#349 Cal Poly-Humboldt (2-13)

avg: 27.1  •  sd: 125  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
63 Western Washington** Loss 5-15 822.23 Ignored Jan 27th Trouble in Corvegas
356 Oregon State-B Win 15-8 556.31 Jan 27th Trouble in Corvegas
339 Portland State Loss 10-12 -132.84 Jan 27th Trouble in Corvegas
18 Oregon State** Loss 2-15 1269.3 Ignored Jan 27th Trouble in Corvegas
168 Washington State Loss 7-15 353.98 Jan 28th Trouble in Corvegas
356 Oregon State-B Win 12-10 229.62 Jan 28th Trouble in Corvegas
297 Occidental Loss 8-13 -130.17 Feb 3rd Stanford Open 2024
151 Cal Poly-SLO-B** Loss 4-13 433.94 Ignored Feb 3rd Stanford Open 2024
202 California-B Loss 6-11 279.83 Feb 3rd Stanford Open 2024
129 San Jose State** Loss 4-12 526.45 Ignored Mar 9th Silicon Valley Rally 2024
194 California-Davis** Loss 2-13 248.06 Ignored Mar 9th Silicon Valley Rally 2024
202 California-B** Loss 4-13 226.52 Ignored Mar 9th Silicon Valley Rally 2024
307 Chico State Loss 3-13 -282.04 Mar 10th Silicon Valley Rally 2024
194 California-Davis** Loss 1-13 248.06 Ignored Mar 10th Silicon Valley Rally 2024
323 California-Santa Cruz-B Loss 4-13 -360.51 Mar 10th Silicon Valley Rally 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)