A Solid Database Situational Query For College Basketball Wagering

Wednesday, February 27, 2019 4:49 PM UTC

Wednesday, Feb. 27, 2019 4:49 PM UTC

I have created more than 1,100 algorithms in the past year, and when working on them via the Python code, I'm inclined to provide insight for new bettors in this age of legalized sports gambling.

These abstracts will also be helpful to the new student of database queries utilizing SQL, Python, MYSQL technologies and others. Lastly, in future articles, I will discuss machine learning and deep learning, which are simply two subsets of artificial intelligence.

A good money-making query must be consistent over time with minimal loss streaks. I spent 18 years on Wall Street and learned many ways of grading a trader’s performance. One of the common metrics was maximum drawdown as a percentage of capital managed, which means: What was the worst overall stretch of losses? Return on investment (ROI) is an extremely important key performance measure for any query, just as it is on Wall Street.

So, to begin, we will look at a simple query with a few specific parameters that has produced solid returns over the last five seasons. Keep in mind, past performance is no guarantee of future profits and should never be relied upon. However, making an educated well-informed decision to adopt the discipline that specific queries provide will certainly enhance the probabilities of making money in sports wagering.

The Situational Query

This query measures a team and its opponent's most recent extremes and exploits the human wagering behavior that results from those recent trends.

A query and algorithm are essentially the same things, but the algorithm sounds so much more complex and "smart" than a query. An informal definition could be "a set of rules that precisely defines a sequence of operations," which would include all computer programs, including programs that do not perform numeric calculations. Generally, a program is only an algorithm if it stops eventually. Computer scientists may classify database-management systems according to the database models that they support.

Relational databases are perhaps the most common and had their origins in the mid-1980s. Relational databases model data as rows and columns in a series of tables, and the vast majority use Microsoft’s SQL for writing and querying data. So, a query is also a set list of parameters and qualifications that return summary results. The use of Pivot tables in Excel is an example of a query.

The query we will examine in this abstract has three rules:

1. Play on any team.

2. After being beaten by the spread by 36 or more points total in their last five games.

3. Facing an opponent after going "under" the total by 54 or more points total in their last 10 games.

The pythonic code would look something like this:

tS(ats margin,N=5)=2015

The results of this query show 65.8% against the spread (ATS) winners on a 73-38-3 record. The third number 3, located after the 38 losses, are games that tied against the spread, commonly called pushes.

The following spreadsheet shows all qualifying games for the 2018-19 season. The column headers are expanded to show the layman wording for greater understanding. ATS margin is by how many points did the team cover the spread (positive number) or fail to cover the spread (negative number). Same for the over/under margin column header with negative results equal to a winning "under" bet and positive numbers equal to a winning "over" bet.

So, for the first installment of this topic, I will stop here. Note that on the spreadsheet, you have games beginning Valentine's Day night through Saturday that qualify. So, my suggestion is simple. If you are new to sports betting, just track these plays. If you are an experienced sports gambler, then consider making disciplined wagers based on these opportunities.

Tracking for beginning bettors is by far the best and safest way to learn. Always bet with your head and not over it.

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