What Costs More in 2011?

This is a nice representation of Consumer Price Index data from the outstanding FlowingData.com:

What costs more in 2011?

Charting the change in prices from March 2010 to March 2011, transportation and education prices went up the most, while communication and apparel dropped a bit.  A nice feature of this infographic is that it includes inflation as a benchmark; it’s easy to see here that even though food prices increased, their increase was consistent with inflation overall.

The creator of this infographic wonders why the government itself doesn’t do what FlowingData.com does:  namely, why doesn’t the Bureau of Labor Statistics create simple, easy-to-understand graphics like this with its data, rather than just publishing a text file full of numbers every month?

That’s a good question, and a good opportunity to get students involved!  Making data easier to understand means making data more useful, so take a look at the Bureau of Labor Statistics (http://www.bls.gov/), the Center for Disease Control (http://www.cdc.gov/), or some other government agency.  Grab some public data, create some visual representations, and make the data understandable!  And use FlowingData’s great work here, and elsewhere, as a guide.

Math and Science Education: State-by-State Rankings

This report from the American Institute of Physics ranks U.S. states by their proficiency in Math and Science education:

https://www.aip.org/press_release/state_outcomes_math_science_education_reveal_big_disparities.html

The study uses student performance in physics and calculus courses (measured by various standardized exams) as well as teacher certification requirements to rate each state.

Massachusetts comes in first, with New York placing a respectable fifth.  Mississippi is dead last by a wide margin.

I originally came upon this story in the Huffington Post, and readers posted some interesting responses.  One comment compared and contrasted these rankings with the average math SAT scores for each state.  And another person remarked how closely these state rankings in math and science education align with state voter preference!

Analysis of NBA Finances

This is a comprehensive and insightful look into the NBA’s claims of financial distress from Nate Silver:

https://fivethirtyeight.blogs.nytimes.com/2011/07/05/calling-foul-on-n-b-a-s-claims-of-financial-distress/

As the NBA prepares to battle the player’s union over revenue, the league has made several public claims about how they have been losing money for years.

Silver takes a deep look into those claims.  He crunches the numbers and compares player revenue as a share of league revenue across the four major sports leagues; he looks at salary growth relative to league growth; and he also discusses some of the dubious accounting tricks teams and leagues use to make profits disappear!

As usual from Nate Silver, this is a very interesting and readable application of mathematics and statistics.  His conclusion is summed up best by a recent message from @fivethirtyeight on Twitter:  “If David Stern really thinks the NBA lost $370 million last season, shouldn’t he have fired himself?”

NBA Draft Math: Evaluating Team Success

After developing a simple metric for evaluating the success of NBA draft selections, I used that metric to investigate talent dispersion in the draft and then to compare the strength of various “draft classes”.  As a third application of this metric, I will now analyze the success each NBA teamin making their draft picks.

I am using the total number of minutes played in the first two years of a player’s career as the basic quantification of draft pick value (the reasoning for this is explained in detail in NBA Draft Math, Part I).

In order to rate the success of a team, I looked at how each team’s draft pick performed relative to the average player chosen at that draft position for the NBA drafts between 2000 and 2009.  I then computed the percentage difference between that team’s choice and the average player at that pick.  The team’s overall rating is then the average of the percent differences for every draft pick.

The chart below summarizes the analysis for the Philadelphia 76ers.

As you can see, all but one of the 76ers draft choices performed better than the average player selected at that draft position.  Overall, draft picks selected by Philadelphia performed about 34% better than average; they topped the list in this ranking.

This chart displays all NBA teams whose picks performed better than average.  In addition to the 76ers, teams that performed notably well by this measure were the San Antonio Spurs, the Houston Rockets, and (surprisingly?) the New York Knicks.

The teams that performed worst in the analysis were the Boston Celtics, the Portland Trailblazers, and the Charlotte Bobcats.

Some interesting results!  The basic limitations of this metric have been addressed in NBA Draft Math, Part I, but this simple approach has opened up a lot of opportunities for analysis, and naturally, improvement.

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