A few weeks ago, I posted some thoughts about statistical analysis and their importance to fans of sports, as well as it’s application. One of my favorite writers about the NBA is David Friedman, whose blog, 20 Second Timeout, has some of the best comprehensive analysis and opinion out there on the Internet. Here are a couple of articles regarding statistical analysis, and it’s application to basketball.
After the jump I’ve posted a couple of quotes for preview and I highly suggest reading the linked articles. Enjoy.
"Stats gurus" plainly do not want to discuss or consider the fact that some of their most precious numbers--the raw data that they plug into their formulas, stats like assists, steals, blocked shots and turnovers--are subjectively recorded. During last season's playoffs, I did a detailed post demonstrating that Chris Paul's supposedly record setting playoff assist totals were in fact inflated by generous scorekeeping. Shouldn't that be of interest to the "stats gurus"? Isn't that claim something that they seriously need to investigate on their own to either confirm or reject? I provided very specific information so that anyone could watch a tape of the game and find the exact plays that I described and thus judge for themselves whether or not each of those assists should have been awarded. Yet I see no indication that the "stats gurus" are the slightest bit concerned about the fact that a lot of their basic data is seriously flawed. A lot of these guys spent a good portion of the season pumping up Chris Paul as the MVP and it is highly likely that they did so on the basis of bogus assist numbers. Based on a skill-set evaluation of Paul's game, I consider him to be the best point guard in the NBA and a top five MVP candidate but that is not the point; the point is that if you are basing your whole analysis of the NBA purely on numbers and some of the basic numbers you are using are not right then your whole analysis is bogus. If a real scientist finds out that the raw data he has gathered is flawed then he understands that he has to gather new, accurate data. Unfortunately, many of the basketball "stats gurus" are not scientists; they are "mad scientists" at best.
In an insightful post titled Using statistics in basketball: the bar is higher, Rosenbaum writes, "Statistical analysis can play a critical role in basketball decision-making, but it can also be misleading if the complexities of the game of basketball (and the statistical issues generated by those complexities) are not well understood. In other words, the bar is higher for statistical analysis in basketball than it is in baseball. Ultimately this will greatly benefit the teams that incorporate skilled statistical analysts in the right way, because the greater complexities in basketball will mean that it will be harder for other teams to ever catch up with the first teams that get this right. It will be fascinating seeing how this all plays out over the next few years."
In The Difference Between Measuring Defense in Basketball and Baseball , I made the important point that basketball statistical analysis is pseudoscience because its practitioners do not base their research on the scientific method:
1. Ask a Question
2. Do Background Research
3. Construct a Hypothesis
4. Test Your Hypothesis by Doing an Experiment
5. Analyze Your Data and Draw a Conclusion
6. Communicate Your Results