#161 AC Bandits (8-17)

avg: 800.29  •  sd: 65.84  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
74 Alchemy Loss 3-15 665.2 Jun 9th Bay Area Ultimate Classic 2018
35 Classy Loss 7-13 956.18 Jun 9th Bay Area Ultimate Classic 2018
182 Sebastopol Orchard Win 11-4 1272.27 Jun 9th Bay Area Ultimate Classic 2018
67 Firefly Loss 10-13 954.46 Jun 9th Bay Area Ultimate Classic 2018
245 Hot Stix** Win 15-1 564.35 Ignored Jun 10th Bay Area Ultimate Classic 2018
131 Absolute Zero Loss 5-10 392.16 Jun 10th Bay Area Ultimate Classic 2018
170 Spoiler Alert Loss 7-12 238.01 Jun 10th Bay Area Ultimate Classic 2018
110 California Burrito Loss 9-10 942.08 Jul 21st Revolution 2018
- Happy Cows Win 12-5 931.79 Jul 21st Revolution 2018
109 Superstition Loss 8-13 576.3 Jul 21st Revolution 2018
210 VU Loss 9-10 401.65 Jul 22nd Revolution 2018
236 Delta Breeze Win 15-3 809.22 Jul 22nd Revolution 2018
- Happy Cows Win 12-5 931.79 Jul 22nd Revolution 2018
51 Cutthroat Loss 5-13 757.69 Jul 28th Truckee River Ultimate Cooldown 2018
119 Buckwild Win 10-8 1293.76 Jul 28th Truckee River Ultimate Cooldown 2018
85 Platypi Loss 3-12 590.67 Jul 28th Truckee River Ultimate Cooldown 2018
91 Argo Loss 7-11 698.06 Jul 28th Truckee River Ultimate Cooldown 2018
91 Argo Loss 11-13 936.12 Jul 29th Truckee River Ultimate Cooldown 2018
119 Buckwild Loss 10-15 577.49 Jul 29th Truckee River Ultimate Cooldown 2018
131 Absolute Zero Loss 7-13 408.53 Sep 8th Nor Cal Mixed Sectional Championship 2018
67 Firefly Loss 4-13 682.6 Sep 8th Nor Cal Mixed Sectional Championship 2018
55 American Barbecue Loss 4-13 724.28 Sep 8th Nor Cal Mixed Sectional Championship 2018
17 Polar Bears** Loss 3-13 1139.68 Ignored Sep 8th Nor Cal Mixed Sectional Championship 2018
187 Megalodon Win 13-6 1243.4 Sep 9th Nor Cal Mixed Sectional Championship 2018
193 Feral Cows Win 13-5 1215.76 Sep 9th Nor Cal Mixed Sectional Championship 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)