#33 Rampage (11-10)

avg: 1163.46  •  sd: 95.19  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
20 Underground Loss 4-13 1043.54 Jun 23rd Eugene Summer Solstice 40
23 LOL Loss 7-11 1043.32 Jun 23rd Eugene Summer Solstice 40
30 Sneaky House Hippos Loss 7-12 810.2 Jun 23rd Eugene Summer Solstice 40
8 Nightlock** Loss 3-13 1320.39 Ignored Jun 23rd Eugene Summer Solstice 40
26 Elevate Win 7-6 1526.11 Jun 24th Eugene Summer Solstice 40
32 FAB Win 9-5 1719.76 Jun 24th Eugene Summer Solstice 40
- Fusion Loss 7-10 1163.85 Jun 24th Eugene Summer Solstice 40
52 Deadly Viper Assassination Squad Win 13-3 1393.57 Aug 18th Ski Town Classic 2018
72 Seattle END** Win 13-4 908.59 Ignored Aug 18th Ski Town Classic 2018
38 Jackwagon Win 12-7 1602.04 Aug 18th Ski Town Classic 2018
47 Trainwreck Win 13-4 1438.18 Aug 19th Ski Town Classic 2018
26 Elevate Loss 6-11 854.41 Aug 19th Ski Town Classic 2018
68 Seven Devils** Win 13-3 1016.4 Ignored Aug 19th Ski Town Classic 2018
46 Venom Win 10-9 998.67 Sep 8th So Cal Womens Sectional Championship 2018
69 Viva** Win 15-1 985.9 Ignored Sep 8th So Cal Womens Sectional Championship 2018
15 Wildfire Loss 7-11 1256.46 Sep 8th So Cal Womens Sectional Championship 2018
8 Nightlock Loss 8-15 1355.58 Sep 22nd Southwest Womens Regional Championship 2018
23 LOL Loss 6-14 910.22 Sep 22nd Southwest Womens Regional Championship 2018
52 Deadly Viper Assassination Squad Win 12-11 918.57 Sep 22nd Southwest Womens Regional Championship 2018
46 Venom Loss 11-12 748.67 Sep 23rd Southwest Womens Regional Championship 2018
52 Deadly Viper Assassination Squad Win 15-8 1358.38 Sep 23rd Southwest Womens Regional Championship 2018
**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)