Tag: visualization

Military Capabilities: A Revionist Metric

 Phil Arena has been playing around with alternative measures of military power. He begins with the straightforward observation that one current and popular measure of military power, the CINC scores in the Correlates of War project, list the United States as having fallen behind the People’s Republic of China in its military capability. As Phil writes, this is not a conclusion that most, if any, observers of world politics would endorse–and that even if it is true in the broadest sense (that in some total war between the United States and the PRC, the United States might not be able to conquer China) that it is not particularly useful.

Phil lays out the broader points of his critique–namely, that CINC overweights raw materials and does not adjust for quality of militaries–in his post. Using a new measure based on COW data, computed as he describes at his link, he proposes an alternate measure–one I think that the Duck’s readership should at least be aware of. This measure may get closer to the notion of who has the most usable military power at any given point in time. I’ve redone his graphics slightly but the data is all his.

In the first chart, we see the post-World War II relationship among Roosevelt’s “four policemen.” This chart, I think, accords pretty well with our understanding of the period: the United States begins and ends the period as the world’s most powerful military force, but during the Cold War its military potential (in conventional terms) was on par with the Soviets. (I know this point could be debated, and has been, ad nauseam, but it is not prima facie invalid.)

The value of the alternate measure becomes a little clearer when we consider the period 1816 to today.

 Here, we see the long peace of the 19th century reflected in the gradual build-down of European militaries (although note Germany’s relative rise over the late-nineteenth century). We also see, as we would expect, that actual forces-in-being peak in the two world wars (and that the United States, in both cases, emerges as the world’s leading military power–although it dismantles its military quickly after 1919). More important, we note that the leading powers of the 21st century — the United States, China, and Russia — are quite literally not on the map in the 19th (and if Japan were ever to begin behaving as realism says it should, it would join the new quartet). Moreover, eras of multipolarity, bipolarity, and unipolarity are fairly easily identifiable in this chart.

The question of how to measure international capabilities is a tough one, but I tend to think that decomposing military strength and military potential is a useful start. (In the short run, we care a lot about strength; in the long run, we care a lot more about potential.) Since all such measures–even GDP!–are ultimately somewhat arbitrary, it is at least useful to have a debate about what we should include in each. Duck readers, what would you include in your measures of international power?


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The Chart That Explains Your World

Everyone agrees China is a rising power. Some people think it can rise indefinitely; some people think its rise will decelerate; and some think that its rise is illusory. But it’s hard to put even the People’s Republic stellar growth rates into perspective without taking a longer view.

The chart above shows the ratios of Chinese to other countries’ GDP per capita. It’s based on painstaking work by Angus Maddison to reconstruct long time series about output. There are reasons to think that Maddison’s estimates before 1900 are a little speculative, but they are widely agreed to capture the broad picture fairly well.

Interpreting the chart is straightforward. The y-axis shows how many times richer each country or continent is than China. In 1960, for instance, the United States was about 20 times as rich per head as China, while Britain was about 15 times as rich and Japan and Russia about 8 times as rich. The chart is showing us, then, just how far behind the rest of the world China had slipped. And note that a lot of that is due to the early Communist regime; the Cultural Revolution and the Great Leap Forward are visible in the time trends as the post-World War II peaks in the ratios (since Chinese output fell dramatically as Western and Soviet output continued to rise).

Over the past 30 years, however, those ratios have plummeted. The United States and other developed countries are still much richer than China, but they are no longer vastly richer. Those falling ratios portend just how dramatic the shift in the global distribution of wealth, and of power, from the North Atlantic community will be.

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PoliSci-unrelated post of the day: Visualizing Major League Baseball, 2001-2010

This post originally appeared at Beyond the Box Score.  If you are a baseball analysis fan and don’t already read BTBS I highly recommend it.

2010 marks the end of the “ought” decade for Major League Baseball.  I thought I would take the opportunity to analyze the last 10 years by visualizing team data.  I used Tableau Public to create the visualization and pulled team data from ESPN.com (on-field statistics) and USA Today (team payroll).

The data is visualized through three dashboards.  The first visualizes the relationship between run differential (RunDiff) and OPS differential (OPSDiff) as well as the cost per win for teams.  The second visualization looks at expected wins and actual wins through a scatter plot.  The size of each team’s bubble represents the absolute difference between their actual and expected wins.  Teams lying above the trend line were less lucky than their counterparts below the trend line.The final tab in the visualization presents relevant data in table form and can be sorted and filtered along a number of dimensions.

The first visualization lists all 30 teams and provides their RunDiff, OPSDiff, wins, and cost per win for 2001-2010.  The default view lists the averages per team over the past 10 years, but you can select a single year or range of years to examine averages over that time frame.  The visualization also allows users to filter by whether teams made the playoffs, were division winners or wild card qualifiers, won a championship, or were in the AL or NL.  The height of the bars corresponds to a team’s wins (or average wins a range of years).  The color of the bars corresponds to a team’s cost per win–the darker green the bar the more costly a win was for a team.  Total wins (or average for a range of years) is listed at the end of each bar.  In order to create the bar graph I normalized the run and OPS differentials data (added the absolute value of each score + 20) to make sure there were no negative values.  For the decade, run differential explained about 88% of the variation in wins and OPS differential explained about 89% of the variation in run differential.

The visualization illustrates the tight correlation between RunDiff and OPSDiff, as the respective bars for each team are generally equidistant from the center line creating an inverted V shape when sorted by RunDiff.  In terms of average wins over the decade, there are few surprises as the Yankees, Red Sox, Cardinals, Angels, and Braves round out the top 5.  However, St. Louis did a much better job at winning efficiently, as they paid less per win than the other winningest teams (<$1M per win).

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The viz also illustrates the success of small market teams such as Oakland and Minnesota who both averaged roughly 88 wins while spending the 3rd and 4th least respectively per win.  If you filter the visualization for teams that averaged over 85 wins during the decade, it really drives home how impressive those two teams’ front offices have been at assembling winning ball clubs with lower payrolls.  No other team that averaged >85 wins paid less than $975K per win.  Oakland looks even more impressive when you isolate the data for years that teams qualified for the playoffs.  Oakland averaged 98.5 wins during seasons they made it to playoffs, and did so spending only $478K per win.


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What about the big spenders?  The five biggest spenders included the Yankees, Red Sox, Mets, Dodgers, and Cubs.  The Yankees spent an astounding $1.8M per win during the decade, but they also averaged the most wins with 97.  Some will say this provides evidence that the Yankees–and other big market teams–simply buy wins and championships.  However, only 17% of the variation in wins was explained by payroll during the decade.  Moreover, while the Yankees occupied 6 of the top 10 spots in terms of cost per win they were the only team to earn a positive run differential.  The Cubs, Mets, Mariners and Tigers all finished under .500 and missed the playoffs while those Yankee teams qualified for the playoffs 5 out of 6 years and won one World Series.  Yes, the Yankees spend significantly more per win, but they spend more wisely than many other deep pocket teams.
Teams that made the playoffs averaged a little over $1M per win in those years they qualified, with Wild Card teams ($1.030M) spending a tad bit more than Division winners ($1.006M)–about $14K per win on average.  World Series winners spent $1.08M per win in their winning years compared to $1.002M for other playoff teams.  Teams that failed to make the playoffs averaged $923K per win.
The best team of the decade in terms of run differential?  The 2001 Seattle Mariners, who amassed an incredible +300 RunDiff.  Even with that total they were only expected to win 111 games–they would go on to win 116.  The Mariners had only the 11th highest payroll that year and so paid a measly $644K per win.  The absolute worst team of the decade?  The 2003 Detroit Tigers, who earned a RunDiff of -337 and actually won less games than expected (43 vs. 47).  Given their ineptitude on the field, the Tigers paid $1.14M per win even though their total payroll for the year was only $49M.
Luckiest team?  The 2005 Diamondbacks who won 77 games despite a RunDiff of -160 (only 64 expected wins).  Hardest luck team?  The 2006 Indians, who only won 78 games with a +88 RunDiff that should have translated into 90 wins.
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There are tons of ways to manipulate the visualizations and cut the data.  Hopefully viewing the data in this way is helpful and illuminates some things we didn’t know and drives home other things we had a hunch about. This is my first attempt to visualize this data, so please feel free to send along any and all comments so I can improve it.

Author’s Note: Due to a very helpful comment by Joshua Maciel, I have updated the visualization.  Here is a link to the original version for those that are interested.

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Visualizing War

Two topics that are right up my alley: international conflict and data visualization. Put the two together, and you have a truly thought provoking piece of work.

David McCandless is a “visual journalist” who specializes in visualizing data across numerous subjects. In his latest work for The Guardian’s Data Blog, David visualize a ton of data regarding troop deaths and injuries, size of forces by country, as well as the civilian toll in Afghanistan.

Like all good visualizers, David tells a story with his infographics, putting many issues in perspective (likely creating some “oh, I didn’t realize” moments for readers) through the use of relational data. For example, David compares absolute measures of troop fatalities by country to fatalities as a percentage of total troops deployed. What one sees is that while the US has lost the most troops by far, the Canadians have lost the most troops as a percentage of those they have deployed:


The piece is chock full of infographics like this. One issue I have with David’s analysis comes less from the data (and it’s visualization), and more with his commentary. One infographic depicts the number of troops in Afghanistan by country or organization (e.g. NATO, etc). David includes a bubble for private security contractors (PSC), which I think is great as it is a key statistic that we should be taking into account. However, while his own data shows that only 3,000 of these contractors are armed he makes the comment that “that’s a huge amount of hired guns”. Now David may just be using the common phrase of ‘hired gun’ to refer to all PSCs, but when talking about PSCs such a phrase implies something very specific (i.e. contractors that carry and use weapons in theater–not just logistical support, etc). If he isn’t just using the phrase in the generic sense then the claim is overblown. By his own numbers, armed PSCs only make up a little over 1% of the entire fighting force in Afghanistan (3,000 out of 292,486). I am not sure 1% constitutes a lot of hired guns.

The best part about how David operates is that he provides links to all his data (including what was used for this article) via Google Docs. This is a great practice, one that encourages readers to check his work, look for additional patterns in the data, and ensures that any errors in presentation or interpretation can be brought to light and discussed. (David has altered other infographics based on reader feedback.) I wish more people would adopt the practice.

In any event, be sure to check out David’s blog and other work.

[Cross-posted at bill | petti]

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