#35 Impact (11-12)

avg: 1517.09  •  sd: 65.15  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
197 Springs Mixed Greens** Win 15-4 1141.33 Ignored Jun 24th Colorado Summer Solstice 2023
101 Green Chiles Win 10-4 1644.26 Jun 24th Colorado Summer Solstice 2023
139 Karma** Win 15-4 1451.96 Ignored Jun 24th Colorado Summer Solstice 2023
21 Love Tractor Loss 7-10 1317.37 Jun 24th Colorado Summer Solstice 2023
1 shame.** Loss 6-15 1567.21 Ignored Jun 25th Colorado Summer Solstice 2023
28 Flight Club Win 13-12 1760.27 Jun 25th Colorado Summer Solstice 2023
21 Love Tractor Win 12-10 1945.15 Jun 25th Colorado Summer Solstice 2023
51 Classy Win 15-12 1684.3 Jul 8th TCT Pro Elite Challenge West 2023
15 Mischief Loss 13-14 1705.88 Jul 8th TCT Pro Elite Challenge West 2023
17 Lawless Loss 13-15 1549.01 Jul 8th TCT Pro Elite Challenge West 2023
18 Polar Bears Loss 12-13 1636.21 Jul 9th TCT Pro Elite Challenge West 2023
28 Flight Club Loss 13-14 1510.27 Jul 9th TCT Pro Elite Challenge West 2023
25 MOONDOG Win 13-11 1877.12 Jul 9th TCT Pro Elite Challenge West 2023
9 Space Force Loss 11-14 1577.36 Aug 19th TCT Elite Select Challenge 2023
15 Mischief Loss 10-11 1705.88 Aug 19th TCT Elite Select Challenge 2023
21 Love Tractor Loss 9-14 1233.16 Aug 19th TCT Elite Select Challenge 2023
13 Slow Loss 8-15 1284.78 Aug 20th TCT Elite Select Challenge 2023
20 Toro Loss 8-14 1201.45 Aug 20th TCT Elite Select Challenge 2023
42 The Chad Larson Experience Win 9-8 1606.04 Aug 20th TCT Elite Select Challenge 2023
241 PanIC** Win 15-1 741.8 Ignored Sep 9th 2023 Mixed West Plains Sectional Championship
203 Locomotion** Win 15-3 1113.55 Ignored Sep 9th 2023 Mixed West Plains Sectional Championship
225 Arms Race** Win 15-3 922.21 Ignored Sep 10th 2023 Mixed West Plains Sectional Championship
31 Kansas City United Loss 9-15 1079.67 Sep 10th 2023 Mixed West Plains Sectional Championship
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)