#114 Cal Poly-Humboldt (7-4)

avg: 762.08  •  sd: 95.79  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
134 Oregon -B Win 12-11 714.25 Jan 25th Pac Con 2025
128 Oregon State-B Win 11-10 763.74 Jan 25th Pac Con 2025
173 Portland State Win 15-7 689.06 Jan 25th Pac Con 2025
110 Simon Fraser Win 15-10 1270.1 Jan 26th Pac Con 2025
1 Oregon** Loss 1-15 1400.35 Ignored Jan 26th Pac Con 2025
47 Western Washington Loss 1-15 730.3 Jan 26th Pac Con 2025
102 San Jose State Loss 3-13 263.84 Feb 8th Stanford Open Mens
156 California-B Win 13-1 956.51 Feb 8th Stanford Open Mens
90 San Diego State Loss 5-9 430.24 Feb 9th Stanford Open Mens
116 California-Santa Cruz-B Win 9-8 870.44 Feb 9th Stanford Open Mens
136 Nevada-Reno Win 11-8 930.19 Feb 9th Stanford Open Mens
**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)