Base running linear weights or base running runs, or Ultimate Base Running (UBR), is similar to the outfield arm portion of UZR. Whatever credit (positive or negative) is given to an outfielder based on a runner hold, advance, or kill on a batted ball is also given in reverse to the runner (or runners). There are some plays that a runner is given credit (again plus or minus) for that do not involve an outfielder, such as being safe or out going from first to second on a ground ball to the infield, or advancing, remaining, or being thrown out going from second to third on a ground ball to SS or 3B.

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.

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In case you missed the first article in this series — in which I talk about another way to look at BABIP — I’m trying to take a look at alternative ways to present sabermetric stats, in order to best represent them to an audience.

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.

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Through some conversations with colleagues, I’ve recently had a bunch of thoughts floating around in my head about how to best present sabermetric stats to an audience. I posted some of these thoughts recently in an article, and I’m planning to continue listing tips every now and then. And of course, a bit thanks to Sky Kalkman’s old series at Beyond the Boxscore for the title inspiration.

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.

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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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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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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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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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Baseball Info Solutions has just released a more comprehensive FAQ on their fielding system, which we list on FanGraphs as DRS (and the various components that make up DRS).

It goes into details about how they make adjustments for various positions, ball hogging, home runs saved, the Green Monster, player positioning, etc….

Click to read the FAQ

Index

1. Introduction
2. Fielder Credits and Debits
3. Batted Ball Types
4. GDP and Arm
5. Base Runner and Outs Adjustments
6. Other Infield Positioning
7. Outfield Positioning and “Wall Balls”
8. Infield Line Drives and Pop-ups
9. Park Adjustments
10. The Baselines
11. Recent Revisions
12. Sample Size and Reliability
13. UZR and Aging
14. Consistency from Year to Year
15. Does UZR tell us what actually happened on the field?
16. Conclusions

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