#203 Shrimp Discs (5-13)

avg: 652.03  •  sd: 65.59  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
9 Doublewide** Loss 2-13 1506.81 Ignored Jun 24th Texas 2 Finger 2023
68 Brawl** Loss 1-13 818.55 Ignored Jun 24th Texas 2 Finger 2023
218 Supercell Win 10-8 795.5 Jun 24th Texas 2 Finger 2023
77 BARNSTORM Loss 7-13 817.22 Jun 25th Texas 2 Finger 2023
243 Bazooka 2: The Zookening Win 13-6 834.53 Jun 25th Texas 2 Finger 2023
125 Cowtown Cannons Loss 12-13 931.61 Jun 25th Texas 2 Finger 2023
165 Firefly TX Loss 10-11 733.17 Aug 5th Dog Days of Summer 23
50 H.I.P** Loss 3-13 953.51 Ignored Aug 5th Dog Days of Summer 23
69 Clutch** Loss 5-13 810.39 Ignored Aug 5th Dog Days of Summer 23
254 Adventure Time Win 11-5 566.76 Aug 6th Dog Days of Summer 23
225 Forge Win 15-13 702.42 Aug 6th Dog Days of Summer 23
102 Harvey Cats Loss 5-13 607.19 Aug 6th Dog Days of Summer 23
68 Brawl** Loss 4-13 818.55 Ignored Sep 9th 2023 Mens Texas Sectional Championship
129 Foxtrot Loss 6-13 442.89 Sep 9th 2023 Mens Texas Sectional Championship
50 H.I.P** Loss 2-13 953.51 Ignored Sep 9th 2023 Mens Texas Sectional Championship
153 Sprawl Loss 11-13 670.03 Sep 9th 2023 Mens Texas Sectional Championship
222 RGV Tlacuaches Win 15-12 807.26 Sep 10th 2023 Mens Texas Sectional Championship
165 Firefly TX Loss 5-15 258.17 Sep 10th 2023 Mens Texas 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)