Revolutions

Baseball games: getting longer?

ESPN's Bill Simmons (aka The Sports Guy) recently suggested that the primary cause of dwindling interest in Red Sox games by fans is that baseball games these days are too long. "It's not that fun to spend 30-45 minutes driving to a game, paying for parking, parking, waiting in line to get in, finding your seat ... and then, spend the next three-plus hours watching people play baseball", he says. But this is a relatively new phenomenon: are the games really any longer today than they used to be?

Inspired by the column and having recently learned about the power of R's ggplot2 package to visualize data, R user (and baseball fan) Ryan Elmore decided to find out. He wrote code to scrape from the Web data on the lengths of all MLB games since 1970, and then created some lovely visualizations using ggplot 2 and R. For example, here's a scatterplot of the lengths of games from all teams over time, with a smooth trend indicating a clear lengthening of the game, at least through the turn of the century:

The peak average game length comes during the "Steroids Era" of Baseball, which Ryan delineates on the chart with the dashed red lines. Beyond that peak, there appears to be a slow but steady decline in the length of the games (although still more than 20 minutes longer than games in the 70's). So how does this explain the disaffectation of Red Sox fans suggested by The Sports Guy? Ryan takes it one step further, and break out Red Sox games separately:

So it does seem that Boston games are indeed getting longer, lending credence to the Sports Guy's claim. However: I'm no baseball expert, but I'm pretty sure there have been more than 3 Red Sox games since 2004, and the apparent increase may just be a fluke. Given that Ryan has posted his code in R to create the chart, maybe someone with access to more data can verify the result? If you do, let us know in the comments. 

The Log Cabin: Are MLB games getting longer?

Charting the World Cup

Now that Spain has won the World Cup, it's interesting to go back and look at some metrics from the matches and see if we can tease out what characteristics made for a winning Cup team this time around. Fortunately, the Guardian's Data Blog has made a wealth of World Cup statistics available, with data on every player of every team (position, shots at goal, passes, tackles made, and saves), plus aggregate statistics for each team (goals, % shots on target, fouls, and much more). The data are ripe for analysis in R, especially given that you can download the data directly from the cloud as an R object with the following commands:

players <- read.csv("http://spreadsheets.google.com/pub?key=tOM2qREmPUbv76waumrEEYg&single=true&gid=1&range=A1%3AH596&output=csv")

teams <- read.csv("http://spreadsheets.google.com/pub?key=tOM2qREmPUbv76waumrEEYg&single=true&gid=0&range=A1%3AAG15&output=csv")

The method I've described before for accessing a Google Spreadsheet from R didn't quite apply here, as those instructions assume you own the document (and have access to the Publish menu). But some experimentation and tweaking of the spreadsheet URL made it work: the key parameters seem to be the "&gid=" (sheet number) and "%range=" (cell ranges, use %3A to encode the colon) and "&output=csv" to download in CSV format. It would be nice if Google published the specs to form URLs like these, but as far as I know they don't. 

Anyway, a couple of bloggers have used these data to great effect to express the results of the World Cup visually using R graphics. For example, the R Charts blog used ggplot2 to look at the number of fouls committed by each team during the tournament:

 

(Personally, I would have sorted the rows by descending number of fouls, rather than alphabetically.) Interesting to see that Cup champions Spain are in the middle of the pack on fouls, whereas runners-up Netherlands lead this table (boosted heavily by their performance in the final).

Blogger Jason Priem also took a look at the data, this time with a scatterplot of goals per game by fouls per game, related to how far each team advanced in the competition:

 

(Download Jason's code for this chart here.) Again it's interesting to see the positions of the two finalists here, with Netherlands on the extreme frontier for both fouls and goals, while spain is moderate on goals per game and near the lowest on fouls per games.

It's a rich dataset and I'm sure other Revolutions readers could come up with some equally interesting visualizations. If you do, tell us about it in the comments.

Guardian Data Blog: World Cup 2010 statistics: every match and every player in data

Analyzing competitive nordic skiing with R

Here's another great example of R being used to analyze sports data. Statistician and skier Joran Elias has started a project to analyze and visualize international cross country ski racing results, and he publishes his analysis at the blog Statistical Skier. All of the analyses are done using R (and for data, SQLite via the RSQLite package). As much as I love to ski, I'm not so familiar with the intricacies of ski racing as a sport, but it looks like there's a rich source of data ripe for analysis, and Joran is pulling out some interesting results. For example, here's the recent history of one recently-retired five-time Olympian Sabina Valbusa:

 

I can only assume a low "FIS Points" is better: Joran notes "best season overall might have been 2002-2003". A great use of ggplot2 and smoothing to visualize these data. Check out the blog for some other analyses -- especially if you're a ski junkie.

Statistical Skier: Data based analysis and commentary on nordic skiing

Winning the first game in a baseball series: a harbinger, or not?

For those not familiar with the major-league baseball in the US (and despite living here for more than 10 years, I still include myself in that category), the games usually played in series: team A visits the home of team B, and the two teams play two or more games against each other on successive days. It's common wisdom that if team A wins on the first day, they're more likely to be the victors on the second day, too. (Folks "knowledgeable in baseball and the mathematics of forecasting" give the probability of a second-day win given winning the first game at 65% to 70%.) But is that assertion borne out by the data?

Decision Science News has analyzed data from all major league baseball games played between 1970 and 2009, and used R to analyze the likelihood of winning the second game in a consecutive pair of games, given the result of the first. (All of the R code and data are provided, if you want to try and replicate the analysis yourself.) You can see various analyses in DSN's post, but the most revealing one for me was this chart on the frequency of successive wins with respect to the margin of victory (excess runs) in the first game:

So moderate and even significant wins by several runs don't in the first game don't appear to confer much benefit in the second. As DSN points out:

The equation of the robust regression line is: Probability(Win_Second_Game) = .498 + .004*First_Game_Margin which suggests that even if you win the first game by an obscene 20 points, your chance of winning the second game is only 57.8%

In fact, any second-game advantage from the first win that may exist is far overshadowed by the home-team advantage: "When it comes to winning the second game, it’s better to be the home team who just lost than the visitor who just won." See the full article at Decision Science News for the details of the analysis.

Decision Science News: You won, but how much was luck and how much was skill? 

Analyzing Google's Winter Olympics Search Traffic with R

The Official Google Blog today includes an analysis of Google's search traffic related to the recently-concluded Winter Olympics, correlating various high-profile events with searches from particular countries. For example, traffic from the United States shows the expected diurnal cycle but with promintent peaks for the opening ceremony and the hockey matches featuring the USA team:

It's not specifically stated in the post, but it's a good bet that R was used to create this analysis. It's well known that R is in widespread use at Google, but the telltale sign here is the Y axis label. Google's traditionally coy about giving exact figures for search numbers, and in this chart the search volumes have been suppressed. That's easy to do in R, by passing the yaxt="n" option ("Y axis type: none") to the plot command. Here's an example, using the built-in lynx data set:

> plot(lynx,yaxt="n")

That tends to leave the Y axis label a bit too far off the axis itself though (it leaves room for number labels that are no longer there), but you can move it into place with an explicit call to the mtext function to place the label 1 line of text from the axis (and suppressing the original axis label with ylab="" when calling plot):

> plot(lynx,yaxt="n",ylab="")
> mtext("Lynx pelts",side=2,line=1)

I'd bet good money that similar code was used for these plots. Now if only that could recoup my losses from the USA-Canada hockey final...

Official Google Blog: Searching for gold during the Games

Analyzing plays in the NBA

The Bastketball Geek had a couple of in-depth analysis of professional basketball games in the US in the last month. The first analyzed the various ways a play could end (shots, out-of-bounds, etc.), and was followed up by an analysis of the ways the subsequent play might begin. Both analyses are based on Bayesian Statistics (assuming a Dirichlet distribution) from the MCMCpack package in R.

Baseball Geek: Conditioning the Distribution of Play Ending Events Given How the Play Starts

Basketball player performance, home vs away

Self-proclaimed basketball geek Ryan has collected player statistics from the 2006/2007 2007/2008 and 2008/2009 NBA seasons, broken down by home and away games. He has data like the free-throw percentage for each player at home and away games, 3-point shots landed, offensive and defensive rebound percentages and so on. (I confess I'm hazy on the meaning of a couple of the acronyms.) He uses R to explore the relationship between the home and away games, to see if there's any home advantage for any of these stats. A good first start is the scatterplots of the home vs away data. For example, here's the chart for free throw percentage (each dot is a player):

Follow the link for charts of the other stats, and links to the source data and the R code. It's an interesting data set, and would lend itself well to other visualizations and analyses, should any basketball fan and R user be so inclined.

It's great to see more sports analysis being done with R, so much that we now have enough articles for a dedicated sports section!

Basketball Geek: Player Statistics at Home vs Away

Kentucky Derby bump-chart

Last week I opined that it would be nice to see a chart of the Kentucky Derby done in R. Little did I know the challenge had already been answered! (Thanks for letting me know in the comments.)

I'd heard about Mine That Bird's come-from-behind performance, but hadn't seen the race myself. You can really see him move ahead at the 3/4 mile mark!

Visit Learning R to see how this chart was created using the ggplot2 package.

Learning R: ggplot2 Bump Chart

Visualizing a horse-race

A reader on the Statistical Modeling blog has come up with an interesting application of ggplot2 in R: visualizing the placements over time of horses in a horse-race (click to enlarge):

The code for the chart is available at the link below. This is simulated race data, but it would be cool to see this chart for the recent upset in the Kentucky Derby.

Statistical Science: A horse-race graph!

Quote of the day

A respected Columbia professor disputes the findings that trailing at halftime makes a team more likely to win. Andrew Gelman's rebuttal is over my head, but seems to boil down to different ways to interpret the same data, and he includes this instant classic of a line: "they do some ridiculous overfitting with a 5th-degree polynomial. Don't even get me started on this sort of thing." Someone had to say it, and he said it.

ESPN (!): Wednesday Bullets