middlebury-B Win 7-6 796.72 Mar 31st Northeast Classic 2024
233 Skidmore Loss 8-9 579.52 Mar 31st Northeast Classic 2024
350 SUNY-Buffalo-B** Win 11-4 625.44 Ignored Mar 31st Northeast Classic 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)