#149 Cal Poly-SLO-B (12-6)

avg: 1155.09  •  sd: 82.24  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
333 California-San Diego-C** Win 12-3 840.47 Ignored Jan 21st Presidents Day Qualifier
105 California-Davis Win 13-11 1573.73 Jan 21st Presidents Day Qualifier
312 San Diego State-B** Win 13-3 956.12 Ignored Jan 21st Presidents Day Qualifier
243 California-Santa Barbara-B Win 9-5 1281.29 Jan 22nd Presidents Day Qualifier
120 California-Irvine Loss 9-10 1170.52 Jan 22nd Presidents Day Qualifier
105 California-Davis Win 11-9 1594.09 Jan 22nd Presidents Day Qualifier
342 Santa Clara-B** Win 13-3 764.22 Ignored Feb 4th Stanford Open
138 Occidental Loss 7-10 808.61 Feb 4th Stanford Open
105 California-Davis Loss 5-8 891.28 Feb 4th Stanford Open
113 Claremont Win 8-5 1766.02 Feb 5th Stanford Open
180 Nevada-Reno Win 11-7 1496.9 Feb 5th Stanford Open
111 Washington-B Win 10-8 1581.23 Feb 5th Stanford Open
113 Claremont Loss 9-11 1063.21 Apr 1st Southwest Showdown
221 California-B Loss 10-11 729.61 Apr 1st Southwest Showdown
230 Cal State-Long Beach Win 10-9 916.9 Apr 1st Southwest Showdown
179 Loyola Marymount Loss 8-10 770.06 Apr 2nd Southwest Showdown
349 Grand Canyon-B** Win 13-5 662.64 Ignored Apr 2nd Southwest Showdown
230 Cal State-Long Beach Win 13-8 1288.06 Apr 2nd Southwest Showdown
**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)