Winning picks from SDQL masters.
S p o r t s D a t a b a s e . c o m
agile access to sports data


name
password


MLB Trends SDB Home    MLB Trends    MLB Query
Include trends from SDB's sample ml against SDB's sample ml on SDB's sample ou against SDB's sample ou on guest's web all active on filter on
Trends from SDB's sample ou against,SDB's sample ou against
$ ROI Margin wins losses % link
3668 12.1 -0.0 109 155 41.3 The Pirates are 109-155-12 AGAINST since Jun 13, 2017
2646 16.1 -0.0 56 87 39.2 The Pirates are 56-87-7 AGAINST since Jun 21, 2017 as a dog
1701 14.7 0.1 40 60 40.0 The Pirates are 40-60-5 AGAINST since Jun 21, 2017 as a road dog
1045 20.8 -0.4 16 28 36.4 The Pirates are 16-28-2 AGAINST since Jun 17, 2017 as a home dog
940 29.3 -1.0 9 19 32.1 The Pirates are 9-19-1 AGAINST since Aug 28, 2018 on the road
930 36.7 -2.0 6 16 27.3 The Pirates are 6-16-1 AGAINST since Aug 14, 2018 as a favorite
730 34.5 -1.9 5 13 27.8 The Pirates are 5-13-1 AGAINST since Aug 18, 2018 as a home favorite
200 47.6 -2.5 1 3 25.0 The Pirates are 1-3 AGAINST since Aug 14, 2018 as a road favorite
200 91.7 -2.2 0 2 0.0 The Pirates are 0-2 AGAINST since Apr 19, 2019 at home

Trend Parameters: active, english, invested, losses, margin, profit, pushes, sdql, start, team, wins


How To Use the Trends Page:
Use the Pythonic Query Language to explore a database of trends. The full PyQL format is: parameters @ conditions. More typical use just specifies the condition and takes a default output.

To see all trends with an average margin of at least 2 use the PyQL condition: margin > 2.

To see all perfect trends use the PyQL: wins * losses = 0
e-mail links:   Content@SportsDataBase.com    Support@SportsDataBase.com   
Content for this site is generated using the Sports Data Query Language (SDQL).