#11 Rival (17-15)

avg: 1804.18  •  sd: 77.21  •  top 16/20: 97.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
15 Wildfire Win 13-11 1952.19 Jul 7th TCT Pro Elite Challenge 2018
14 Showdown Win 13-7 2288.66 Jul 7th TCT Pro Elite Challenge 2018
4 Seattle Riot Loss 8-13 1767.62 Jul 7th TCT Pro Elite Challenge 2018
12 Traffic Loss 10-11 1644.07 Jul 8th TCT Pro Elite Challenge 2018
16 Heist Loss 10-11 1597.08 Jul 8th TCT Pro Elite Challenge 2018
9 Schwa Win 13-9 2256.53 Jul 8th TCT Pro Elite Challenge 2018
3 Molly Brown Loss 10-12 2035.45 Jul 8th TCT Pro Elite Challenge 2018
7 Ozone Loss 5-15 1336.53 Aug 3rd 2018 US Open Club Championships
19 Pop Loss 7-15 1079.32 Aug 3rd 2018 US Open Club Championships
5 Scandal Loss 8-12 1661.94 Aug 3rd 2018 US Open Club Championships
1 Brute Squad Loss 9-15 1947.19 Aug 4th 2018 US Open Club Championships
10 Nemesis Loss 9-13 1405.92 Aug 4th 2018 US Open Club Championships
21 Siege Win 15-10 2053.24 Aug 4th 2018 US Open Club Championships
19 Pop Win 14-8 2215.35 Aug 5th 2018 US Open Club Championships
24 Wicked Win 13-11 1721.01 Aug 18th TCT Elite Select Challenge 2018
48 Portland Ivy** Win 13-1 1432.97 Ignored Aug 18th TCT Elite Select Challenge 2018
6 6ixers Win 13-11 2245.19 Aug 18th TCT Elite Select Challenge 2018
16 Heist Win 13-6 2322.08 Aug 19th TCT Elite Select Challenge 2018
18 Phoenix Loss 11-13 1468.83 Aug 19th TCT Elite Select Challenge 2018
57 Helix** Win 13-1 1272.41 Ignored Sep 22nd Great Lakes Womens Regional Championship 2018
75 Autonomous** Win 13-0 735.98 Ignored Sep 22nd Great Lakes Womens Regional Championship 2018
34 Dish Win 13-9 1579.36 Sep 22nd Great Lakes Womens Regional Championship 2018
31 Indy Rogue Win 15-8 1768.8 Sep 22nd Great Lakes Womens Regional Championship 2018
10 Nemesis Loss 14-15 1699.48 Sep 23rd Great Lakes Womens Regional Championship 2018
34 Dish** Win 15-4 1760.8 Ignored Sep 23rd Great Lakes Womens Regional Championship 2018
7 Ozone Loss 8-15 1371.73 Oct 18th USA Ultimate National Championships 2018
19 Pop Win 15-9 2194.8 Oct 18th USA Ultimate National Championships 2018
4 Seattle Riot Loss 6-15 1663.78 Oct 18th USA Ultimate National Championships 2018
12 Traffic Win 15-9 2284.55 Oct 19th USA Ultimate National Championships 2018
19 Pop Loss 14-15 1554.32 Oct 19th USA Ultimate National Championships 2018
6 6ixers Loss 12-15 1715.86 Oct 19th USA Ultimate National Championships 2018
18 Phoenix Win 15-13 1911.85 Oct 20th USA Ultimate National Championships 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)