Archive for February, 2011

Pitchers and Injuries: It Happens

When news broke on Wednesday of Adam Wainwright‘s season-ending injury, it obviously was quite distressing news for Cardinals fans. Not only was Wainwright the ace of the Cardinals’ pitching staff, but the Cardinals are projected to be thick in the race for the NL Central, making his contributions all the more valuable. While Wainwright isn’t costing the Cardinals much this season, the list of pitchers that will be competing to replace him isn’t anything to get excited about. If I were a Cardinals fan, I’d be watching this video over and over and over again, drowning my sorrows in fond memories and root beer.

But Wainwright’s injury isn’t traumatic only for Cardinals fans: no matter what team you root for, this news is frightening. Wainwright is a relatively young pitcher (entering his age 29 season) and he’s pitched 230 innings each of the previous two years. He’s been a perennial Cy Young contender, and never had significant arm issues before. If this sort of an injury can happen to him, well, who isn’t at risk?

This is probably old news for the majority of FanGraphs readers, but this point can’t be driven home often enough: pitchers are fickle creatures that are always at risk for an injury.

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Understanding Projections, “True Talent Level”, and Variability

This is the second in a series of posts about projections. The first part was about the methodology behind each projection system. In this section, we look at what projections are actually telling us.

If you’re new to projections and want to use them to, say, help with your fantasy team, it’s easy to make a common mistake: underestimating the built-in variability in projections. Many people – and I used to be among this group myself – view projections as hard and fast guesses at a player’s production this next season. Most people get into projections as a result of fantasy baseball, so this makes sense; we all want to know which player is going to hit 30 homeruns this next season and which will steal 40 bases. However, projections are actually measuring something different than a player’s expected production: they’re measuring a player’s true talent level.

This might seem like an arbitrary distinction, but trust me, it’s not. As we all know from our day-to-day lives, having a “true talent level” at a particular skill does not necessarily mean you’ll perform at that level every single time in the future. Our minds love to ignore variability and instead treat outcomes as solely talent-driven, but the world doesn’t work that way. Let’s consider a couple examples.

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Food Metaphors, Replacement Level Style

When writing my irreverent NotGraphs post on Casey Fossum, an interesting question popped into my head: how could I best explain the concept of a replacement level player using a food metaphor? In other words, is there a “replacement level” food? Not every baseball fan is a math nerd, but ALL sports fans love food. This is an indisputable truth, and means that food metaphors have the potential to be one of the most potent teaching instruments since these amazingly quirky mathematics videos.*

*Also, before you ask, this post is a direct reference to Fire Joe Morgan and their historic “Food Metaphors” tag, possibly the best thing that Ken Tremendous has ever created, ever. And yes, I’m a huge fan of “The Office”.

Before we get into the nitty gritty of finding the perfect food metaphor for replacement level, we need to know what replacement level is. In case you have forgotten (or don’t know), here’s Graham MacAree’s description of replacement level, as taken from our page in the Library:

We can define a replacement level player as one who costs no marginal resources to acquire. This is the type of player who would fill in for the starter in case of injuries, slumps, alien abductions, etc.

These are essentially the Triple-A filler players that can be found in every organization (and in copious amounts on the free agent list) every year. They cost next to nothing to acquire, can be found in massive quantities, and should only be used in case of emergency – at best, they make adequate bench players. They are, in short, the very base of major league baseball’s (triangular) talent distribution.

So with this in mind, what’s the ideal food to capture the essence of a replacement level player? Let’s take to the Twitter!

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The Projection Rundown: The Basics on Marcels, ZiPS, CAIRO, Oliver, and the Rest

Now that football season is over and baseball is once again close at hand, Projection Season is well underway. Fantasy players, analysts, bloggers, and plain ol’ fans – everyone turns to projections to help them this time of year. The Hot Stove has cooled down and Spring Training has just started, so really…what else is there to do?

With that in mind, I’ve got a handful of posts on projections in the works for the next week. This is the first one, and in it I deal with a basic question: what are the different projection systems available, and how are each of them calculated? In order to know how to properly use each projection, it’s always a good idea to understand what data is taken into account and how it is used. Remember: there is no one “gold standard” for projection systems. Each system will tell you something slightly different, so whenever trying to draw conclusions from projections, it’s best to use as many sources as possible.

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“Sabermetrics for Dummies”: Mainstream Media Style

Jason Collette and Tommy Rancel talking with J.B. Long from the Bright House Sports Network.

Rarely do you ever see a mainstream media outlet take the time to discuss sabermetric stats. Every now and then you’ll see a passing reference to WAR or FIP on ESPN, but the announcers have a maximum of 30 seconds to introduce the statistic, explain what it means, and make their point. These mentions are great for general awareness of sabermetric statistics, but do they actually educate anyone? They can make be a good introduction to a statistic and make someone curious to learn more – and don’t get me wrong, I love when mainstream news sources mention saber stats – but to truly educate someone about sabermetrics takes more than that.

Enter the Bright House Sports Network.  While Bright House is a major sports network in the Tampa Bay area, covering topics ranging from national sports stories to local high school teams, they’ve begun augmenting their baseball coverage with some sabermetric analysis. Jason Collette, Tommy Rancel, and R.J. Anderson – three premier Rays bloggers – contributed articles on the BHSN website during the later half of the 2010 season, using their analyses as a springboard for readers to become familiarized with advanced statistics.

And now, Bright House is taking it a step further: filming “Sabermetrics for Dummies” videos with Jason, Tommy, and reporter J.B. Long. This first video is a mere introduction to the series, but more videos will be released this week and the topics will include wOBA, BABIP, LOB%, WAR, IsoP, and FIP. These are extended videos, with the idea of explaining to viewers how the sabermetric stats are calculated and why they are useful.

Is it just me or is this rather unique? Has any other mainstream sports station done something similar? I’d love to hear examples of other media outlets doing similar projects (please share!), but at least to my knowledge, the Bright House Sports Network is ahead of the curve.


Left On Base Percentage (LOB%): A Video Explanation

Analyzing pitchers is one of the most difficult things to do in baseball (at least, in the “non-playing” category). Pitchers are notoriously fickle, and their performances can vary widely from start to start and year to year. They don’t follow a set aging curve like position players (who peak at ages 27-30), but improve and decline with no overarching pattern. Some pitchers are late-bloomers and don’t peak until their 30s (e.g. Randy Johnson), while others peak in their early 20s and never reach the same level again (e.g. Scott Kazmir).

Not to mention, when you try analyzing a pitcher’s results, there are so many variables in play. How much of a pitcher’s performance is his talent shining through, and how much is the defense, opposing team, umpire, catcher, and ballpark? With no discernible difference in his pitch movement, sequencing, or velocity, a pitcher may let up 8 runs in four innings during one start yet turn around and throw an 8 inning shutout his next time out. How much of that variance should we pin on the pitcher and how much is outside his control?

These are all difficult questions without any exact answer, which is why there are a large number of pitching statistics available here at FanGraphs. In order to see past those confounding variables and get a grasp on a pitcher’s true talent level, it’s best to look at a wide range of statistics instead of relying upon one as the be-all-end-all. ERA, FIP, tERA, xFIP, BABIP, LOB%, HR/FB – all these stats tell you something different and paint a more complete picture when used together.

And so, here’s a chance to learn a bit more about one of those statistics: Left On Base Percentage (LOB%). This video is courtesy of Bradley Woodrum from DRaysBay and Tom Tango from The Book Blog:


Heat Maps: What They Show, and Mistakes to Avoid

When David Appelman dropped his newest bomb on us the other day and announced that you could now find customizable heat maps here at FanGraphs, I think it’s safe to say that most of us saber-nerds had our minds blown. Personally, I’ve always admired the work that Dave Allen and other Pitch F/x gurus have done, yet being unskilled in the art of SQL and R, I figured this was a type of analysis that would always be beyond my abilities. Following in the footsteps of other FanGraphs updates, though, this analysis has now been democratized and made available to even the newest of saber newbies. You don’t have to know how to string together code or manipulate huge data sets: all you need is a mouse and a pointer finger.

But heat maps are like any other tool: before you can add them to your toolbox, you have to understand how to use them. Pitch F/x data can be a tricky thing to interpret, and many experienced saberists (myself included) have made mistakes because they didn’t know what they can and can’t do with that data. What exactly are heat maps? What do they show, and how should we use them? Let’s go exploring:

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