But this is bittersweet news. Yes, the wait until Spring Training is almost over, but the coming month and a half is typically the slowest, most painful time of the offseason. The Winter Meetings have passed and baseball news has slowed down to a crawl, so there isn’t much to keep us baseball-philes content. This January promises to be more eventful than most, considering Prince Fielder is still on the market and there are multiple potential trades that may happen, but I’m not about to set my expectations too high.

Since things can get so slow this month, this is typically the time of the year when I update and re-edit the Sabermetric Library — a mid-winter cleaning, if you will. I haven’t begun dusting out the cobwebs yet, though, as I’d love to get input on what people would like to see this time around. And so…

• Are these any pages in the Library you think badly need an edit? Is there anything you’d like to see added to any particular page?
• Are there any new pages or articles you’d like to see added to the Library?
• Any new links that I should be sure to include in the Library?

In short, if you have any ideas on how to improve the glossary here at FanGraphs and to make it more useful, please share! I’ll be spending the next month or so making edits and changes, and I welcome any ideas.

If we were to try and define power from the ground up, obviously you’d have to start with home runs. Power hitters are guys that mash lots of home runs, right? When I think power, I think of players like Jose Bautista, Babe Ruth, Hank Aaron, and Barry Bonds. Home runs are so flashy, they steal the show.

But there’s more to power than a player’s raw home run total. You can’t completely ignore other extra base hits, which is why there are statistics like Slugging Percentage and Isolated Power. Slugging Percentage measures a player’s total bases and Isolated Power measures a player’s extra bases*, so both statistics count doubles and triples as well as home runs.

*Quick refresher course for everyone. Slugging Percentage = Total Bases / At Bats ; Isolated Power = Extra Bases / At Bats

Or if you prefer to think about it another way, Jose Bautista has a .330 ISO this season. That means he averages nearly one extra base every three at bats. 

Both these stats have the same problem, though: not all bases are created equal. If a player has accumulated 30 extra bases in 100 at bats, isn’t there a big difference if those extra bases were accumulated through 10 home runs versus 30 doubles ? Both players have the same Isolated Power, but which one has provided their team with more value through their power production?

Good question, I’m glad you asked.

When trying to answer this question over at DRaysBay yesterday, I decided to bust out the weights included in wOBA. After all, Weighted On-Base Average weighs each type of hit in proportion to their actual importance for run scoring, so we should be able to use those coefficients to isolate power production. Here’s what I came up with:

wXB = (1.268 * 2B) + (1.610 * 3B) + (2.086 * HR)

I call it Weighted Extra Bases. It’s very simplistic (and not translated into runs form), but it essentially shows us how much value a player produces through their power. It’s using the same exact concept as both Slugging and ISO, except in this instance using coefficients that relate to value, not bases. Here’s a quick leaderboard for 2011:

This is a counting statistic, though, since players with more playing time have more chances to get extra base hits. Jose Bautista may look like he comes in second fiddle to Curtis Granderson, but once you adjust for playing time and opportunities, the difference vanishes. To adjust, let’s divide wXB by at bats:

For those of you following along at home, you may notice that those values look very close to ISO…and that’s because the formula’s for the two stats are very similar. Consider:

wXB/AB = [ (1.268 * 2B) + (1.610 * 3B) + (2.086 * HR) ] / AB
ISO = [ (1 * 2B) + (2 * 3B) + (3 * HR) ] / AB

When you actually weigh extra base hits in proportion to their value, it turns out that ISO is undervaluing doubles, while overvaluing both triples and home runs. This is why a player like Matt Holliday (33 doubles, 19 home runs) can have the 9th highest wXB/AB in the majors, yet only the 20th highest ISO and 17th highest SLG.

In all honesty, this experimental stat makes me appreciate ISO all the more. I never used to like ISO that much, due to the reasons I stated above, but this shows me that for all its simplicity, it’s a very effective shorthand way of conveying a player’s true power. It does undervalue players with extreme doubles totals — and overvalue players with lots of homers and few doubles — but for the most part it’s rather accurate. The largest disagreements between wXB/AB and ISO only move a player 15-20 spots in the rankings, while the largest differences between wXB/AB and SLG amount to something closer to a 40 spot adjustment.

If nothing else, I hope this helps people remember to look closer at a player’s double total as well when talking about power. Doubles are a lot more valuable than we typically assume, and they are undervalued by even the best publicly-available power statistic (ISO). I’ll have to mess around with wXB a bit more, but maybe this is a concept we can build upon.

Here’s a Google Doc workbook on wXB. Enjoy!

So the sabermetric truism has stuck: strikeouts aren’t that bad. Hitters can have high strikeout rates and still contribute loads of offensive value through their plate discipline. After all, the end goal is not making an out, right? It shouldn’t matter how a player does it, simply as long as they reach base at a high rate and avoid making outs.

But there’s a problem with this logic. While a player can be valuable even he strikes out frequently, strikeouts still decrease how often a player reaches base and can have an adverse effect on a player’s on-base percentage. They’re not as harmless as casual saberists typically assume.

Let’s walk through a quick thought experiment. Say we have a player, Zadock Bartlett, who has good plate discipline (10% walk rate) but strikes out a high percentage of the time (say, 25%). Zadock is going to reach base 10% of the time due to his walks, but when he doesn’t walk, ideally he would like to reach base as often as possible through a hit. And there are two main ways Zadock could boost his batting average: have a high percentage of his balls in play fall for hits (in short, have a high BABIP), hit home runs, or put the ball in play as often as possible.

That’s pretty straightforward, right? If a ball goes over the fence for a home run, nobody can catch it so it obviously goes for a hit. If Zadock has a high percentage of his balls in play fall in for hits — and some players can post BABIPs higher than league average on a consistent basis — then his batting average will obviously be higher. And if Zadock puts the ball in play more often, he’s giving himself more chance and opportunities for his balls to fall in for hits.

To put it another way, when a player strikes out, they don’t give themselves a chance to get a hit. The more often Zadock strikes out, the lower his batting average and on-base percentage will be…unless he compensates through posting a higher BABIP or hitting lots of home runs.

If we assume a league-average BABIP rate, here’s what batting averages we can expect from players based on their strikeout rate and home run total (per 500 PA):

Red = below average ; Blue = average ; Green = above average
League average strikeout rate = 18.4% 

As you can see, hitters only start getting into trouble if they’re striking out in over 20% of their plate appearances — specifically, above 25% is the danger zone. Once you cross that threshold, even if you’re mashing a large number of home runs and walking at an above-average clip, it gets difficult to post a high on-base percentage.

Here’s the same chart as above, only this time it’s displaying on-base percentage instead of batting average. It’s assuming a league-average BABIP rate and a 10% walk rate (above league-average):

Once you start striking out in 25% (or more) of your plate appearances, it’s difficult to post an on-base percentage higher than .340; you either need to be a very powerful or very patient one, or be able to post a high BABIP.

Of course, many of the hitters that strike out this often fall into one of these categories; most of them are powerful hitters that smash a large number of home runs and walk at a high clip as well. Problems arise when you’re a player like B.J. Upton: a player that hits for moderate power, but strikes out 25% of the time. With a slightly depressed BABIP (.275) and a strike out rate near 25%, his on-base percentage this season is a mere .310. And that’s despite having a 10% walk rate.

I don’t mean to suggest that saberists should reverse course and start telling everyone that strikeouts are the devil; I’m simply trying to provide some perspective, and to remind everyone that strikeouts do have a negative consequence. If I see a rookie posting a strikeout rate above 25%, I’m going to start getting worried about them unless they’ve also shown good plate discipline and power.* When you’re striking out that much, your margin for success shrinks.

*Well, or they’re an excellent fielder. Then they get some leeway as well.

Batting average is a statistic that gets too much love from mainstream fans, but it tends to get undervalued among saberists. Getting a hit is just as important as drawing a walk — you can’t have a high on-base percentage unless you do both. And as often as we point to BABIP as the cause of a high or low batting average, a player’s strikeout rate can have an influence as well.

The following is taken from the new FanGraphs Library page on SIERA, so it will always be available here whenever needed.

***

Skill-Interactive ERA (SIERA) is the newest in a long line of ERA-estimators. Like its predecessors FIP and xFIP, SIERA attempts to answer the question: what is the underlying skill level of this pitcher? Is this pitcher likely to be successful going forward or not? Based on their past results, how should we expect them to perform in the future?

But while FIP and xFIP largely ignore balls in play — they focus on strikeouts, walks, and homeruns instead — SIERA adds in complexity in an attempt to more accurately model what makes a pitcher successful. SIERA doesn’t ignore balls in play, but attempts to explain why certain pitchers are more successful at limiting hits and preventing runs. This is the strength of SIERA; while it is only slightly more predictive than xFIP, SIERA tells us more about the how and why of pitching.

Here’s what SIERA tells us:

Strikeouts are good…even better than FIP suggests. High strikeout pitchers generate weaker contact, which means they allow fewer hits (AKA have lower BABIPs) and have lower homerun rates. The same can be said of relievers, as they enter the game for a short period of time and pitch with more intensity.

Also, high strikeout pitchers can increase their groundball rate in double play situations. Situational pitching is a skill for pitchers with dominant stuff.

Walks are bad…but not that bad if you don’t allow many of them. Walks don’t hurt low-walk pitcher nearly as much as they hurt other pitchers, since low-walk pitchers can limit further baserunners. Similarly, if a pitcher allows a large amount of baserunners, they are more likely to allow a high percentage of those baserunners to score.

Balls in play are complicated. In general, groundballs go for hits more often than flyballs (although they don’t result in extra base hits as often). But the higher a pitcher’s groundball rate, the easier it is for their defense to turn those ground balls into outs. In other words, a pitcher with a 55% groundball rate will have a lower BABIP on grounders than a pitcher with a 45% groundball rate. And if a pitcher walks a large number of batters and also has a high groundball rate, their double-play rate will be higher as well.

As for flyballs, pitchers with a high flyball rate will have a lower Homerun Per Flyball rate than other pitchers.

***

If I had to explain SIERA concisely to a friend, it’d probably sound a little something like this:

If you want to know how well a pitcher is likely to perform in the future, SIERA is the stat for you. At the moment, it’s the most accurate stat for predicting a pitcher’s future ERA, and it’s also the best at modeling the complexity of pitching.

But for all its complexity, the theory behind SIERA is intuitive and easy to understand. For example, SIERA assumes that strikeouts are good, walks are bad, high strikeout pitchers induce weaker contact, wild pitchers allow more baserunners to score, and extreme groundball pitchers are better at generating easy-to-field groundballs.

SIERA is a fun new tool, and I can’t wait to see it in action. Have at it, world!

For even more information on SIERA, see its FanGraphs Library page.

As I have alluded to in the not-so-distant past, I feel like sabermetric writing should not be all the same. If you’re writing a piece that’s geared for other saberists — or for a very knowledgeable audience, like this one — then obviously very different rules apply than if you’re trying to cater your analysis to a broader audience. You can toss around multiple acronyms and discuss statistical concepts without much worry, while doing the same thing in other places could have you denigrated by your audience as a know-it-all, pompous jerkface.

We all love to poke fun at television announcers — whether at ESPN, MLBN, or elsewhere during game broadcasts — but they face a very difficult task: how do you give insightful analysis while still appealing to the wide range of different viewers out there? There are plenty of announcers out there that love stats and analysis (hello, David Cone!), and it’s no easy task to try and mesh those numbers into a game broadcast without scaring off all the viewers out there who don’t like math.

These same challenges apply to us saberists. What sort of an audience are we trying to reach, and how can we best do so? Today I want to suggest another way in which people can help make saber-stats easier to digest: rounding your numbers.

All too often, saber writers and bloggers neglect to consider the aesthetics of their posts. When I sit back and look at this piece, does it look like something that I would want to read? Does it have large blocks of text? Are there multiple acronyms in each paragraph? Are there too many links? As silly as some of these things may sound, all them influence whether people are going to read a piece or not.

And one of the worst offenders of aesthetics in sabermetric pieces are decimal points. Many of our traditional baseball stats are based around decimals — batting average, on-base percentage, slugging, etc. — and many of the new stats list out decimals to the point of insane specificity. Paragraphs end up looking like giant strings of numbers, separated by odd acronyms, making the piece dense and tough to access unless you really, really like sabermetrics.

So here’s my question: Does it really matter if I know that Evan Longoria has a 2.66 wFB/C, or is it enough to know that he has a 2.7 wFB/C? What difference does it make if we say someone has a 12% walk rate instead of an 11.7% walk rate? Is it sacrilegious to say someone has thrown 57% fastballs, when really it’s only been 56.7%? These are all very small changes, but they can go a long way toward making pieces more readable.

Of course, it all depends what audience you want to try and reach. I’m glad that FanGraphs lists out statistics to such detail, as it’s perfect for research purposes and allows people to get as specific as they desire, but we sometimes forget that it’s okay to take these numbers and round a bit (especially any percentages). Don’t round those that require that extra precision — I’m thinking of WAR and wOBA, specifically — but for the vast majority of stats, you should be able to make the numbers shorter and easier to read without losing any meaning.

So cut loose. Be wild. And be conscious of the audience you’re trying to reach.

Q: I like to flatter myself that I’m an ‘early adopter’ to the sabermetric perspective on the game, even though it’s been so many years since its introduction and uptake by those like yourself. Is sabermetrics already ‘mainstream’ in your mind, or how long do you think it will be til it is? What was / will be the tipping point to #2?

Tango: Sabermetrics will always be on the leading edge. There’s no need for it to be in the mainstream. If the mainstream wants to adopt, they know where to find us. If they want to ignore us, they can. We’re there to make sure they don’t misuse numbers, that’s all.

I hope [the tipping point] never happens, actually. You look over to your left and right to make sure that whoever wants to be part of the movement has the tools and knowledge to join in. There’s no sense in looking over your shoulder to make sure everyone comes along. They aren’t in a burning building they are trying to escape. They are on the beach, and they can decide if they want to come surfing with us or not. But I don’t need them to tell me that I’m drowning people with numbers. We’re giving out surfboards, and they can decide if they want one. And then we’ll be happy to make sure they don’t drown.

I couldn’t agree more, but I realize that might seem counterintuitive for those that have followed my recent Saber-Tips series here. A large part of my writing and work here seems geared at making sabermetrics more mainstream – or at least, more widely used – but that’s not my intention. Let me explain.

One of the most beautiful things about sports coverage these days is that you can have your baseball however you want it – as shallow and broad or as deep and specific as you’d like. And as Tango hit upon in his answer, the same can be said for sabermetrics – everyone is free to take as much as from sabermetrics as they want. If you’d prefer to skim the surface and learn enough to help slightly in fantasy baseball or discussions with friends, you can do that. But if you’d like to jump in with both feet, there are plenty of places out there to read detailed research as well. All the options are there, and you can pick from them as you choose.

But here’s where my point comes in: I feel that for the longest time, sabermetrics has largely been a research-driven field. Up until the last few years, most of the writing about sabermetrics was done by people that were doing unique research and trying to push the field forward in terms of knowledge. It was a very small collection of people that were in on this, and everyone knew about the stats and concepts you were talking about.

And since then….bit by bit, sabermetrics has become democratized. Fire Joe Morgan made it fun and humorous to care about advanced analysis, and FanGraphs came along and made these saber-stats available to everyone for free. And over the past few years, the online baseball blogosphere has really solidified into a strong, vibrant mass of awesomeness. Now, not only can you find a blog specializing in, say, the Tampa Bay Rays or Milwaukee Brewers, but you can also find blogs on those teams that focus specifically on sabermetric stats and analysis.

As more people are writing about saber concepts, the field is evolving: there is still the collection of researchers that do yeoman’s work and move the field forward (and get hired by teams in the process), but there’s a larger, growing group of people that couldn’t run a t-test if their lives depended on it, but know enough about saber-stats to use them in their writing. As opposed to being saberists (a la Tom Tango), they’re saber-writers (like Rob Neyer or Joe Posnanski, for instance).

There’s no shame to being in this category; I consider myself a saber-writer, and there are lots of people still doing insightful analyses even though they don’t necessary fall under the heading of “researcher”. But I do think that even though more people have become saber-writers more than researchers, the language we’ve used around sabermetrics has remained largely static. We’re talking like researchers and peppering articles with acronyms and decimal points, yet we’re trying to reach a different audience.

In the end, it’s all about finding the right balance to best reach your audience. This is something we all have to worry about, whether we’re writing research pieces, writing on a blog or mainstream site, or just explaining things to a friend. What audience are you trying to reach, and how can you best convey your point to them? As Bob Costas pointed out in a recent interview with Joe Posnanski, he approaches each of his appearances differently depending on what audience he’s appearing in front of (which is one of the reasons it’s tough for announcers to include saber stats and analysis in their game broadcasts).

Sabermetrics will never be mainstream. But to use Tango’s metaphor, there should be some gradient between lying on the beach and surfing in water 20 feet deep. I like to reach out my hand and help people swim deeper, but other writers reach out to other niches. All I ask is that you be aware of which niche you’re trying to reach, and not only think about using saber-stats correctly, but consider how to use them in a way that will be easier for your audience to digest. If you’re not a researcher, do you need to include a decimal point in a player’s swing rate? Is it that important to use ISO instead of Slugging? I don’t think so.

Runs are awarded to base runners in the same way they are rewarded to outfielders on “arm” plays. The average run value in terms of the base/out state is subtracted from the actual run value (also in terms of the resultant base/out state) on a particular play where a base runner is involved. The result of the subtraction is the run value awarded to the base runner on that play.

If you didn’t understand that, a simple example should explain it clearly:

Let’s say that there is a runner on second and one out. A ground ball is hit to the SS. Let’s say that on the average, in that same situation, the runner advances safely to third and the batter is thrown out 20% of the time, he stays put 70% of the time, he gets thrown out at 3rd 5% and beats a throw to third 5% of the time (batter safe on a FC). And let’s say that average base/out run expectancy (RE) of all those results, weighted by their frequency of occurrence, is .25 runs (all the numbers are made up). If the runner advances and the batter is thrown out, and the resultant RE is .5 runs, then the runner gets credit for .25 runs (.5 minus .25). If he stays put, and the average RE of a runner on second and 2 outs is .23 runs, then gets “credit” (he gets docked) for -.02 runs (.23 minus .25). So basically a runner gets credit for the resultant run value of what he does minus the average weighted resultant run value of all base runners in that situation.

Here are most of the situations where a base runner gets some kind of positive or negative credit. Obviously more than one base runner can get credit on any particular play.

1) On a hit, advancing an extra base, not advancing an extra base, or getting thrown out trying to advance an extra base, as long as no other base runner is blocking an advance.

2) A batter getting thrown out trying to advance an extra base on a hit (if he successfully does, we don’t know it, as he is simply awarded a double, for example, on a usual single where he advances an extra base).

3) On a hit, the batter advancing, not advancing, or getting thrown out when a runner is safe or out advancing an extra base.

4) Trailing runners advancing, not advancing or getting thrown out when a leading runner is safe or out trying to advance an extra base on a hit or an out. This is basically lumped together with #1 above.

5) Runners trying to advance on fly ball outs – i.e. tagging up.

6) As mentioned above, on ground balls to the infield, runners on first staying out of the force or DP at second base, whether the batter is out or is safe on a FC.

7) Also as mentioned above, a runner on second advancing or not (or getting thrown out) on a ground ball hit to SS or 3B.

Runners on third base advancing, not advancing, or getting thrown out at home on a ground ball are not considered (on air balls they are). Runner advances or outs on WP or PB are not considered either.

All of these situations are considered an “opportunity” for the base runner or the batter (except when a batter gets a hit, he is not awarded an opportunity unless a leading runner tries to advance an extra base and the batter has an opportunity to advance on the throw).

As with UZR, a player’s “games” are not his actual games played. They are his opportunities divided by the league-average opportunities per game. No adjustments are made for how often a player typically gets on base (or how often subsequent batters put the ball in play or get hits), such that a player with a high OBP will likely have more “games” than actual games played and a player with a low OBP will likely have fewer “games” than actual games played.

On batted balls to the outfield, only whether it was hit to LF, CF, or RF is considered, not the depth or actual vector or zone within the three outfield positions.

Future versions of UBR will likely adjust “games” for a player’s OBP and will also likely include more outfield location zones. These upgrades are not likely to significantly change the numbers.

When you stop and think about it, despite the numerous baseball statistics out there, there are only a few limited ways of talking about a batter’s power. While there are a multitude of options when talking about plate discipline — On-Base Percentage, walk rate, outside swing rate, etc. — there are only a handful of widely available stats to use for power: the old standby, Slugging Percentage; a player’s raw total of homeruns or extra base hits; or the sabermetric alternative, Isolated Power.

So when you want to talk strictly about how powerful a player has been, which stat do you use? There are pluses and minuses to each of these stats, but do any of them necessarily stand out from the others? I’d argue no.

Slugging Percentage

The Good: Everyone knows it. It’s simple, easy to understand, and we all grew up using it.

The Bad: Like mentioned above, the formula of Slugging Percentage is very simplistic; it’s a player’s total bases divided by at bats. But if we’re talking about power, why are we including singles in the calculation? And if we’re putting value onto extra base hits, is a homerun worth twice what a double is worth?

Raw Homerun or Extra Base Hit Totals

The Good: Using the raw totals don’t attempt to place any value on each of the different hits, like Slugging Percentage does. Also, baseball fans don’t need to be told that 30+ homeruns is very good.

The Bad: Homeruns is just one part of the picture; doubles and triples are also very important. And if you list a player’s total extra base hits, you then run into the problem that you’re considering doubles as important as homeruns. Who’s the more powerful batter: someone who hits 30 doubles and 0 homeruns, or someone that hits 25 homeruns and 5 doubles?

Isolated Power (ISO)

The Good: It’s also a very simple statistic: Slugging Percentage minus Batting Average. This corrects for one of the flaws of Slugging Percentage, since this subtraction removes singles from the equation and leaves just the extra bases. As a result, Isolated Power gives more value to hitters that accumulate lots of extra bases but don’t hit for a high batting average.

The Bad: It’s on a funky scale. It’s a three-decimal stat, so I expect it to be on the same sort of scale as OBP or AVG, but it’s not. Instead, an average ISO score is around .145 and power hitters normally crack .200. It took me a long time to feel comfortable enough with the scale to begin using it in my writing, and new readers could have a problem with it.

***

I’ve gone back and forth on this question. Do I use a familiar stat, like Slugging Percentage, or do I go with Isolate Power — a stat that’s slightly more rigorous, yet is on a confusing scale for new readers? Believe it or not, the two stats correlate at a very high rate (.90 so far this season), so in general, you’re not losing much in terms of accuracy if you choose to use Slugging Percentage instead of Isolated Power. ISO is still an important statistic to use, since it can show you if a player might be over- or under-rated due to Slugging Percentage, but in the majority of cases the difference between the two stats isn’t as large as you may think. Power isn’t a very subtle skill; it tends to shine through no matter what lens you look at it through.

If you’re looking for a more rigorous alternative to Slugging Percentage, though, I’ve recently started looking at a player’s percentage of extra base hits. Consider:

Both these percentages tell you slightly different things — one tells you how often a player gets an extra base hit when they step to the plate, while the other tells you how often a player rips the ball deep when they get a hit — but they can both be useful when trying to get a full picture of a player’s power. Instead of limiting ourselves to just the handful of stats out there right now, why shouldn’t we use percentages like this, much like we’d use walk rate as an alternative to OBP? Both these stats correlate a high amount with Isolated Power (.91 for XBH/AB and .83 for XBH/H), so you know that they’re telling you similar information, but just in a different form.

I probably sound like a broken record, considering I finished the BABIP article on a similar note, but which stat you use depends on your audience and what questions you want to address. So don’t limit yourself to only Isolated Power; Slugging Percentage shouldn’t be throw out in the wash, as it has its many positives going for it as well. And don’t be afraid to branch out into some of the different percentage stats; they take but the work of a minutes to calculate, they are easy for new readers to understand, and they add nuance to your analysis.

Batting Average on Balls In Play (BABIP) is one of the mainstays of sabermetric analysis. In fact, I’d suggest it’s one of the most commonly used saber-stats; it’s important whether you’re talking about batters or pitchers, and it’s useful in explaining why players aren’t performing as you’d otherwise expect. If you’re trying to analyze a player and talk about how they will perform going forward, how can you not talk about BABIP?

But despite being such an important statistic, many people are initially skeptical of BABIP. What do you mean to tell me that batters don’t have control over where they hit the ball? Why should I believe that there isn’t a large amount of skill involved in BABIP? To say that there’s a large amount of variation and luck involved in BABIP (and therefore, batting average) seems counterintuitive to people. After all, many baseball fans grew up with the idea that hitting for a high average is very much a skill, not the product of skill and some luck.

So recently, I’ve started trying something a little bit different: presenting BABIP as a percentage. And so far, I think it’s helping.

In other words, instead of writing out a sentence like, “Carlos Santana has a .233 BABIP — much lower than his.277 BABIP from last season — suggesting that his batting average should increase going forward,” I’m starting to write my analyses like so:

When Carlos Santana has put the ball in play this season, he’s only had balls fall for hits 23% of the time. The league average rate for a hitter is normally around 29-31%, while Santana had 28% of balls in play fall for hits last season. Since hitters have little control on if they hit a ball right at a fielder or slightly in the gap, Santana should have more balls fall for hits going forward and therefore, increase his batting average.

I think by using the percentage you accomplish two main things: you rid your article of an acronym and a decimal-heavy stat (both of which can turn people off), and you disconnect BABIP from batting average. As we mentioned above, people grew up thinking of batting average as a skill-driven stat, so when they hear “Batting Average on Balls In Play”, their implicit assumption connects the stat with a skill. Why shouldn’t better players have higher BABIPs? And why shouldn’t better pitchers have lower BABIPs against them? When you’re used to thinking of batting average as a skill, it’s tough not to automatically associate BABIP with skill too.

Also, our normal language surrounding BABIP reinforces that skill connection too. “Carlos Santana has a .222 batting average; he has a .233 BABIP.” It is something he has done, acquired as a result of his skill and performance. But when you use a percentage instead, your language becomes more passive and you imply a sense of uncertainty. Instead of saying a player is actively “hitting” or “produced” a .350 BABIP, you’re saying that 35% of his balls in play fell for hits. It’s no longer the hitter that’s driving these balls in play; it’s simply some balls fell in while some didn’t. Your semantics are matching up with the purpose of the statistic, and helping the reader better understand your point.

In the end, you should present BABIP however you think best serves the audience you’re trying to reach. At a site that’s already saber-heavy, it’s obviously fine to use BABIP since most readers would already understand the stat and it makes your articles more concise. But if you’re trying to reach out to a more mainstream audience, or trying to explain BABIP to someone that’s never heard of it before, it’s not a bad idea to slide that decimal point over two places and then round. Using a percentage instead of BABIP does more justice to the concept linguistically, and you might find your audience more immediately receptive to your point.

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.

This is common sense to anyone that’s played fantasy baseball, but some statistics are more fluky than others.  Even very casual baseball fans can recognize that ERA and Wins bounce up and down from year to year, and players’ batting averages fluctuate like crazy over the course of a season. And while some statistics shouldn’t be trusted even over the course of a full season, there are some statistics that stabilize quite rapidly. Thanks to research by Pizza Cutter (which can always be found in the FanGraphs Library), we can see that there are four statistics that have stabilized so far in 2011 for most players: swing and contact rates for position players (50-100 PA), and strikeout and groundball rates for starting pitchers (150 BF).

When I say “stabilize”, I don’t mean that these rates won’t change at all over the remaining course of the season. Instead, all it means is that once a player approaches these sample sizes, you can consider that there’s something more than just random variation going on: there’s some underlying change in a player’s approach/skill level/process/etc. in play as well. Matt Garza isn’t guaranteed to finish the year with a 12 K/9 rate because his strikeout rate has “stabilized”, but at the same time, I wouldn’t be surprised if his final strikeout rate is higher than what it’s been in the past.

With this in mind, let’s take a quick look at some of the early season standouts in each of these stats:

Swing Rate – 50 PA

Jose Bautista is only swinging at 33% of pitches thrown to him, which isn’t all that surprising considering that pitchers are acting like he’s the reincarnation of Barry Bonds and only throwing him strikes 34% of the time. But Bautista doesn’t even have the lowest swing rate in the AL; that belongs to Carlos Santana at 31%. This can’t be a good long-term strategy: while Santana is walking a lot, he’s also getting thrown strikes 43% of the time and striking out at a higher rate than last season. His plate approach still needs some refining.

On the other end of the spectrum, Vladimir Guerrero is giving new meaning to the phrase “swing at anything”. He’s swinging at 64% of pitches he sees, which is crazy high even for him (career 60% swing rate). Other players with notable high rates: Alfonso Soriano (58%) and Robinson Cano (57%).

Contact Rate – 100 PA

The list of players with low contact rates shouldn’t surprise anyone. Adam Dunn, Carlos Pena, and Nelson Cruz have all made contact on only 66% of their pitches, but that rates isn’t a large aberration for any of them; they’re simply sluggers that strike out a lot. Mike Stanton is looking to join their group, though, making contact on 68% of the time.

Where aren’t any big surprises on the other side of the list either. There’s nobody dramatically performing better than expected, and the list if topped with slap hitters like Michael Brantley, Ichiro, and Denard Span.

Strikeout Rate – 150 BF

Now we switch over the pitchers, and there are immediately some odd results so far this season. Matt Garza leads the majors with nearly 12 K/9? While Garza has always had the stuff to be a dominant pitcher, he’s never struck out more than 8.3 per nine over the course of a season before. And on the opposite end of the spectrum, there are a number of pitchers with low strikeout totals so far this season. Wade Davis, Clay Buchholz, and Jordan Zimmermann are all young starters that have struck out over 6 batters per nine in past seasons, but are averaging only around 4.5 strikeouts per nine (or less, in Davis’ case) this season. These pitchers are just barely over the 150 BF threshold, but I’d keep my eyes on them just in case.

Groundball Rate – 150 BF

As if there wasn’t already enough reason to be worried about John Lackey, it turns out his groundball rate has plummeted this year from 45% to 33%. Meanwhile, his rotation-mate Jon Lester has increased his groundball rate for the third year in a row, bringing it all the way up to 58% so far this year. The largest increase in the majors, though, comes from the enigmatic Charlie Morton, who has increased his groundball rate from 47-50% all the way to 64%.

Are all of these players with dramatic increases or decreases in their stats going to continue to perform at this rate over the rest of the season? No, of course not. But in each of these cases, the sample size has grown large enough that we can realistically consider that their skill level may be different than what we originally projected for them this season. Only time will tell in all of these cases, but don’t ignore the early returns. There’s value to be found in them if you look in the right places.