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.

It also reminded me of this episode of The West Wing, where C.J. and Josh are briefed on how the Mercator projection map that we all use distorts the relative size and position of the continents.

The size of each rectangle is proportional to the items share of the total budget.

There are two great features that make this visualization particularly helpful. One is you can toggle back and forth between this year’s budget and the proposed 2011 budget and compare the changes for each item.  The other is that you can isolate discretionary spending from mandatory spending, which is very educational when thinking about the size of the budget:

The visualization is not only beautiful, but also clever, informative, and user friendly.  It takes the most popular supplements and plots them from top to bottom in terms of how strong the scientific evidence is for their relative effectiveness in treating certain conditions.  Additionally, viewers can sort the data by indication to see which supplements are backed (or not) by scientific evidence.

The universe of health supplements is indeed a noisy one, but data visualizations such as this one can help make sense of it all.  Good data visualizations can help separate signals from noise by clearly presenting a large amount of data in a simple, relational way.  In this case, you have the most popular supplements ranked by the degree to which scientific evidence backs their claims.  It is pretty easy to see which claims are valid, and which appear to be nothing but empty promises (and a big waste of money).

Lundry quickly runs down the importance of infographics and data visualizations in the political realm.  Bottom line: people are hard wired to learn through visualization, and infographics can be very powerful tools in political battles over ideas and policy:

It amazes me that we haven’t seen a faster uptake among professional politicians of data visualization, especially considering the sheer number of political operatives, consultants, and strategic communication firms.  All it takes is about five minutes watching C-SPAN to realize that these folks are due for a major upgrade in the infographics department.

I also love Lundry’s updating of a famous H.G. Wells quote

Visual Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.

Personally, I think you need both visual and statistical in there, but in general I agree wholeheartedly with the sentiment.

The first comes via Nathan Yao over at FlowingData, who has put together an interactive data visualization that depicts spending patterns by U.S. consumers over the past 25 years (data courtesty of the Bureau of Labor Statistics).  You can compare spending across the various categories or click on a single category and see how consumers have (or have not) altered their spend.  What’s most interesting is that for many categories, spending patterns have barely shifted since the mid-1980′s.  What isn’t clear is what is actually driving the change in relative spending.  I would suspect that to some extent the data reflects changes in consumer behavior, but it is also likely capturing shifts in the relative cost of various items (e.g. apparel spending decreased, but so did its cost relative to other items).

The second was published by the Wall Street Journal on the heels of the recent jobs report, which showed a drop in unemployment and drastic slow down in the rate of job loss. As a companion to their coverage, the Journal published an interactive chart that plots the monthly U.S. unemployment rate going back to 1948. Scroll over each cell to see the recorded unemployment rate for each month. Additionally, you can choose to view previous recessions overlayed on the data (marked by circles–see below). Two things immediately struck me: 1) it put into perspective how rare an unemployment rate above 10% has been in the U.S. over the past 51 years; and 2) if the current data does indicate that we’ve reached the nadir of unemployment (which I am not convinced we have), we will have only experienced two consecutive months of 10%+ unemployment compared to the recession of the early 1980′s, which experienced 10 consecutive months of 10%+ unemployment.

AT&T did not file this lawsuit because Verizon’s ‘There’s A Map For That’ advertisements are untrue; AT&T sued because Verizon’s ads are true and the truth hurts.

Verizon in an official response to AT&T’s lawsuit that Verizon’s “there’s a map for that” ad campaign is misleading.

The background is that Verizon recently rolled out a campaign that uses a visualization of relational data to compare their 3G coverage to AT&T.  The visualization is pretty stark.  AT&T fired back that the ad was misleading, giving consumers the impression that AT&T doesn’t have ANY wireless coverage in most parts of the country.

Decide for yourself.  FlowingData has a great recap post with all the ads to date.

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.

What jumped out at me was the number of times Rivera pitched in more than 2 innings*: 52 out of 88. In this day and age where closing has become even more specialized and pitchers in this role rarely throw in more than one inning, Rivera took the rubber in multiple innings almost 60% of the time.  This included a three inning, nine out performance against the Boston Red Sox in game 7 of the 2003 ALCS (the famous Grady Little game).

*By innings I mean separate half innings–innings here does not mean that Rivera needed to get 3 outs each time he took the mound.

(Via Nate at FlowingData)

For those that don’t know him, Rosling is something of a superstar at the intersection of public health research and data visualization.  Rosling’s recent work focuses on dispelling the misconception that there is a binary distinction between the developed and developing worlds.  While Rosling is not the first to argue against this dichotomy (and all its political ramifications), his approach is both fascinating and affective.  Rosling utilizes an impressive visualization tool–that he developed and Google bought–to present data on the progress of the developing and developed worlds in a relational manner.  The video below was shot in June of 2009 at a TED-sponsored talk at the US State Department.

Rosling’s presentation drove home two points for me: the importance of challenging our assumptions, the impact of data visualization, and the power of relational data.

1. Too often we take for granted what the facts are around a given issue. What was true yesterday may not be true today; however, what was true yesterday undoubtedly contributes to our mindset today and the prism through which we view today’s issue. How does one combat this characteristic of human perception? The review of data. We should be challenging our assumptions, testing them against meticulously collected data to ensure that what we once that of as conventional wisdom still applies. If not, change it.
2. How data is visualized can have as big an affect on helping us to alter our views. The data Rosling relies on is not new or proprietary (heck, I used some of it when I lectured on the political economy of development). What is innovative and impactful is how we presents the data.  Rosling’s software visualizes the data in a way that is more powerful than simply providing a list of child mortality rates or the percent change over time for each country.  It allows him to tell a story, to weave a narrative which connects the audience to the data in a way a regular bar chart or data table simply could not do.
3. The most powerful way to visualize data is relationally.  Rosling’s visualizations make full use of this principle, presenting country data relative to other countries as well as across time.

When I think of the difference a data-driven world can and will make, it is this–the ability to view the world and make decisions in a fact-based manner.  We can better separate the wheat from chaff, signal from noise.  We can make our mind set match our data set.  And we are just scratching the service.