#13 Toro (12-8)

avg: 1850.48  •  sd: 74.21  •  top 16/20: 74.8%

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# Opponent Result Game Rating Status Date Event
153 Jackpot** Win 13-2 1520.99 Ignored Jun 22nd Summer Glazed Daze 2019
62 JLP Win 13-8 1847.17 Jun 22nd Summer Glazed Daze 2019
23 Rally Loss 11-13 1482.5 Jun 22nd Summer Glazed Daze 2019
15 Loco Win 13-9 2220.9 Jun 23rd Summer Glazed Daze 2019
55 Malice in Wonderland Win 13-6 2009.77 Jun 23rd Summer Glazed Daze 2019
23 Rally Win 13-10 2039.48 Jun 23rd Summer Glazed Daze 2019
16 Weird Win 15-13 2010.98 Jun 23rd Summer Glazed Daze 2019
41 BW Ultimate Win 14-6 2139.26 Aug 17th TCT Elite Select Challenge 2019
11 Lochsa Loss 11-15 1502.58 Aug 17th TCT Elite Select Challenge 2019
5 Wild Card Loss 9-13 1584.18 Aug 17th TCT Elite Select Challenge 2019
15 Loco Win 12-6 2381.65 Aug 18th TCT Elite Select Challenge 2019
14 Love Tractor Win 8-6 2126.79 Aug 18th TCT Elite Select Challenge 2019
4 Slow White Loss 7-12 1494.88 Aug 18th TCT Elite Select Challenge 2019
16 Weird Win 9-6 2215.36 Aug 18th TCT Elite Select Challenge 2019
2 AMP Win 13-11 2336.21 Aug 31st TCT Pro Championships 2019
37 Jughandle Loss 12-13 1437.97 Aug 31st TCT Pro Championships 2019
9 Snake Country Loss 8-11 1560.15 Aug 31st TCT Pro Championships 2019
8 shame. Loss 10-15 1472.99 Sep 1st TCT Pro Championships 2019
10 Space Heater Loss 10-14 1498.93 Sep 1st TCT Pro Championships 2019
31 XIST Win 15-9 2141.41 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)