#79 Brunch Club (14-7)

avg: 1149.12  •  sd: 64.47  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
237 Rampage Win 13-6 803.33 Jul 8th Summer Glazed Daze 2023
177 District Cocktails Win 12-9 994.98 Jul 8th Summer Glazed Daze 2023
248 Pickles** Win 13-4 626.54 Ignored Jul 8th Summer Glazed Daze 2023
124 Magnanimouse Win 11-9 1210.79 Jul 8th Summer Glazed Daze 2023
237 Rampage** Win 15-5 803.33 Ignored Aug 12th HoDown Showdown 2023
52 Roma Ultima Loss 7-15 754.1 Aug 12th HoDown Showdown 2023
69 Too Much Fun Loss 10-12 979.16 Aug 12th HoDown Showdown 2023
154 Moontower Win 11-8 1160.21 Aug 12th HoDown Showdown 2023
208 Piedmont United Win 14-8 1006.01 Aug 13th HoDown Showdown 2023
98 FlyTrap Loss 12-13 928.4 Aug 13th HoDown Showdown 2023
208 Piedmont United** Win 13-4 1069.97 Ignored Sep 9th 2023 Mixed North Carolina Sectional Championship
22 Storm Loss 9-13 1270.78 Sep 9th 2023 Mixed North Carolina Sectional Championship
124 Magnanimouse Win 12-10 1199.71 Sep 9th 2023 Mixed North Carolina Sectional Championship
69 Too Much Fun Win 11-6 1763.97 Sep 9th 2023 Mixed North Carolina Sectional Championship
108 Bear Jordan Win 13-8 1523.25 Sep 10th 2023 Mixed North Carolina Sectional Championship
69 Too Much Fun Win 15-11 1598.44 Sep 10th 2023 Mixed North Carolina Sectional Championship
12 'Shine Loss 6-13 1251.08 Sep 23rd 2023 Southeast Mixed Regional Championship
89 B-Unit Loss 11-15 721.53 Sep 23rd 2023 Southeast Mixed Regional Championship
108 Bear Jordan Win 13-12 1152.09 Sep 23rd 2023 Southeast Mixed Regional Championship
61 Malice in Wonderland Win 10-9 1419.25 Sep 23rd 2023 Southeast Mixed Regional Championship
61 Malice in Wonderland Loss 7-15 694.25 Sep 24th 2023 Southeast Mixed Regional Championship
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)