Information you need to win
S p o r t s D a t a b a s e . c o m
tools    for    the    objective    handicapper


name
password


NHL Trends SDB Home    NHL Trends    NHL Query
Include trends from SDB's sample ml against SDB's sample ml on SDB's sample ou against SDB's sample ou on SDB's sample su 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
590 42.1 -0.8 4 9 30.8 The Sharks are 4-9-1 AGAINST since Oct 09, 2010 as a home dog
2610 31.1 0.7 51 30 63.0 The Sharks are 51-30-3 ON since Feb 18, 2018
2060 26.8 0.6 46 30 60.5 The Sharks are 46-30-1 ON since Dec 07, 2017 as a favorite
1750 30.7 0.6 35 21 62.5 The Sharks are 35-21-1 ON since Dec 07, 2017 at home
1310 24.7 0.5 31 21 59.6 The Sharks are 31-21-1 ON since Dec 07, 2017 as a home favorite
1230 33.2 1.2 23 13 63.9 The Sharks are 23-13-1 ON since Mar 14, 2018 on the road
1000 90.9 2.3 10 1 90.9 The Sharks are 10-1 ON since Apr 26, 2018 as a dog
890 89.0 2.5 9 1 90.0 The Sharks are 9-1 ON since Apr 26, 2018 as a road dog
760 29.2 1.0 16 10 61.5 The Sharks are 16-10 ON since Nov 24, 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
e-mail links:   Content@SportsDataBase.com    Support@SportsDataBase.com   
Content for this site is generated using the Sports Data Query Language (SDQL).