#98 Family Style (9-11)

avg: 1118.32  •  sd: 73.65  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
97 California Burrito Loss 5-13 520.63 Jul 20th Revolution 2019
61 American Barbecue Win 10-9 1423.36 Jul 20th Revolution 2019
48 Pivot Loss 10-11 1315.51 Jul 20th Revolution 2019
64 Donuts Loss 5-13 672.77 Jul 20th Revolution 2019
119 Bulleit Train Win 9-8 1178.77 Jul 21st Revolution 2019
104 Argo Loss 10-11 976.84 Jul 21st Revolution 2019
96 Robot Loss 7-11 653.83 Jul 21st Revolution 2019
69 Instant Karma Loss 8-10 979.87 Aug 24th Ski Town Classic 2019
59 Flight Club Loss 9-11 1061.36 Aug 24th Ski Town Classic 2019
188 The Strangers Win 11-5 1273.76 Aug 24th Ski Town Classic 2019
99 Ouzel Win 11-9 1367.4 Aug 24th Ski Town Classic 2019
135 Springs Mixed Ulty Team Loss 10-13 625.65 Aug 25th Ski Town Classic 2019
66 Firefly Win 13-8 1753.45 Aug 25th Ski Town Classic 2019
195 Birds of Paradise Win 13-4 1248.76 Sep 7th So Cal Mixed Club Sectional Championship 2019
96 Robot Loss 9-11 871.52 Sep 7th So Cal Mixed Club Sectional Championship 2019
30 Lotus Win 11-10 1742.38 Sep 7th So Cal Mixed Club Sectional Championship 2019
243 Rogue** Win 12-1 1010.15 Ignored Sep 7th So Cal Mixed Club Sectional Championship 2019
97 California Burrito Loss 8-9 995.63 Sep 8th So Cal Mixed Club Sectional Championship 2019
48 Pivot Loss 7-12 920 Sep 8th So Cal Mixed Club Sectional Championship 2019
142 Superstition Win 12-8 1338.96 Sep 8th So Cal Mixed 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)