#20 Polar Bears (12-7)

avg: 1756.06  •  sd: 59.77  •  top 16/20: 22.8%

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# Opponent Result Game Rating Status Date Event
14 Love Tractor Loss 10-13 1498.16 Jul 13th TCT Pro Elite Challenge 2019
37 Jughandle Win 12-8 2004.12 Jul 13th TCT Pro Elite Challenge 2019
3 Seattle Mixtape Loss 7-11 1616.82 Jul 13th TCT Pro Elite Challenge 2019
43 Birdfruit Win 10-6 2013.95 Jul 14th TCT Pro Elite Challenge 2019
11 Lochsa Loss 9-12 1538.38 Jul 14th TCT Pro Elite Challenge 2019
4 Slow White Loss 9-10 1890.39 Jul 14th TCT Pro Elite Challenge 2019
37 Jughandle Win 15-8 2127.77 Aug 17th TCT Elite Select Challenge 2019
32 NOISE Win 12-10 1849.92 Aug 17th TCT Elite Select Challenge 2019
4 Slow White Loss 11-15 1634.23 Aug 17th TCT Elite Select Challenge 2019
18 Columbus Cocktails Loss 7-9 1483.27 Aug 18th TCT Elite Select Challenge 2019
31 XIST Win 8-6 1926.42 Aug 18th TCT Elite Select Challenge 2019
32 NOISE Win 8-6 1912.29 Aug 18th TCT Elite Select Challenge 2019
67 American Barbecue Win 12-7 1849.93 Sep 7th Nor Cal Mixed Club Sectional Championship 2019
98 Buckwild Win 13-7 1731.86 Sep 7th Nor Cal Mixed Club Sectional Championship 2019
233 Feral Cows** Win 13-1 1090.13 Ignored Sep 7th Nor Cal Mixed Club Sectional Championship 2019
163 VU Win 11-6 1417.49 Sep 7th Nor Cal Mixed Club Sectional Championship 2019
12 Blackbird Loss 8-12 1438.57 Sep 8th Nor Cal Mixed Club Sectional Championship 2019
59 Donuts Win 10-7 1773.53 Sep 8th Nor Cal Mixed Club Sectional Championship 2019
59 Donuts Win 13-3 1983.86 Sep 8th Nor Cal Mixed Club Sectional Championship 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)