January 2013

Chances of making an NFL field goal

January 28, 2013 | David Smith

If the scores are tied in this weekend's Super Bowl and the game rests on the outcome of a field goal attempt, the chart below will tell you the chances of the goal being made. All you need to know is the distance of the kick: What this says that ... [Read more...]

How slow is R really?

January 28, 2013 | Jacob Simmering

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... [Read more...]

Football geeks: your 10,705 field goals are ready

January 28, 2013 | dan

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 ... [Read more...]

Getting Started with F1 Betting Data

January 28, 2013 | Tony Hirst

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 ... [Read more...]

The "golden age" of a football player

January 28, 2013 | Sascha W.

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, ... [Read more...]

The law of small numbers

January 28, 2013 | arthur charpentier

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 ... [Read more...]

Evolution of a logistic regression

January 28, 2013 | gerhi

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 ... [Read more...]

The components garch model in the rugarch package

January 28, 2013 | Pat

How to fit and use the components model. Previously Related posts are: A practical introduction to garch modeling Variability of garch estimates garch estimation on impossibly long series Variance targeting in garch estimation The model The components model (created by Engle and Lee) generally works better than the more common ... [Read more...]

I thought R was a letter…intro/installation

January 27, 2013 | moonheadsing

I will make a confession. This past summer, I didn’t spend my spare time watching relentlessly addicting TV shows nor clubbing in San Francisco. Instead, I checked out figures. No, not the sort of figures you’re probably thinking about. The ones that are included in research papers and ... [Read more...]

European Fishing

January 27, 2013 | Wingfeet

I am playing around with Eurostat data and ggplot2 a bit more. As I progress it seems the plotting gets more easy, the data pre-processing a bit more simple and the surprises on the data stay.Eurostat dataThe data used are fish_fleet (number of ships) and fish_pr (production=... [Read more...]

A slightly different introduction to R, part II

January 27, 2013 | mrtnj

In part I, we looked at importing data into R and simple ways to manipulate data frames. Once we’ve gotten our data safely into R, the first thing we want to do is probably to make some plots. Below, we’ll make some simple plots of the made-up comb ... [Read more...]

Regression tree using Gini’s index

January 27, 2013 | arthur charpentier

In order to illustrate the construction of regression tree (using the CART methodology), consider the following simulated dataset, __ set.seed(1) __ n=200 __ X1=runif(n) __ X2=runif(n) __ P=.8*(X1.7) __ Y=rbinom(n,size=1,P) __ B=data.frame(Y,X1,X2) with one dichotomos varible (the variable of interest, ), and two ... [Read more...]

Tracking Number of Historical Clusters

January 26, 2013 | systematicinvestor

In the prior post, Optimal number of clusters, we looked at methods of selecting number of clusters. Today, I want to continue with clustering theme and show historical Number of Clusters time series using these methods. In particular, I will look at the following methods of selecting optimal number of ... [Read more...]
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