NBA Schedule/Log

QUICK SDQL OVERVIEW
SDQL stands for Sports Data Query Language. It is a new
language that allows the investigation of past sports results
over the internet using your home computer. It is easy, it is
fast and it is free. If you can perform a search on Google, you can
query the past results of professional sports games.
Like the Google search, there is a text query box in which you
enter what you would like to search. Unlike Google, the search has
to be specific and you must use the Sports Data Query Language.
The advantage of the SDQL is that you get one hit, which is exactly
what you asked for – not a billion hits, most of which are not what
you are looking for.
For example, if you want to see the results for all road favorites in
the NBA since 1995-96 (the start of the database), simply enter:
AF query
into the query text box and then click on the query button. It is as
simple as that! There are SDQL query text boxes at many internet
sites. The most developed is currently at:
killersports.com/nfl.py/query
To see all the NBA games in which a team committed at least
twenty-five turnovers and won the game, enter
TO>=25 and W query
That’s it!
The SDQL allows access to billions of situations that are of interest
to sports historians, the sports media, fantasy league participants
and serious sports bettors.
The ability to quickly and efficiently interrogate historical data in
the NBA (as well as the NFL, NHL, MLB and NCAA football
and basketball) will provide the SDQL user a terrific advantage
over those that just pore over box scores and read other people’s
interpretations of the results.
Perhaps the best way to grasp the SDQL is to simply try the hundreds
of examples in the trends section of book using query page.
Basically, there are only a couple of key ideas that will get you well
on your way to becoming an SDQL master.
The first is that a query consists of a number of conditions separated
by the word “and.” The second is grasping the difference between
the team and the opponent. In sports, there are two combatants.
To distinguish between them, SDQL calls one of these the team
and the other the opponent. This allows access to results based on
both the performance of the team and the performance of their
opponent. For example, we can see how a team performs when
they score at least 100 points and we can see how a team performs
when their opponent scores at least 100 points.
For example, to see how the Heat perform in games in which they
scored at least 100 points, use:
team = Heat and points>=100 query
When this query is run, the computer responds with a records
summary and a game listing of all the games since 1995 in which
the Heat scored at least 100 points.
To see how the Heat perform in games in which they allowed at
least 100 points, use:
team = Heat and o:points>=100 query
The o: prefix on the “points” directs the “points” parameter to the
opponent.
To see how the Heat perform in games AFTER they scored at least
100 points, use:
team = Heat and p:points>=100 query
Here, the p: prefix on the “points” directs the parameter to the
team’s previous game.
Each one of these queries has two SDQL phrases. The first defines
the team and the second gives a condition. There is no limit to
the number of SDQL phrases that can be strung together with
the word “and.”
That’s it. This is the basic structure of the SDQL. This structure
will allow the thorough interrogation and investigation of historical
sports data. Understanding this structure is the key to understanding
the SDQL. Once you have a grasp of this structure, you will be
able to perform your own investigations.
Start by trying the many examples in this book. If you have any
questions about the SDQL, address them to the sportsdatabase.
com discussion group at:
http://groups.google.com/group/SportsDataBase
This group is monitored by numerous SDQL masters who
will be able to be able to address all your well-posed questions.
Happy Hunting!
killersports.com/nba.py/query
2011–2012 NBA Schedule Log | 17