#233 Brown University-B (6-4)

avg: 614.99  •  sd: 119.68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
232 Northeastern-B Loss 4-5 506.32 Mar 3rd Cherry B lossom Classic 2018
254 Columbia-B Win 8-5 808.32 Mar 3rd Cherry B lossom Classic 2018
257 Towson-B Win 5-4 421.68 Mar 4th Cherry B lossom Classic 2018
270 American-B** Win 10-0 300.68 Ignored Mar 4th Cherry B lossom Classic 2018
261 Maryland-B Win 6-3 684.78 Mar 4th Cherry B lossom Classic 2018
240 Providence Loss 4-10 -73.36 Mar 24th Huck Buddy 2018
240 Providence Win 10-4 1126.64 Mar 24th Huck Buddy 2018
186 Rhode Island Win 8-7 1074.96 Mar 24th Huck Buddy 2018
186 Rhode Island Loss 5-12 349.96 Mar 24th Huck Buddy 2018
77 Brown** Loss 4-10 1088.92 Ignored Mar 24th Huck Buddy 2018
**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)