Going into this season, I thought I’d made a huge mistake. During the auction draft in my ottoneu league, I got distracted and ended up putting in the highest bid for a pitcher I hadn’t heard about much: Jaime Garcia. I knew enough about him to know he’d had a great 2010 season  (2.70 ERA, 3.41 FIP) and was still quite young, but due to being a Rays fan, I’m not as well versed on the National League. The more I looked into him after the draft, I saw analysts spelling doom for Garcia everywhere. He outperformed his peripherals. He struggled against righties. He got an artificial boost from Busch Stadium. He increased his innings total by around 120 IP from 2009 to 2010. The popular consensus seemed to be, “Don’t touch this guy!”, so I just added the incident to my long list of  “Reasons I Don’t Write About Fantasy Baseball” and moved on.

After his near perfect game on Friday night, though, it’s time someone pointed this out: Jaime Garcia has been darn good so far this season. And when I say good, I mean 1.99 ERA / 2.36 FIP good.

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As I’m sure many of you are aware, doing early season baseball analysis can be a difficult thing. It’s tempting for saberists to scream “Small sample size!” whenever someone makes a definitive statement about a player, and early season results should always be viewed with a heavy dose of skepticism. After all, it’s a heck of a long schedule: the season started over a month ago, but we’re still less than 20% of the way finished. With most players, we have years and year of data on them – whether in the majors or minors – so why should we trust their results over a mere 100 plate appearances? More data almost always leads to better predictions, so at this point in the season, trusting 2011 results over a player’s past history is a dangerous thing.

At the same time, completely ignoring 2011 results is a horrible idea too. Some players do make dramatic improvements in their game from year to year, and there are always players that age at a different rate than expected — young players that develop fast (or slow) and old players that age quickly (or slowly). Some of a player’s early season results might be the result of a slump or streak, but sometimes there’s also an underlying skill level change that’s tied in with that slump or streak.

So how do we untangle what’s random variation and what’s a skill level change? Scouting information is huge when evaluating players in small samples, but sadly, not many of us are scouts. But stats can still help; you just have to know where to look.

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I don’t know about everyone else, but it can be somewhat difficult for me to keep track of all the different splits that are worth remembering. We all know that batters typically fair better against opposite-handed pitchers, but sabermetric knowledge has now progressed to the point where that’s not the only thing to keep track of anymore. What about batted ball splits? Does this pitcher throw a dominant changeup, and if so, what are the platoon splits for changeups?  How large of a sample size do I need before I can make assumptions about a player’s platoon split? It can be a lot of knowledge to remember, but it’s all important information in case you want to analyze a managerial move or lineup.

So below the jump, you’ll find a crib sheet for understanding lefty-right, batted ball, and pitch platoon splits. If you have any questions, feel free to ask in the comments.

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To say that Rafael Soriano has struggled so far this season would be akin to saying Babe Ruth was a decent ballplayer: you’d technically be right, but off by multiple factors of ten. Soriano has struggled mightily since making his Yankee debut, allowing at least one baserunner in nine of his ten appearances, and posting an “Ouch!” inducing 7.84 ERA and 5.55 FIP. He has three meltdowns already this season (only had four total in 2010), and he’s blown two Yankee leads: once on April 5th against the Twins and once last night against the White Sox. Last night was particularly painful, as Soriano plunked Carlos Quentin with a slider and then grooved a fastball down the heart of the plate to Paul Konerko. I don’t think I need to tell you where that pitch ended up.

Since it’s so early in the season, it’s easy to write off these struggles as relatively unimportant; odds are, this is just a slump and Soriano will be his normal, dominant self for the rest of the year. Soriano has only thrown 10 innings this year, so it’s way too early to begin putting credence in his ERA, FIP, or xFIP. But while that may be true, I don’t like leaving analyses at this level; I want to know why Soriano is slumping now. Is this simply a matter of bad luck? Has he changed his pitching approach? Is he struggling with any of his pitches?

To the Pitch F/x data we go!

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Alternate titles to this article: “How to NOT Look Like a Nerd” or “Convincing Your Friends You’re Right and They’re Wrong”.

As weird as it may sound, sabermetrics doesn’t need to be geeky. After all, saberists are simply trying to answer the same questions that everyday fans are trying to answer. How valuable is this player? How will certain players and teams perform in the future? Was this the correct managerial move or not? Sabermetrics is a new tool – a confusing tool to some people -but the questions are the same ones that fans have been asking for the last 80+ years.

But how do we present these new tools in a way that keeps mainstream fans from tuning out? How do you talk to your friends about sabermetrics without confusing them and looking like a nerd? It’s a tough balance to maintain, but I’ve found there are five guidelines that work well for me when talking with friends and writing articles.

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It’s funny how quickly we – and by we, I mean us fans – can shift our attention from one top prospect to the next. I like to call this phenomenon the “Shiny New Toy Syndrome”, as we become enamored with the Next Big Thing coming up from the minors and slowly forget the prospects we were falling for a week earlier. Prospects are showered with attention when they reach the majors and their performance is analyzed from 10 different angles. But once those players become established, they fall off the radar — and our attention shifts to the next big prospect. In many ways, prospects are like Christmas presents: anticipation builds until Christmas morning arrives; but within two weeks, the presents are forgotten and tossed in the toy bucket with everything else.

While Michael Pineda is currently dominating the prospect chatter, I want to shift our attention back to a top prospect who made his debut a little less than a year ago: Starlin Castro.

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If you’re any sort of saberist, you should already know that Weighted On-Base Average (wOBA) is vastly superior to On-Base Plus Slugging (OPS) at measuring offensive value. While OPS is a mishmash statistic, throwing together OBP and SLG for kicks and giggles, wOBA was created based on research on the historical run values of events. It weighs all the different aspects of hitting in proportion to their actual, real-life value to a team’s offense.

But how exactly do these two statistics differ in assigning value to events? See for yourself:

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Cleveland fans have a rough life. It seems like all their sports success is tainted with pain: the Cavs were good in the 2000s, but then LeBron James dissed them on national television; the Browns were good in the 1980s, but they consistently lost in heart-breaking fashion in the playoffs and have only had three winning seasons since; and the Indians were great in the late 1990s, but haven’t won a World Series since 1948. There are many markets in the running for the title of “most miserable fans,” and while I won’t go so far as to crown a winner, I think Cleveland has a case to be considered among the best (worst?) of them.

So it should come as no surprise that after the Indians’ hot start, which has included an eight-game winning streak and a sweep of the Boston Red Sox at home, some Cleveland fans are already talking about being buyers at the trade deadline and making a run for the playoffs. While obviously it’s waaay too early in the season to be making such pronouncements, is there reason for hope in Cleveland this season?

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If you haven’t taken a statistics class, regression can be rather tricky to grasp at first. It’s a word you’ll hear bantered about frequently on sabermetrically inclined websites, especially during the beginning of the season: “Oh, Albert Pujols is hitting .200, but it’s early so he’s bound to regress.” “Nick Hundley is slugging over .700, but that’s sure to regress.” This seems like a straightforward concept on the surface – good players that are underperforming are bound to improve, and over-performing scrubs will eventually cool down – but it leaves out an important piece of information: regress to what level?

The common mistake is to assume that if a good player has been underperforming, their “regression” will consist of them hitting .400 and bringing their overall line up to the level of their preseason projections. I like to call this the “overcorrection fallacy”, the belief that players will somehow compensate for their hot or cold performances by reverting to the other extreme going forward. While that may happen in select instances, it’s not what “regression” actually means. Instead, when someone says a player is likely to regress, they mean that the player should be expected to perform closer to their true talent level going forward.

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First of all, I’ve done a couple updates to the Library over the course of the past week. We now have a section there on Contract Details, which can be found under the “Sabermetric Principles” heading. It’s filled with articles discussing the details behind such confusing things like waivers, player options, service time, and Super Two status. The beginning of the year is always filled with lots of questions about how many options players have and when rookies can be promoted yet still delay their arbitration clock, so hopefully these articles are helpful for everyone.

Also, I’ve finished adding pages on Shutdowns and Meltdowns and the minus stats (ERA-, FIP-, xFIP-). If there are any other pages you’d like added to the Library, feel free to reach out to me on Twitter or through the contact us form on the right-hand side of all Library pages.

And now, since the minus statistics are all still rather new around these parts, below the jump I’m going to include some brief thoughts on ERA-, FIP-, and xFIP-, using number from the Aughts (2000-2010) as an example. Since these statistics are park and league adjusted, they are perfect for comparing performances across different years and leagues.

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