#289 Taco Cat (2-15)

avg: -32.91  •  sd: 71.57  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
73 Petey's Pirates** Loss 4-11 674.68 Ignored Jul 6th Motown Throwdown 2019
187 Pixel** Loss 4-11 140.96 Ignored Jul 6th Motown Throwdown 2019
175 Moonshine Loss 5-11 181.83 Jul 6th Motown Throwdown 2019
231 Buffalo Brain Freeze Loss 6-10 14.95 Jul 7th Motown Throwdown 2019
213 Mastodon** Loss 4-13 45.52 Ignored Jul 7th Motown Throwdown 2019
263 SlipStream Win 10-8 598.21 Jul 7th Motown Throwdown 2019
282 Sabers Loss 6-12 -402.36 Jul 7th Motown Throwdown 2019
156 ELevate** Loss 2-13 312.98 Ignored Aug 3rd Heavyweights 2019
279 Identity Theft Loss 8-13 -286.23 Aug 3rd Heavyweights 2019
169 Wildstyle** Loss 2-13 211.29 Ignored Aug 3rd Heavyweights 2019
226 Boomtown Pandas** Loss 4-13 -28.39 Ignored Aug 4th Heavyweights 2019
295 Fox Valley Forge Win 13-10 37.11 Aug 4th Heavyweights 2019
129 Bird** Loss 1-13 435.56 Ignored Aug 17th Cooler Classic 31
254 Robotic Snakes Loss 6-13 -195.92 Aug 17th Cooler Classic 31
182 Rocket LawnChair** Loss 2-13 169.7 Ignored Aug 17th Cooler Classic 31
226 Boomtown Pandas Loss 4-9 -28.39 Aug 18th Cooler Classic 31
228 Midwestern Mediocrity Loss 3-10 -74.06 Aug 18th Cooler Classic 31
**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)