#9 Wild Card (18-9)

avg: 1873.65  •  sd: 67.78  •  top 16/20: 99.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
66 The Feminists Win 15-3 1883.56 Jun 23rd Boston Invite 2018
11 Slow White Loss 11-15 1446.13 Jun 23rd Boston Invite 2018
- Battleship Win 12-11 1622.39 Jun 23rd Boston Invite 2018
30 Jughandle Win 15-8 2122.16 Jun 23rd Boston Invite 2018
33 League of Shadows Win 12-10 1774.08 Jun 24th Boston Invite 2018
- Anchor Win 15-9 2080.91 Jun 24th Boston Invite 2018
22 XIST Loss 11-12 1493.05 Jun 24th Boston Invite 2018
16 NOISE Win 12-8 2184.34 Jul 7th TCT Pro Elite Challenge 2018
26 Alloy Win 10-7 1979.02 Jul 7th TCT Pro Elite Challenge 2018
15 Toro Win 13-8 2256.8 Jul 7th TCT Pro Elite Challenge 2018
7 Blackbird Loss 8-12 1450.52 Jul 8th TCT Pro Elite Challenge 2018
29 7 Figures Win 11-9 1809.59 Jul 8th TCT Pro Elite Challenge 2018
15 Toro Loss 11-12 1635.64 Jul 8th TCT Pro Elite Challenge 2018
11 Slow White Win 14-12 2048.25 Sep 1st TCT Pro Championships 2018
24 Rally Win 15-7 2211.11 Sep 1st TCT Pro Championships 2018
2 Seattle Mixtape Loss 13-14 1896.63 Sep 1st TCT Pro Championships 2018
1 AMP Win 14-13 2278.89 Sep 2nd TCT Pro Championships 2018
20 No Touching! Win 15-8 2246.19 Sep 2nd TCT Pro Championships 2018
3 Drag'n Thrust Loss 12-15 1717.74 Sep 2nd TCT Pro Championships 2018
32 UNION Win 13-7 2098.33 Sep 22nd Northeast Mixed Regional Championship 2018
107 Sunken Circus Win 14-9 1551.39 Sep 22nd Northeast Mixed Regional Championship 2018
57 Heartless Win 15-6 1913.66 Sep 22nd Northeast Mixed Regional Championship 2018
11 Slow White Loss 13-15 1613.12 Sep 22nd Northeast Mixed Regional Championship 2018
31 Metro North Loss 12-13 1431.32 Sep 23rd Northeast Mixed Regional Championship 2018
33 League of Shadows Win 13-8 2032.11 Sep 23rd Northeast Mixed Regional Championship 2018
50 Grand Army Win 15-7 1969.68 Sep 23rd Northeast Mixed Regional Championship 2018
11 Slow White Loss 12-13 1702.3 Sep 23rd Northeast Mixed Regional 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)