The right package of statistics can help you stay up-to-date with your favorite teams

The right package of statistics can help you stay up-to-date with your favorite teams.

This blog post is a follow up to [my last blog post in which I cover the 2016 NBA season]( In the previous blog, we covered how to get the 2016 NBA season schedule, box scores and play-by-play data using the [sportsreference Python package]( In this post we will cover how to scrape betting odds data from []( for the playoffs and finals series of the 2015-2016 NBA season and then run some simple statistical tests to see if we can predict game outcomes.

I’ve been following the NBA for years now, and I’ve noticed that a lot of fans are getting into the nitty-gritty of basketball statistics. If you’re new to the game, you may not know the best sources of quantitative data on what’s going on in the league.

I’m here to help. Here are some great resources for keeping track of your favorite teams and players using statistical analysis: is the best resource I’ve found for looking at player statistics, both past and present. You can look up any player who has ever played in the NBA by name, and find out how well he performed over his career. The site also includes its own analysis tools which lets you compare players with each other or against the entire league.

I use Basketball-Reference to keep track of my favorite players’ careers long after they leave my team (my favorite player growing up was Chris Webber). It’s also very useful for finding stats on old players or obscure games from decades ago (like when Wilt scored 100!).

The National Basketball Association (NBA) is the world’s elite basketball competition. From October to June, 30 teams play 82 games in their own cities, before the best of them make it through to the playoffs.

It’s a busy schedule. Games are played most days, and if you can’t get to the TV or the radio, it can be hard to keep up with how your team is doing. But luckily there are APIs out there that allow developers to create tools that keep users up-to-date with scores, news and analysis as it happens.

The official NBA API

The official NBA API offers real-time access to NBA statistics, scores, and news. The NBA provides RESTful APIs for developers for free for non-commercial use, but requires an official application process before you start using them.

A blog post by Joseph Misiti gives an overview of some of its features:

“The API provides an interface into current and historical NBA statistics, allowing us to look back at decades of past performances along with detailed analysis of recent games and on-going games.”

As a die-hard basketball fan, I have been following the NBA league for the past decade. This year, I decided to switch things up a bit and started looking into other stats of the game in addition to points, rebounds and assists.

I was looking for a solution that was simple and relatively easy to understand. As you can probably tell from this post, I’m not an expert at statistics. But I am a pretty good basketball player (I think).

Anyway, while searching around on the internet, I discovered this website which allows you to search for any player in the NBA and get their stats on the fly. All you have to do is enter their last name and you’re golden!

The first thing that struck me about this site is that there are no ads on it at all! The second thing is how easy it is to use. Just type in one word and it does all the work for you. The third thing is how accurate it is with its predictions. After all, if they were wrong then why would anyone use them? So far I haven’t found anything wrong with their predictions!

I decided that this would be my go-to website when checking out new players or teams in the league because they have such an extensive database of

The National Basketball Association is a North American professional sports league founded in 1946 and considered to be the top basketball league in the world. It currently has 30 teams, with 29 of them coming from the United States and one from Canada. The NBA playoffs are a best-of-seven elimination tournament among 16 teams in the league. Eight teams from each conference advance to the playoffs based on regular season records. The final round of the tournament is known as the NBA Finals, and pits the winner of the Eastern Conference against the winner of the Western Conference in a best-of-seven series. The team that wins four games in that series wins their conference’s championship and gets to play for an NBA championship.

As an avid fan of the NBA, I always have been interested in statistics related to my favorite teams and players. I have found several websites that provide this information at different levels of detail, but I never have found one that provides all of it in one place. So I decided to build my own website which collects this data and displays it on one page for easy reference.

The NBA’s top statistical resource for journalists and bloggers just got better.

The Play Index is an invaluable tool for basketball fans and reporters who want to dig deeper than the usual box score. The Play Index allows you to research historical data, check trends or compare players using a variety of statistics such as points per game, rebounding or assists.

Now that same tool is available in the form of an app.

Basketball Reference iPhone App

We know the NBA is an efficient league, but we’re not going to discuss that here. Instead, this post is about how we can use the statistics that are available through to assess efficiency in other ways. is a goldmine of information that has only been recently unlocked. Until the start of the 2014-2015 season, the data on was static and could only be accessed by visiting each page individually and downloading a PDF or Excel file. It was a hassle, but some people like me still used it because it was often more complete than other sources (like Basketball Reference). But now has made all their data available through an API! If you don’t know what an API is, don’t worry about it for now – all you need to know is that we can access this data programmatically and quickly collect as much information as we want.

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