Last week, I discussed the two questions you should ask when you are trying to evaluate any problem with numbers, whether in the world of baseball or the so-called "real world" (like there's a real world outside of baseball). Here is what I said about evaluating a player's total performance.

As a a result, the takeaway here is the following: if you want to measure how productive a player is (and this is generally what people mean when they ask about how "good" a player is), you need to find a measure that converts all of his offensive and defensive performances into runs, which can then be converted into wins.

I then said that we would tackle how to approach turning a player's production into runs this week, and that is exactly what we'll be doing here. But why do we need a "sabermetric" method for dividing up runs? Why can we not just use the classic staple of runs and RBI in this case? After all, they have the right units, right?

In order to clarify this question, we need to go back to our previous discussion and ask those two pertinent questions again: 

1. Always ask yourself what question am I trying to answer and what stat can I use to answer that question before discussing.

Let's apply these to a basic baseball situation.

Side Note: An Example of Why RBI and Runs Are Limited

Consider this set of plate appearances occurring in the first inning of a Marlins game.

1. Emilio Bonifacio leads off with a single up the middle.

2. Omar Infante doubles down the left field line. The ball hangs up in the air a bit, so Bonifacio had to hold just long enough that he ends up at third base instead of scoring.

3. Hanley Ramirez hits a sacrifice fly, scoring Bonifacio from third.

The question is, who gets credit for this run and how much credit?

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I mentioned earlier in one of my introductory pieces that I would occasionally drop an explanatory article that went into some of the thinking process behind the sabemetric stats that are commonly used on this site. Of course, sabermetrics is not so much about advanced statistics as it is about a way of thinking about baseball and many other subjects. In fact, while people may think proponents of sabermetrics believe they have all the answers, sabermetrics is really more about asking questions.

With that in mind, there are two questions that I want you to consider whenever you choose to use a statistic for whatever reason. These two questions should guide how you use any stats, whether they be things you see on ESPN.com or the stuff you see on FanGraphs. These two questions are important any time you want to discuss something about a player or a team.

1) What question am I trying to answer?

2) What stat or observation answers that question? 

These may sound simple, but they are at the heart of any argument about almost anything, including our favorite national pastime. Let us consider an example.

Question: What player gets the most hits on average?

If this is the question you want answered, the answer should not be difficult to find. There happens to be a statistic, batting average, that answers this exact question, provided we define some of the questions parameters. Let us assume that this question wants to find out which player got the most hits given a certain number of opportunities between 2009 and 2011. Among players with at least 1000 plate appearances (PA), here are your top three batting averages:

1) Joe Mauer (.333)
2) Miguel Cabrera (.332)
T3) Ryan Braun (.318)
T3) Joey Votto (.318)

That is a simple question with a simple answer. Now, let us try a different question.

Question: What player makes the fewest outs on average?

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Note: This is a very basic introduction to the numbers we will use on this site. Every week, I will go into a more in-depth discussion about how these numbers are derived and what they really mean. Right now, we all just want to talk about the Marlins, so I will not yet spend too much time on this. At the end of the piece, there are three takeaways that I'd like for everyone to get. If you Fish Stripers can get those three takeaways, you won't have to worry about the rest of this for now.

Later today, I am going to begin discussing in detail the various aspects of yesterday's plan for offseason success. In the process, I will be throwing in some numbers that will regularly be used on the web site. But do not fear! These are not numbers that should scare anyone. Like many aspects of sabermetrics, these numbers have a firm basis on logic behind them and are useful for telling us something about players that we perhaps did not know. In the end, numbers and observations are the tools we use to enlighten us about the game of baseball. With both our eyes and the numbers on our side, we can learn a lot about the game.

What will follow is a brief introduction to some of the numbers that we will be using here on the site. There will also be various links to simple explanations about these statistics, so that if you want a better grasp on what is going on, you can find out.

Before we go into that, I would like to point out a part of the links section that was not previously there.

Click to embiggen

This set of links will provide tremendous assistance towards understanding some of the statistics that are used to talk about the Marlins here. Resources like Alex Remington's great Everything You Wanted To Know series on Big League Stew and FanGraphs' Sabermetrics Library are excellent starts on the way to learning more about the numbers.

However, if you just want to hear what these silly acronyms and numbers mean, let me summarize them for you.

Some Acronyms You Might Want to Know

OPS: On-Base Plus Slugging. We know that on-base percentage and slugging percentage are good. Mashing them together allows us to have a measure of both numbers at the same time for comparison purposes.

OPS+: All "plus" metrics are designed to be adjusted two things: league and park. There are park adjustments made to the numbers, and those numbers are compared to the league average. In OPS+, a number greater than 100 implies a percentage better than average, and vice versa for less than 100.

wOBA: Weighted On-Base Average. Here's the problem with OPS. For one, it doesn't take a math genius to figure out that adding two fractions with different denominators does not yield a "real" number. Secondly, because OBP and SLG are on different scales, OPS does not properly weigh those numbers; it overvalues SLG and undervalues OBP. How then can we put together the total value of a hitter? How can we resolve that issue?

Enter wOBA!

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