NIH Funding Patterns, Part 2

As a follow-up to my last post concerning NIH funding, I though I’d do a deeper dive into the funding landscape over the last decade or so.  In the last entry, I found that the total amount of money disbursed by the NIH fluctuated dramatically, reaching an apex just after the recession and regressing since.  Over that time, a more pernicious change also occurred, namely an increase in funding inequality at the level of individual grants, as measured by the Gini coefficient.

Grants are the basic unit of the NIH bureaucracy, but they are not the only level of concern.  Another interesting question bears on the individual investigators who receive those grants.  Is funding at the investigator level becoming more uneven?

PI_Gini_over_Time

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NIH Funding is Increasingly Uneven

The Glory of the NIH

When you stop and think about it, the mere fact that the United States government aggressively funds research is a little crazy.  The innovation of government-funded research is new, at least on the scale of world history.  That American taxpayers, many of them hardworking folks with little cash to spare, are willing to fork over a few dollars a year for the purpose of science is not only strange, it’s wonderful.  It speaks to the cooperative spirit that is at the core of government: if we all give a little, our combined charity can move mountains.

And make no mistake, the taxpayer-funded engine of research that is the National Institutes of Health has moved mountains.  A wonderful study came out in 2013 which showed that as funding priorities changed and new institutes of study were created, the disease(s) to which each institute was devoted to curing showed reduced mortality and morbidity.  As an example, as research into heart disease increased (via the creation of the National Heart, Lung, and Blood Institute) the death rate plummeted.  The reduction in mortality and the increase in healthy lifespan pays dividends for the country, not only at an emotional level (delaying the loss of loved ones) but also economically, by increasing the amount of time each worker can contribute their work to society.

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On Sequencing

I wrote something up at Baseball Prospectus about pitch sequencing.  This time, I scaled up the initial analysis I did before, wherein I just looked at Clayton Kershaw and Joe Saunders.  In that limited sample, I found very little evidence of non-random sequencing for 2-pitch sequences.

For Baseball Prospectus, I examined all of the starting pitchers (>100 IP) using a similar approach, but applied to 3-pitch sequences (specifically, the three pitches which start an at-bat).  I found these longer sequences to be much less random than 2-pitch sequences, and that variation in the level of randomness correlated with some elements of pitcher skill (see the article for more details).  The results are largely uncertain at this early stage, but point in any case towards sequencing being important for pitchers (which was probably obvious before).

Many caveats apply, this analysis still being very young, so I won’t try to claim that I’ve solved sequencing or somesuch.  However, I do think that, as with my earlier work using entropy, we can apply some cool tricks from information theory to figuring out how pitchers harness variation in their endless quest to confuse, befuddle, and out-think the batter.