#229 Texas Toast (1-11)

avg: 215.14  •  sd: 84.26  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
29 Clutch** Loss 1-12 938.44 Ignored Jun 29th Texas 2 Finger Mens and Womens
154 Foxtrot Loss 3-13 161.61 Jun 29th Texas 2 Finger Mens and Womens
162 DUPlex Loss 8-13 214.02 Jun 29th Texas 2 Finger Mens and Womens
69 Riverside** Loss 5-13 606.25 Ignored Jun 29th Texas 2 Finger Mens and Womens
218 Messengers-B Win 13-11 527.8 Jun 30th Texas 2 Finger Mens and Womens
147 Glycerine Loss 6-15 179.12 Jun 30th Texas 2 Finger Mens and Womens
208 Alamode Loss 10-11 280.69 Jun 30th Texas 2 Finger Mens and Womens
76 Gamble** Loss 1-15 560.52 Ignored Jul 13th Riverside Classic 2019
91 Harvey Cats** Loss 4-15 490.81 Ignored Jul 13th Riverside Classic 2019
208 Alamode Loss 10-14 6.99 Jul 13th Riverside Classic 2019
147 Glycerine Loss 7-15 179.12 Jul 14th Riverside Classic 2019
182 E.V.I.L. Loss 8-11 200.84 Jul 14th Riverside Classic 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)