NBAstuffer.com is designed for all NBA fans who are looking for unique quantitative analyses and in-depth statistics. With battling conventional NBA wisdom, the research articles, charts and graphical indicators will help you get a better understanding about the NBA basketball facts. Readers also can find research articles and helpful blog posts that aren't based only on statistics, but give a more intelligent viewpoint on professional basketball.
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Like Aaron Levenstein said, stats always look good before the game time.
What they reveal is suggestive, but what they conceal is vital. If you don't watch basketball games, it's really tough to figure out what really happened on the court. By looking at the box scores, you try to comment individual and team performances. A box score is not enough itself to get a understanding of the game. In the meantime, I think that efficiency statistics are being missed big time by most of the popular NBA resources. Possession based stats have to be accepted as a fundamental tool which helps us get closed to understand the "whole story" most. My major goal with developing NBAstuffer.com is to help basketball lovers find differentiated stuff about all sorts of modern basketball's quantitative analysis.
As I consider myself "a basketball engineer", every detail of basketball interests me and I like to go deep and research everything that I can do with my data. I'm open to any ideas, comments or criticism. Please feel free to contact with any questions or feedbacks you may have. If you have specific NBA related statistical or research needs, just drop a line to discuss what we can do together.
In a ball game; one team can attack (offense) as much as opponent team allows (defense). Throw away "points per game" and "points allowed per game". It is a common mistake to use them when it comes to assess quality of offenses and defenses. Points per possession (efficiency) method takes into account points scored, field goal percentage, turnovers, offensive rebounds, and free throw percentage that can justifiably be looked at in measuring offensive or defensive quality.
The predictive model uses regression analysis to determine coefficients for the factors that might have impact on game outcomes. As the model continuously improves itself by employing some optimization techniques, it only focuses on predicting the nearest final scores individually for road team and home team. This is why some criterias such as margin of victory, accuracy of picking the winner team should not be primarily considered when evaluating the success. An assigned grade for both road team and home team does a better job of classifying the results by taking points differential between predicted and actual score.
Certain game factors such as injuries to key players, player trades, coaching changes are not taken into account by the algorithm of the model. It takes four weeks after each NBA season begins for the prediction model to pull real season data for making an assessment of the performance level of each team. That's why the December 1st is the date that the score predictions starts.
Last Updated ( Tuesday, 25 November 2014 )