Monthly Archives: January 2013

How slow is R really?

January 28, 2013
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One thing you always hear about R is how slow it is, especially when the code is not well vectorized or includes loops. But R is an interpreted language and its strong suit really isn't speed but rather the comparative advantage is the 4,284 packages o...

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Football geeks: your 10,705 field goals are ready

January 28, 2013
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Football geeks: your 10,705 field goals are ready

We looked at NFL punts before on Decision Science News. That's old news. Field goals are the new hotness, and Super Bowl Sunday is coming up, so let's look at a kicker's chances. We've taken the same data set and looked at a kicker's chances of getting the ball through the uprights depending on the yard line the kick is...

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Using R: writing a table with odd lines (GFF track headers)

January 28, 2013
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Using R: writing a table with odd lines (GFF track headers)

The other day, I wanted to add track lines to a GFF file, so that I could view different features as separate custom tracks in a genome browser. The need to shuffle genome coordinates between different file formats seems to occur all the time when you deal with some kind of bioinformatic data. It’s usually

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Applying Tradeblotter’s Nice Work Across Manager Rather than Time

January 28, 2013
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Applying Tradeblotter’s Nice Work Across Manager Rather than Time

Ever since I saw the very helpful distribution page first presented in Download and parse EDHEC hedge fund indexes, I have used it liberally.  Now that it is has been functionalized (Visually Comparing Return Distributions), I thought I would amen...

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Getting Started with F1 Betting Data

January 28, 2013
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Getting Started with F1 Betting Data

As part of my “learn about Formula One Stats” journey, one of the things I wanted to explore was how F1 betting odds change over the course of a race weekend, along with how well they predict race weekend outcomes. Courtesy of @flutterF1, I managed to get a peek of some betting data from one

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The "golden age" of a football player

January 28, 2013
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The "golden age" of a football player

It's been some time since my last post on football. And we're talking about european soccer here. So I finally managed to write some functions which allow me to extract player stats from www.transfermarkt.de. The site tracks lots of stats in the world of soccer. For each player, there is information about the dominant foot, height, age, the estimated...

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The law of small numbers

January 28, 2013
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The law of small numbers

In insurance, the law of large numbers (named loi des grands nombres initially by Siméon Poisson, see e.g. http://en.wikipedia.org/…) is usually mentioned to legitimate large portfolios, because of pooling and diversification: the larger the pool, the more ‘predictable’ the losses will be (in a given period). Of course, under standard statistical assumption, namely finite expected value, and independence (see http://freakonometrics.blog.free.fr/…....

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Evolution of a logistic regression

January 28, 2013
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Evolution of a logistic regression

In my last post I showed how one can easily summarize the outcome of a logistic regression. Here I want to show how this really depends on the data-points that are used to estimate the model. Taking a cue from the evolution of a correlation I have plotted the estimated Odds Ratios (ORs) depending on

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analyze the survey of consumer finances (scf) with r

January 28, 2013
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the survey of consumer finances (scf) tracks the wealth of american families.  every three years, more than five thousand households answer a battery of questions about income, net worth, credit card debt, pensions, mortgages, even the lease on th...

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My template for controlling publication quality figures

January 28, 2013
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My template for controlling publication quality figures

The following is a template that I usually start with when producing figures for publication. It allows me to control:The overall size of the figure (in inches) (WIDTH, HEIGHT)The layout of figure subplots (using the layout() function) (LO)The resoluti...

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