Posts Tagged ‘ sports ’

Nine lightning talks on R

October 12, 2012
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At Tuesday's Bay Area R User Group meetup, nine speakers gave five-minute talks on various aspects of R. Revolution Analytics' Luba Gloukhov was one of the presenters, and also provides the summary of the talks below. Links to the slides are included where available for you to check out. Ariel Faigon: Chrestomathy with R Ariel walked us through his...

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Does playing baseball shorten your lifespan? (Answer: No.)

August 24, 2012
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Does playing baseball shorten your lifespan? (Answer: No.)

A National Institute for Occupational Safety and Health study, published in March, found that professional American football (NFL) players lived longer, on average, than similar "mere mortals" in the general population. Football is a dangerous sport, so that might seem surprising at first, until you consider the fact that NFL players are elite sportsmen: only the strongest, fastest and...

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Predicting the 100m sprint: results

August 6, 2012
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Last week, Markus Gesmann used a log-linear model in R to predict the Olympic gold-medal winning 100m sprint time to be 9.68 seconds. The actual time was 9.63 seconds. Not bad! Meanwhile, the New York Times put Usuain Bolt's olympic record in context, comparing him in a virtual race with other gold medal-winners over the past century (via FlowingData).

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A prediction for the Olympic men’s 100m sprint

July 30, 2012
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A prediction for the Olympic men’s 100m sprint

R user Markus Gesmann used the gold-winning times from the Olympic Men's 100m sprint since 1990 as the basis of the following prediction for the London Games: My simple log-linear model forecasts a winning time of 9.68 seconds, which is 1/100 of a second faster than Usain Bolt's winning time in Beijing in 2008, but still 1/10 of a...

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Three hours of pure soccer emotion, visualized with R

July 6, 2012
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Three hours of pure soccer emotion, visualized with R

The biggest prize in UK soccer, the Premier League Championship, is decided by a points system. Unlike most sports competitions, there's no final round or playoff series: once the regular round of games is complete, the team that has accumulated the most points (three for a win, and one for a draw) is the champion of English football. In...

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Figuring an exchange rate for sports scores

June 26, 2012
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While the US's Major League Soccer is using advanced analytics to analyze ball movement and improve team composition, they might want to think about a smaller, but possibly more impactful, goal for analytics. Like, how to explain to an American audience what a 1-2 game means to a basketball or baseball fan not familiar with scoring in the beautiful...

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Simulating Euro 2012

June 11, 2012
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Simulating Euro 2012

Why settle for just one realisation of this year’s UEFA Euro when you can let the tournament play out 10,000 times in silico? Since I already had some code lying around from my submission to the Kaggle hosted 2010 Take on the Quants challenge, I figured I’d recycle it for the Euro this year. The

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Mariano Rivera’s baseball prowess, illustrated with R

May 11, 2012
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Mariano Rivera’s baseball prowess, illustrated with R

Kevin Quealy, graphics editor at the New York Times, has published another fascinating behind-the-scenes look at how the Times creates data visualizations for print and online. In his latest post, he looks at how a visualization of the Yankee's Mariano Rivera performance compared to other Major League Baseball pitchers was created. (Detail below, click for the full image.) The...

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FBS Coaches Avg. Salary

November 18, 2011
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FBS Coaches Avg. Salary

Of course, a few days before I leave for a much needed vacation, USA Today released their updated NCAA coaching salary database. For sports junkies, there’s an unlimited number of analysis and visualizations that can be done on the data. I took a quick break from packing to condense the data to a csv and

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What 5,728.986 miles look like…

November 10, 2011
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What 5,728.986 miles look like…

Time Series as calendar heat maps + All of my running data since April 1, 2009 = Generated by the following code: #Sample Code based on example program at: source(file = "calendarHeat.R") run<- read.csv("log.csv", header = TRUE, sep=",") sum(run$Distance) date <- c() for (i in 1: dim(run)){ if(run$DistanceUnit== 'Kilometer'){ miles <- c(miles,run$Distance * 0.62) }

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