#11 Johnny Bravo (13-5)

avg: 1898.7  •  sd: 61.35  •  top 16/20: 93.3%

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# Opponent Result Game Rating Status Date Event
90 Choice City Hops** Win 13-4 1703.6 Ignored Jun 22nd Fort Collins Summer Solstice 2019
39 Inception Win 12-11 1539.7 Jun 22nd Fort Collins Summer Solstice 2019
138 Colorado Cutthroat: Youth Club U-20 Boys** Win 13-3 1441.08 Ignored Jun 22nd Fort Collins Summer Solstice 2019
105 Johnny Walker Win 13-7 1582.42 Jun 22nd Fort Collins Summer Solstice 2019
31 Johnny Encore Win 15-10 1977.17 Jun 23rd Fort Collins Summer Solstice 2019
32 Prairie Fire Win 15-8 2078.91 Jun 23rd Fort Collins Summer Solstice 2019
18 Patrol Win 13-9 2156.89 Jul 13th TCT Pro Elite Challenge 2019
9 SoCal Condors Loss 13-14 1845.39 Jul 13th TCT Pro Elite Challenge 2019
3 PoNY Loss 8-13 1671.85 Jul 13th TCT Pro Elite Challenge 2019
15 Rhino Slam! Loss 9-13 1434.98 Jul 14th TCT Pro Elite Challenge 2019
6 Sub Zero Loss 8-13 1567.09 Jul 14th TCT Pro Elite Challenge 2019
19 Voodoo Win 11-10 1860.56 Jul 14th TCT Pro Elite Challenge 2019
33 Freaks Win 15-8 2064.4 Aug 17th TCT Elite Select Challenge 2019
30 Black Market I Win 15-8 2100.19 Aug 17th TCT Elite Select Challenge 2019
10 DiG Win 14-13 2092.04 Aug 17th TCT Elite Select Challenge 2019
16 Chain Lightning Win 10-9 1961.97 Aug 18th TCT Elite Select Challenge 2019
12 Pittsburgh Temper Win 11-8 2255.46 Aug 18th TCT Elite Select Challenge 2019
9 SoCal Condors Loss 10-11 1845.39 Aug 18th TCT Elite Select Challenge 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)