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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.
Always expect to find differentiated content on NBAstuffer.com: - Daily score predictions of every single game,
- Articles and a blog written on NBA basketball research,
- Power rankings chart made of possession based team efficiency data,
- The impact of rest days on teams' efficiency ,
- Position by position detailed player matchups with injury reports,
- Top 100 players based on player efficiency ratings (PER),
- Referee stats with total points scored, home team winning pct., ejections, fouls and technicals called,
- Basketball analytics glossary with team and player metrics,
- A bunch of links to the quality sources around the web,
- Downloads for NBA stats and raw data , articles, score prediction archives.
Philosophy:
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.
With basketball teams and players also need some intangibles as well as technical strengths, NBAstuffer.com's prediction model incorporates several factors which can be summed up under 3 main headings: - Physical Factors:
- Player matchups
- Previous meetings
- Rest days
- Mental Factors:
- Motivation to win
- Coaching
- External Factors:
- Home court factor
- Referee behaviour
Prediction model does not include overtime possibility.
Statistics are like bikinis, they are nice to look at but they don't tell you the whole story.
Like Brent Barry said, stats always look good before the game time. If you don't watch basketball games, it's really tough to figure out what really happened on the court. By looking at the boxscores, you try to comment individual and team performances. A boxscore is not enough itself to make the game understandable. I think efficiency statistics are being missed bigtime 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 me with any questions or feedbacks you may have.
Enjoy NBAstuffer.com,
Best wishes,
Serhat Ugur
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Last Updated ( Friday, 04 July 2008 )
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