#69 Broad City (4-12)

avg: 660.68  •  sd: 66.31  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
26 Virginia Rebellion Loss 7-11 980.81 Jul 13th Scuffletown Throwdown 2019
58 Agency Loss 6-12 282.42 Jul 13th Scuffletown Throwdown 2019
100 Oligarchy** Win 11-1 318.24 Ignored Jul 13th Scuffletown Throwdown 2019
99 Pickup Lines** Win 11-1 357.46 Ignored Jul 13th Scuffletown Throwdown 2019
48 Brooklyn Book Club Loss 7-10 692.52 Jul 14th Scuffletown Throwdown 2019
65 Eliza Furnace Loss 7-10 336.11 Jul 14th Scuffletown Throwdown 2019
99 Pickup Lines** Win 15-2 357.46 Ignored Jul 14th Scuffletown Throwdown 2019
41 Vice Loss 6-10 679.72 Aug 10th Chesapeake Open 2019
40 Pine Baroness Loss 9-11 959.83 Aug 10th Chesapeake Open 2019
66 Hot Metal Win 10-6 1215.8 Aug 10th Chesapeake Open 2019
48 Brooklyn Book Club Loss 8-11 716.57 Aug 11th Chesapeake Open 2019
64 Notorious C.L.E. Loss 10-11 610.54 Aug 11th Chesapeake Open 2019
66 Hot Metal Loss 8-9 594.64 Aug 11th Chesapeake Open 2019
66 Hot Metal Loss 10-12 481.51 Sep 7th Founders Womens Club Sectional Championship 2019
40 Pine Baroness Loss 4-15 609.04 Sep 7th Founders Womens Club Sectional Championship 2019
65 Eliza Furnace Loss 10-12 487.66 Sep 7th Founders Womens Club Sectional Championship 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)