So you want to bet on the NBA? Whether you’re experienced or a sports betting newbie, there are several strategies that you can use to make smart wagers. One of the most popular sports betting models involves using historical data to estimate the likelihood of an upcoming game’s outcome. This article will walk you through the steps to set up a sports betting model for the NBA using Python and Las Vegas Stats.’
Step One: Acquire NBA Data
The first step in building a sports betting model for the NBA is to acquire data. There are several platforms that can be used to gather information about NBA games, including BetOnline, which offers live odds for NBA matchups. Another popular tool for basketball bettors is SportingCharts, which compiles box score data for over a hundred sports including the NBA. You can also use Google to search for specific NBA statistics, which are then presented in a list view.
You don’t need to limit yourself to these platforms, as there are many other options available. For example, you can use HockeyDB, which offers the same betting odds as BetOnline and SportingCharts, but also allows you to build custom reports and track your favorite hockey teams’ statistical information. Yahoo Sports has a section dedicated to odds and betting news, as well as an API that can be used to access sports data, including the NBA. On top of that, DailyRoto has a large database of NFL, NCAAF, and NBA stats, which you can query using their web-based tools. Simply type in a team name and/or the opposing teams’ names in the search bar, and then click the “Search” button.
Step Two: Cleanse The Data
The next step in building a sports betting model for the NBA is to cleanse the data. This involves removing any extraneous or redundant information, which might confuse the model. For example, SportingCharts gives you the option to filter your box score data by Date, Time, Location, and Weather. These are all redundant information, which you don’t need to factor into your analysis. Another example is BetOnline offering live betting for NFL, NBA, and College Football games, allowing you to choose from six bookmakers with varying odds. While this might be convenient, it is also redundant information, which you can essentially eliminate from your analysis. This is not as easy as it sounds, especially if you are working with large data sets. You will most likely want to use Python to automate this process, so that it can be done quickly and accurately.
Step Three: Create Time-Series Tables
The third step in building a sports betting model for the NBA is to create time-series tables. These are simply data tables that track a statistic over time. They can be used to examine trends in a data set, or to predict the outcome of an event based on past performance. For example, you can use the box score data from SportingCharts to create a time-series table for HomeWinningAwayAway (HWA) matches, where Home means the team with the higher seed in the NBA playoffs, and Away means the team with the lower seed. This type of analysis can help you identify which teams are trending in what direction, and whether or not this will impact the outcome of the game.
These tables can be built manually, or using a tool like Handy Excel, which makes it easy to sort and filter data. Once you have your time-series tables ready, you can start analyzing them.
Step Four: Normalize The Data
The fourth step in building a sports betting model for the NBA is to normalize the data. This involves adjusting the data so that all of the values fall within a reasonable range. For example, BetOnline offers horse racing, college football, and NBA betting, but the odds are either very, very high or very, very low. When presented with this type of data, most people would interpret it as extremely over or under-valued. For this reason, it is important to normalize the data before you start analyzing it. One good method is to use the z-score algorithm. This algorithm assigns a normalized score to each value, based on a standard deviation calculated from the entire data set. Once you normalize the data, it is ready to be analyzed.
Step Five: Create The Model
The fifth step in building a sports betting model for the NBA is to create the model. You have several options here, depending on how much experience you have in analyzing data. If you are a beginner, it is usually best to use an offline tool like Python or R, or an online tool like MAGIC (Marketing Analytics in a Graphical Interface) or TIBCO (Tibco Software). If you are more experienced, you can opt for XML files or CSV (computer text file) formats, which can be parsed by any analytics tool that can read them. These formats make it much easier to include additional variables, like the Odds, into your model, if you deem this necessary.
Step Six: Test The Model
The final step in building a sports betting model for the NBA is to test the model. This involves applying your newly-created model to a test set, and then comparing the results to your expected outcomes. Most importantly, you need to make sure that your model performs as you intended. For example, if you built a model that compared Home win percentage to Away loss percentage for HWA matches, you would want to start by testing this model on games that you have not yet used to build the model. This will ensure that your model is not overfit to the data set you used to build it, and that it is still applicable to other data sets. Another good way to ensure the validity of your model is to cross-validate it. Simply use a different data set to test your model, and ensure that your results are consistent. Once you are satisfied with the results, it’s time to refine and adjust your model, as necessary.
At this point, you have a completed sports betting model for the NBA. You can use the information learned to make more accurate wagers, or to identify events and teams that are likely to win, place, or kick off. Finally, knowing when to stop betting is almost as important as knowing when to begin. Simply put, if you are making money off of your winnings, you are probably winning enough games that the odds are in your favor. If not, it might be a good idea to shut down your account, and reconsider your betting habits.
As you can see, it’s not as difficult as you might think to make a sports betting model for the NBA. Using Las Vegas Stats, you can set up multiple bookmakers, and then simply search for NBA teams. From there, you can export the data to Handy Excel, or bring it into the database so that you can perform complex analyses. With very little hacking, you can create a working model in no time, which will help you understand the NBA, and make better wagers on its games.