#261 Cal Poly-SLO-B (8-8)

avg: 821.14  •  sd: 53.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
434 Southern California-B** Win 12-0 389.71 Ignored Feb 2nd Presidents Day Qualifiers Men
164 Arizona State Loss 8-11 737.62 Feb 2nd Presidents Day Qualifiers Men
328 Caltech Win 8-5 1018.38 Feb 2nd Presidents Day Qualifiers Men
407 California-Santa Barbara-B** Win 13-3 784.86 Ignored Feb 3rd Presidents Day Qualifiers Men
100 California-Santa Cruz Loss 8-13 862.61 Feb 3rd Presidents Day Qualifiers Men
272 Arizona State-B Loss 8-9 651.47 Feb 3rd Presidents Day Qualifiers Men
104 Portland Loss 5-13 739.16 Feb 9th Stanford Open 2019
21 California** Loss 2-13 1243.46 Ignored Feb 9th Stanford Open 2019
62 Duke** Loss 2-11 951 Ignored Feb 9th Stanford Open 2019
388 Arizona-B Win 13-9 699.36 Mar 23rd Trouble in Vegas 2019
184 California-B Loss 11-13 803.67 Mar 23rd Trouble in Vegas 2019
353 California-San Diego-B Win 12-6 1054.99 Mar 23rd Trouble in Vegas 2019
406 Colorado School of Mines - B Win 9-6 605.58 Mar 23rd Trouble in Vegas 2019
191 Montana State Loss 9-13 606.39 Mar 24th Trouble in Vegas 2019
272 Arizona State-B Win 13-10 1104.62 Mar 24th Trouble in Vegas 2019
305 Boise State Win 7-5 970.47 Mar 24th Trouble in Vegas 2019
**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)