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Edition

Pattern: Heavy Scorpid Shoulders

UncommonLeatherworking
Open in the auction house

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Price

Last scan Oct 30, 2026, 00:00 UTC

Last seen price on Ambossar (EU)

72g 51s 0c
Not listed in the last 7 days. This is the newest listing a scan recorded, not a live price.
Lowest buyout
72g 51s 0c
Listings at that scan
1
  • Regional auction data, hourly
  • 194 scans
How prices are measured
  • Pattern: Heavy Scorpid Shoulders
  • Item Level: 1
  • Leatherworking
  • Sell price: 1g 0s 0c
Sources: 1 recipe

Sources

Recipe, 1 source

  • Heavy Scorpid Shoulders

Most sources come from published game data.

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Market

Ambossar (EU)

Price History

Ambossar (EU)
Low
1g 50s 0c
Average
3,100g 88s 34c
High
100,000g 0s 0c
Samples
194
The 7-day average needs at least a week of history; pick a wider range than 1D.Show the 7-day average market price
Price history for Pattern: Heavy Scorpid Shoulders, 1 week, Ambossar (EU)
Time (UTC)Lowest buyoutAverageQuantity
Mar 1, 00401g1,595g17
Mar 2, 00401g1,450g18
Mar 3, 00400g1,255g17
Mar 4, 00400g962g18
Mar 8, 00123g550g16
Mar 9, 00123g673g17
Mar 10, 0061g220g18
Mar 11, 0061g190g16
Mar 14, 0015g236g14
Mar 18, 0014g19g10
Mar 19, 0014g19g8
Mar 23, 007g 4s11g11
Mar 27, 001g 55s1g 62s3
Mar 30, 0049g50g6
Apr 4, 0035g66g3
Apr 5, 0035g49g5
Apr 8, 0035g47g4
Apr 9, 0035g50g5
Apr 10, 0035g50g5
Apr 11, 0035g47g4
Apr 12, 0030g48g3
Apr 13, 0034g44g3
Apr 14, 0030g34g5
Apr 15, 0022g30g4
Apr 16, 0022g25g4
Apr 17, 0020g22g2
Apr 18, 0020g31,270g5
Apr 19, 0020g43,501g6
Apr 20, 0020g32,048g3
Apr 21, 00250g10,771g3
Apr 22, 00250g7,491g4
Apr 23, 00227g11,153g2
Apr 24, 00244g10,573g1
Apr 25, 0034g10,723g1
Apr 26, 0034g5,023g2
Apr 27, 001g 51s11,250g1
Apr 28, 00150g13,143g2
Apr 29, 0014,999g14,999g1
Apr 30, 0014,999g14,999g1
May 1, 00100,000g100,000g1
May 2, 002,182g46,545g2
May 3, 002,023g21,452g4
May 4, 002,000g2,023g2
May 5, 001,960g1,996g4
May 6, 001,750g1,939g5
May 7, 001,750g1,880g3
May 8, 001,000g1,695g4
May 9, 001,000g1,414g5
May 10, 001,000g1,094g2
May 11, 001,000g1,000g3
May 12, 001,000g4,266g3
May 13, 001,000g3,625g3
May 14, 001,000g1,000g4
May 15, 001,000g1,000g5
May 16, 00175g897g4
May 17, 00175g670g2
May 18, 00173g173g3
May 19, 00173g173g2
May 20, 00173g3,880g2
May 21, 00173g173g1
May 23, 00173g173g1
May 25, 00173g173g1
May 26, 00173g173g1
May 27, 00467g467g1
May 28, 00467g467g2
May 30, 001g 51s1g 51s1
May 31, 001g 51s1g 51s1
Jun 1, 00498g501g1
Jun 3, 00498g498g1
Jun 5, 00498g498g1
Jun 8, 001,000g1,000g1
Jun 9, 001,000g1,000g1
Jun 11, 001,000g1,000g1
Jun 12, 001,000g3,800g3
Jun 13, 001,000g4,536g2
Jun 14, 001,143g1,143g3
Jun 17, 001,121g5,747g2
Jun 18, 001,045g3,704g2
Jun 19, 001,045g4,333g3
Jun 20, 00964g991g3
Jun 21, 00964g964g2
Jun 22, 00964g6,979g2
Jun 23, 00964g4,473g2
Jun 24, 00964g964g2
Jun 25, 00964g964g1
Jun 26, 00964g2,969g2
Jun 27, 00964g4,473g3
Jun 28, 00964g4,203g2
Jun 29, 00964g964g2
Jun 30, 00964g964g1
Jul 1, 00964g964g2
Jul 2, 00964g5,350g3
Jul 3, 00964g7,982g1
Jul 4, 00964g964g1
Jul 5, 0015,000g15,000g1
Jul 6, 001,000g4,818g1
Jul 7, 001,000g1,000g3
Jul 8, 001,000g1,000g2
Jul 9, 001,000g1,000g1
Jul 10, 001,000g1,000g1
Jul 11, 001,000g3,799g2
Jul 12, 001,000g2,473g3
Jul 13, 001,000g1,000g2
Jul 14, 00999g999g2
Jul 15, 001g 50s667g2
Jul 16, 001,000g1,000g1
Jul 17, 001,000g1,000g2
Jul 18, 00750g945g5
Jul 19, 00457g1,718g4
Jul 20, 00400g2,481g3
Jul 21, 00350g414g3
Jul 22, 00350g375g2
Jul 23, 00350g752g2
Jul 24, 001,466g1,466g2
Jul 25, 001,466g1,466g2
Jul 29, 001,487g1,487g1
Jul 30, 001,487g1,720g1
Jul 31, 001,952g1,952g1
Aug 1, 002,166g2,166g1
Aug 2, 002,010g2,082g1
Aug 3, 001,995g1,995g1
Aug 4, 002,064g2,064g2
Aug 5, 001,099g1,760g3
Aug 6, 001,099g1,220g3
Aug 7, 001,099g1,099g3
Aug 8, 001,022g1,078g4
Aug 9, 001,022g1,044g4
Aug 10, 001,022g1,022g4
Aug 11, 00739g939g3
Aug 12, 00739g739g2
Aug 13, 00739g739g2
Aug 14, 00739g739g2
Aug 15, 00739g739g2
Aug 16, 00739g739g1
Aug 17, 00195g195g1
Aug 18, 00195g195g1
Aug 19, 00195g1,106g1
Aug 20, 001,652g1,652g1
Aug 21, 001,652g1,652g2
Aug 24, 00195g195g1
Aug 25, 00195g195g1
Aug 28, 00135g3,368g1
Aug 29, 00135g1,078g2
Aug 30, 00135g135g2
Aug 31, 00135g135g3
Sep 1, 00135g158g3
Sep 2, 00864g864g2
Sep 3, 00864g868g1
Sep 4, 00927g927g1
Sep 5, 00927g927g1
Sep 6, 00452g452g3
Sep 7, 00452g452g3
Sep 8, 00442g451g3
Sep 9, 00442g446g2
Sep 10, 0072g421g3
Sep 11, 0072g276g4
Sep 12, 0072g211g3
Sep 13, 0072g72g2
Sep 14, 0072g1,487g1
Sep 15, 003,001g4,349g2
Sep 16, 0087g3,866g2
Sep 17, 0087g87g1
Sep 18, 0087g87g2
Sep 19, 0087g87g2
Sep 20, 0087g87g3
Sep 21, 0087g87g2
Sep 22, 0087g87g2
Sep 23, 0087g87g2
Sep 24, 0087g87g2
Sep 27, 00411g411g1
Sep 28, 00378g401g2
Sep 29, 00378g395g2
Sep 30, 00378g384g2
Oct 1, 00378g378g3
Oct 2, 00378g378g3
Oct 3, 00378g378g3
Oct 4, 00378g378g3
Oct 5, 00172g306g2
Oct 6, 00172g172g1
Oct 7, 00172g172g1
Oct 8, 00172g172g1
Oct 9, 00172g172g1
Oct 10, 0056g150g2
Oct 11, 001g 51s159g2
Oct 13, 00172g172g1
Oct 14, 00172g172g1
Oct 15, 00172g663g1
Oct 16, 001,481g1,481g1
Oct 17, 001,448g1,452g1
Oct 18, 001,432g1,447g1
Oct 27, 0073g73g2
Oct 28, 0073g73g2
Oct 29, 0073g73g2
Oct 30, 0073g73g1
  • Lowest buyout
  • Average
  • Quantity
  • Latest lowest price
Daily average for Pattern: Heavy Scorpid Shoulders
Day (UTC)AverageUnits listed
Oct 3072g 51s 0c1
Oct 2972g 51s 0c2
Oct 2872g 51s 0c2
Oct 2772g 51s 0c2
Oct 181,446g 79s 50c1
Oct 171,452g 46s 33c1
Oct 161,481g 39s 0c1
Oct 15662g 76s 50c1
Oct 14171g 59s 0c1
Oct 13171g 59s 0c1
Oct 11158g 50s 69c2
Oct 10150g 49s 91c2
Oct 9171g 59s 0c1
Oct 8171g 59s 0c1
Oct 7171g 59s 0c1
Oct 6171g 59s 0c1
Oct 5305g 99s 85c2
Oct 4378g 37s 0c3
Oct 3378g 37s 0c3
Oct 2378g 37s 38c3
Oct 1378g 37s 82c3
Sep 30383g 99s 91c2
Sep 29394g 53s 50c2
Sep 28400g 74s 85c2
Sep 27410g 69s 0c1
Sep 2486g 51s 0c2
Sep 2386g 51s 0c2
Sep 2286g 51s 0c2
Sep 2186g 51s 0c2
Sep 2086g 51s 0c3

Sparse data for 1 week: showing all available history.

Trading hours: the cheaper and dearer hours of the week, in UTC

Trading Hours

See when Pattern: Heavy Scorpid Shoulders tends to trade below or above its usual price, based on the last 30 days.

The grid follows the auction week in UTC.

Loading trading hours
Cheaper than usualTypicalMore expensive than usualLimited data