Advanced Stats 101

sabermetrics
There are several points of contention with baseball fans. Whether or not the DH should be in both leagues or be banished altogether, the use of the sacrifice bunt, and the use of instant replay. These are hot topics in and around baseball from the front offices to the beat writers to the fans. However, there may not be a bigger argument in the game than the use of advanced statistics, aka sabermetrics.

Stats have always been a part of the game. They are used to measure a person’s career as a whole, their value in terms of compensation, and Hall of Fame voting by the writers. Stats are as common in baseball as ratings are in television or radio. Almost any knowledgeable fan can tell you that Roger Maris hit 61 home runs, Hack Wilson’s record for RBIs in a season is 190, or Bob Gibson had a 1.12 ERA in 1968. Stats are a part of the game, and the history of baseball.

Now we come to advanced stats. There are stat-heads who embrace sabermetrics, like many of us at SDI, or the traditional guys who don’t need them, like Tom Grieve and many other broadcasters around the league. One thing about advanced stats is that they are used in every front office in baseball. The Jon Daniels’ and Alex Anthopolous’ of the world live and breathe value, which is what advanced stats contribute to measuring appropriately. As we said, “contribute”, they are not the be-all, end-all of evaluating players. You still need the eye test that scouts like Jason Parks, Don Welke, Jason Cole and Kevin Goldstein provide teams or readers.

For some, advanced stats can be confusing, scary or they just plain don’t make sense. For others they are useless, because batting average, slugging percentage and runs batted in have been used for over a hundred years, so why change? For others, they are a way of life. A way to separate close players, like the AL MVP battle last year between Cabrera and Trout.

Our thought here is to help the casual fan grasp sabermetrics on a small scale. This article isn’t for our buddies Philip Brown, Dustin Dietz or Kazuto Yamazaki or anyone who is a stat-head. We are going to give you a handful of advanced stats here. We will explain what they are, how they are calculated (if we can), what they provide, and how they can help you in evaluating a player.

WAR (Wins Above Replacement)
The basic idea of WAR is to provide the overall value of a player, if the team he plays for would have to replace him with an “replacement-level” player, or the equivalent of a Triple- A call-up. The thing about WAR that many forget, is that it is a  theoretical equation. There is not a way to determine a specific value for a “replacement-level” player. It is an average by which the baseball community equates the “zero” or starting line in WAR. When you think of a replacement-level player, just think in terms of a AAA players or a players on the cusp of the majors, and that is your starting point for comparing players by WAR.

There is bWAR (Baseball-Reference.com), fWAR (FanGraphs.com) and WARP (BaseballProspectus.com). We are using bWAR in our exercise today to limit the confusion. Before we go on, the equation to calculate WAR by either of the three websites is complicated so we won’t go into that actual way to calculate it. However, we have averaged the WAR of all three websites here.

Let’s take Nelson Cruz and his 2010 season to use as a baseline:

. Games PA AB Runs Hits 2B 3B HR RBI SB CS BB SO BA OBP SLG TB WAR
. 108 445 399 60 127 31 3 22 78 17 4 38 81 .318 0.374 0.576 230 3.8
Looking at the above, Nelson Cruz was worth 3.8 wins for the Texas Rangers. This means that Nellie provided 3.8 wins more than a Triple-A player would have provided the Rangers over the same time period, or to simplify, Cruz (in value) was worth 3.8 wins. WAR is a great, and I mean great stat to help gauge a player. However, it is not the end of a conversation about a player, as Dave Cameron of  FanGraphs.com said in a recent article. But, it should be where a conversation begins, because, as said earlier WAR is a theoretical equation.

“WAR is necessarily an approximation and will never be as precise or accurate as one would like.”-Baseball-Reference.com

BAbip (Batting Average on Balls in Play)
BABIP is a useful tool for both hitters as well as pitchers. For hitters, it is the average of hits a players gets when he puts the ball in play (in fair territory and in the ballpark). There are some who think this particular stat is a gauge of luck, while others think it’s a tool to measure the skill of a player at the plate. BABIP shouldn’t be used to define a player’s success at the plate, since hard hits balls right at a defender lower his average, or a seeing-eye grounder raises his average. Luck does factor to into the equation, however, BABIP is used to show a trend of a player’s success. You can get a feel for what a player is doing when you use BABIP AND a few other standard stats in conjunction. Let’s take David Murphy for example:

“Murph” in 2011 had a batting average of .275 in 404 AB’s, and a BABIP of .299. So, anytime David put a ball in play, fair and in the park, he hit .299. In 2012, David had a batting average of .304 in 53 more AB’s (457) and had a BABIP of .333. So, what does this mean? 

Basically it boils down to the fact that either Murphy got a lot more lucky in 2012, or that he made more solid contact or a combination of both. The fact is, he had 15 more doubles in 2012 than the previous year, which would likely mean that Murph made more solid contact than in 2011. If you had not seen David Murphy play at all in 2012, your initial thought would likely be that Murphy had a tremendous year at the plate – and he did. He hit left handed pitching very well, even though he only had 75 AB’s against LHP, and a BABIP of .433. Again, using BABIP as a standalone stat, we wouldn’t know if David’s 26 hits vs LHP were flares, Texas Leaguers or hot shots in the gap. Having said that, 75 AB’s is a very small sample size, and hard to judge a performance, but it does show a trend when used with other stats.

For pitchers, BABIP is used much the same way, as it shows how many hits a pitcher is giving up. One thing to remember when using BABIP to measure a pitcher is that the range of the defenders behind him can greatly affect him. For example Max Scherzer had a BABIP of .337, while Matt Harrison had a BABIP of .285. Their stats are pretty close to compare, but not identical. But if we were to dig deeper, the range factors of Moreland, Beltre, Kinsler and Andrus are much better than Fielder, Peralta, Cabrera and Infante. Again, BABIP is not a standalone stat, but a great way to help measure the performance trend of a player.

Here are a couple of our favorite advanced stats at SDI. We hope this helps anyone scared or
confused by Sabermetrics. These stats are there to help us understand the game beyond batting average, home runs, RBI’s and slugging percentage. For those of you that like the traditional way of judging a game or players, we are not here to persuade you. There is something about the eye test that still holds water. For those of you that are interested in the advanced statistical categories, and do not have a complete understanding, this article was for you. We’ll have a few more up throughout the next few weeks.

Patrick Despain is the CEO and Co-Founder of ShutDown Inning. He can be reached atPatrick.Despain@shutdowninning.com or on Twitter @ShutDownInning
Patrick Despain
Patrick is a member of the IBWAA and creator of Shutdown Inning. He was raised him Arlington, Texas and grew up watching games on HSE and listening to Eric Nadel and Mark Holtz on the radio. He is a long time Rangers fan and never achieved his dream of being a bat boy. He know lives in Georgia with dreams of a Texas return.

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