#36 Rampage (9-6)

avg: 1225.19  •  sd: 90.04  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 Cold Cuts** Win 15-2 836.9 Ignored Jul 13th TCT Select Flight Invite West 2019
67 Jackwagon Win 13-8 1206.75 Jul 13th TCT Select Flight Invite West 2019
10 Traffic Loss 7-15 1337.23 Jul 14th TCT Select Flight Invite West 2019
38 FAB Win 13-8 1711.92 Jul 14th TCT Select Flight Invite West 2019
35 Seattle Soul Loss 9-11 1008.78 Jul 14th TCT Select Flight Invite West 2019
40 Pine Baroness Loss 6-11 662.34 Jul 14th TCT Select Flight Invite West 2019
88 Ultraviolet** Win 13-1 828.26 Ignored Aug 24th Ski Town Classic 2019
56 Venom Win 12-7 1392.28 Aug 24th Ski Town Classic 2019
51 Fiasco Win 12-4 1623.87 Aug 24th Ski Town Classic 2019
67 Jackwagon Win 13-8 1206.75 Aug 24th Ski Town Classic 2019
47 Crush City Win 13-5 1685.87 Aug 25th Ski Town Classic 2019
35 Seattle Soul Loss 10-11 1132.99 Aug 25th Ski Town Classic 2019
56 Venom Loss 9-12 526.4 Sep 7th So Cal Womens Club Sectional Championship 2019
75 Viva** Win 15-2 1161.06 Ignored Sep 7th So Cal Womens Club Sectional Championship 2019
14 Wildfire** Loss 6-15 1270.04 Ignored Sep 7th So Cal 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)