Greetings, MMO. Regulars will recognize my posts, but this is my first blog. Thanks to Joe D. and the MMO hierarchy for allowing me the opportunity.
I’ve been brought on mostly due to my understanding of sabermetrics, and as the Mets move forward into a new era with a new front office and new theories and ideas, it is my hope that I can be somewhat of a liaison between what you hear and what you know. My ultimate goal is to provide Mets fans with traditionalist values and a clearer understanding of what this new regime is all about.
Since the off season is still in its infancy and Sandy Alderson had been very unfortunately otherwise occupied this past week, there’s nothing specific going on in Mets land right now roster-wise. So, through a multi-part blog post, I thought I’d introduce both myself, my ideas and some of the more common sabermetric theories, ideas and statistics to you before the ball really gets rolling.
Sabermetrics has been defined many ways by many different people, but I like to think of it as an “objective analysis of statistics.” To me, this means that it’s not enough to look at a player’s RBI total, batting average, ERA or win-loss record to determine whether or not the player is productive. You have to look at WHY the player has those numbers. Baseball is a team game down to its very core, and often, stats have more to do with the contributions of one’s teammates than they do with said player’s contributions. There are deeper sabermetric principles that can sufficiently determine the exact contribution in terms of runs or Win Shares. Those formulas are for a later date. For now, we’ll stick to the basic principles.
The most basic and important stat to a sabermetrician is on-base percentage, because the most basic and important principle is that the best thing a batter can do is reach base. You only get 27 outs, and the more outs you make, the more you are detrimental to your team’s success. The simple fact that you can’t score a run without there being a base runner lends itself to the importance of OBP. If you were to research the league rankings in OBP and runs scored in any given year, for either league, a team’s place in the league in one category will be very, very similar to its place in the other category a vast majority of the time. But make no mistake; OBP is not the be-all end-all stat. It’s not the ONLY important stat, but it is the MOST important.
Problems With Rate Stats
There are innate problems with most rate stats that attempt to define a hitter’s value. There’s just too many variables to find one comprehensive statistic that measures every little thing that can happen. The best we can do is use the stats that quantify what is most important and/or use the stats that measure as many of the important variables as possible. One stat to stay away from is batting average. Batting average is archaic and full of misinformation, as it attempts to fool us into believing all hits are created equal. Obviously a home run is better than a single, but batting average doesn’t seem to think so. BA also doesn’t account for times reached base other than hits; it’s not really a useful stat by any means. Batting average is to player evaluation as backseat cup holders are to cars. It’s nice if it’s there, but really, what’s the use?
SLG% is probably the second most important rate stat available, but I like to think of it as OBP’s understudy. It definitely serves a purpose and is important to production, but without OBP, it doesn’t really have much meaning. That’s because SLG% doesn’t account for the rate of reaching base, or not making outs. It also pretends doubles are twice as valuable as singles and home runs are twice as valuable as doubles. Not true by any means. OPS attempts to combine the two to incorporate everything, which is a great idea, but not exact in it’s nature. Don’t take it as gospel because it weights OBP and SLG% the same, and that’s also a false notion. A player with a .400 OBP and a .400 SLG% is far more valuable than a player with a .350 OBP and a .450 SLG%, despite the same OPS. OBP is not perfect all by itself because it does not account for the weight of the manner of reaching base.
That’s why I like wOBA (weighted on-base average). I like this the best because of the “weighted” aspect. Possible outcomes of an at bat are weighted by the amount of runs the team is expected to score as a result. Those run amounts were determined by people far smarter than me analyzing game data from hundreds of thousands of games over the course of baseball history, so it’s a pretty precise determination.
How wOBA works is the formula adds together the products of all the most common possible outcomes of an at bat and their expected runs, and that sum is divided by plate appearances. This number tends to resemble batting average, and we don’t want that, so we simply add 15% to make it read more with a better stat, OBP. The reasoning behind that is because league-average hitters would generally have around a .300 wOBA. That’s a nice number for a batting average, but a terrible number for an OBP. We add that 15% to make wOBA resemble OBP because OBP is a truer measure of a player’s value. The wOBA stat reads like OBP does. For instance, .335 is average, .380 is great and .300 is poor.
Run Values Determined
wOBA can be turned into a run value very easily. Bill James was the first to attempt to quantify a hitter’s contribution on a scale of runs, and cleverly called this stat “runs created.” It served as a much better alternative to looking at an entire stat line of singles, doubles, triples, home runs, walks, etc., because all that gave you was a bunch of numbers with no connection to how they helped score runs. Call me when one team beats another nine hits to six, or four doubles to two. As long as it’s determined who wins by the amount of runs scored, every stat or number should be viewed in relation to the amount of runs they produce. Runs Created attempted to combine those singular numbers together to one useful, easy-to-read stat. “Player X was worth 32 runs last year.” Simple.
James’ idea has since been surpassed by wRC, which uses wOBA instead of mere stat totals because it’s more accurate to weigh the stat totals to the runs they’re expected to result in. wRC is good for determining the total runs created, but it’s not a tool for comparing one player to another or to the league average (to see how dominant a player actually was). I usually glance at wRC, but refer mostly to wRAA (weighted runs above average) for a better understanding of how a player did compared to his league.
This is a very simple formula. Just take the difference between the player’s wOBA and the league average wOBA and divide that by his plate appearances multiplied by 1.15 (remember the 15% added above to get the wOBA). This number is a player’s runs above average and a great stat for comparing players to each other and to the league.
The best part about these stats (wOBA, wRC, wRAA, OBP, etc.) is that you don’t have to do any of the difficult calculations. The geeks have already done it for you. Just mosey on over to your favorite stats website (I use Fangraphs, they have everything) and look it up.
The path to enlightenment is with understanding. If you can understand and accept why certain stats are more telling than others and how to identify them, you’ll have a better, more intimate knowledge of the game. There’s more on offense, but stay tuned for a blog on pitching metrics.
About the Author: Jesse Elgarten
NL East Standings
Last updated: 06/19/2013
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