#199 Claremont (5-4)

avg: 996.34  •  sd: 77.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
- Sacramento State Win 13-9 969.18 Feb 9th Stanford Open 2019
- Oregon-B Win 10-8 1024.25 Feb 9th Stanford Open 2019
78 Carleton College-GoP Loss 6-12 878.41 Feb 9th Stanford Open 2019
75 Air Force Loss 1-7 877.54 Feb 10th Stanford Open 2019
62 Duke Loss 4-8 986.2 Feb 10th Stanford Open 2019
254 Cal Poly-Pomona Win 13-6 1440.19 Mar 9th 2019 SoCal Mixer
254 Cal Poly-Pomona Win 11-7 1307.08 Mar 9th 2019 SoCal Mixer
353 California-San Diego-B Win 12-10 713.8 Mar 9th 2019 SoCal Mixer
216 Occidental Loss 11-13 698.5 Mar 9th 2019 SoCal Mixer
**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)