#31 XIST (9-16)

avg: 1625.92  •  sd: 63.04  •  top 16/20: 1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
2 AMP Loss 9-12 1762 Jun 22nd Boston Invite 2019
151 Buffalo Lake Effect** Win 13-3 1523.09 Ignored Jun 22nd Boston Invite 2019
5 Wild Card Loss 6-10 1506.58 Jun 22nd Boston Invite 2019
81 The Feminists Win 13-6 1852.72 Jun 22nd Boston Invite 2019
35 League of Shadows Win 13-7 2120.93 Jun 23rd Boston Invite 2019
35 League of Shadows Win 12-5 2163.4 Jun 23rd Boston Invite 2019
68 Metro North Win 11-9 1564.45 Jun 23rd Boston Invite 2019
43 Birdfruit Loss 9-12 1172.43 Jul 13th TCT Pro Elite Challenge 2019
12 Blackbird Loss 6-13 1279.73 Jul 13th TCT Pro Elite Challenge 2019
18 Columbus Cocktails Loss 9-13 1344.04 Jul 13th TCT Pro Elite Challenge 2019
11 Lochsa Loss 9-12 1538.38 Jul 14th TCT Pro Elite Challenge 2019
37 Jughandle Win 10-9 1687.97 Jul 14th TCT Pro Elite Challenge 2019
30 No Touching! Loss 10-11 1515.73 Jul 14th TCT Pro Elite Challenge 2019
14 Love Tractor Win 13-9 2244.87 Aug 17th TCT Elite Select Challenge 2019
16 Weird Loss 8-12 1355.64 Aug 17th TCT Elite Select Challenge 2019
30 No Touching! Win 15-7 2240.73 Aug 17th TCT Elite Select Challenge 2019
11 Lochsa Loss 5-8 1430.14 Aug 18th TCT Elite Select Challenge 2019
14 Love Tractor Loss 9-10 1701.3 Aug 18th TCT Elite Select Challenge 2019
20 Polar Bears Loss 6-8 1455.56 Aug 18th TCT Elite Select Challenge 2019
14 Love Tractor Loss 10-14 1427.6 Aug 31st TCT Pro Championships 2019
4 Slow White Loss 9-12 1670.03 Aug 31st TCT Pro Championships 2019
10 Space Heater Win 14-13 2022.63 Aug 31st TCT Pro Championships 2019
1 Drag'n Thrust Loss 8-15 1631.55 Sep 1st TCT Pro Championships 2019
37 Jughandle Loss 9-13 1144.4 Sep 1st TCT Pro Championships 2019
13 Toro Loss 9-15 1335 Sep 1st TCT Pro Championships 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)