Winning picks from SDQL masters. 		S p o r t s D a t a b a s e . c o m
tools for the objective handicapper


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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 on,SDB's sample ou against,SDB's sample ou on
$ ROI Margin wins losses % link
2825 12.9 -0.0 79 114 40.9 The Orioles are 79-114-5 AGAINST since Jul 09, 2016 at home
1390 21.9 -0.3 20 36 35.7 The Orioles are 20-36-1 AGAINST since Mar 31, 2018 as a home dog
400 88.5 -3.2 0 4 0.0 The Orioles are 0-4 AGAINST since Sep 28, 2018
400 88.5 -3.2 0 4 0.0 The Orioles are 0-4 AGAINST since Sep 28, 2018 as a dog
300 45.8 -2.4 1 4 20.0 The Orioles are 1-4-1 AGAINST since May 29, 2018 as a home favorite
300 45.8 -2.4 1 4 20.0 The Orioles are 1-4-1 AGAINST since May 29, 2018 as a favorite
810 21.2 1.5 21 12 63.6 The Orioles are 21-12-1 ON since Jul 07, 2018 as a road dog
710 18.1 1.4 21 13 61.8 The Orioles are 21-13-1 ON since Jul 07, 2018 on the road
585 54.2 3.0 8 2 80.0 The Orioles are 8-2 ON since Aug 12, 2017 as a road favorite
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
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Content for this site is generated using the Sports Data Query Language (SDQL).