If there is one beef I have with the sabermetric based writing community is the occasional feeling that perhaps the general reader is being alienated or made to feel stupid. Advanced statistical analysis in baseball has come a long way and while there is no doubt it has changed the landscape of baseball debates it can leave some readers feeling despondent, frustrated and annoyed.
While I also tend to use the more advanced stats when doing an analysis piece I am also aware that you can never say many things in baseball with 100% certainty – with the exception that Delmon Young is an awful defensive player as some facts require little explanation. But you will see that callous and smug tone from time to time that the argument or point being made is absolute.
When I started this new blog I wanted to do the best analysis possible and to do that in my opinion you have to use the best tools available. For us baseball nerds, those ‘tools’ are the advanced stats provided by the good folks at Fangraphs, Baseball Prospectus, Baseball Reference etc.
Not using the absolute best data available is akin to Roy Halladay not using his cutter, just plain silly. Although the new metrics/stats can be intimidating at first glance once you learn them you realize your baseball knowledge prior to knowing them was well, rudimentary. If you care enough to be a well informed baseball follower why be an analog player in a digital world?
I first visited Fangraphs after listening to a Detroit sports talk radio show 5-6 years ago and heard one of the callers reference the site when discussing a certain player. He basically said “to the people that really know the stats” player X isn’t really all that valuable and well the rest as they say is history.
I thought I had a pretty healthy understanding of the game, I knew batting average was not the best way to value an offensive player, ERA was a flawed and basic stat, RBIs have no place in an argument about value (too reliant on teammates), batting average on balls in play etc – the basic tenets of the sabermetric world.
But after visiting the various sources I became completely immersed in learning everything I could about the game and the multiple exciting new statistics available to the general public (for free!) was a boon.
A disclaimer, since I have been actively researching baseball for the past decade I have learned one thing for certain – stats can be very trendy.
While most stats don’t completely go by the wayside they are often improved many times over. VORP (value over replacement player) has been pushed aside for WAR (wins above replacement), DIPS (defense independent pitching stats) has many new cousins with FIP (fielding independent pitching), xFIP (FIP with a normalized homerun rate) and SIERRA (skill interactive ERA).
For those not familiar with some of the offensive stats I will frequently use on this site let’s go over some of them quickly. Again, I will explain these as simply as I can and also give sources to where you can get the more detailed and full explanation.
Let’s deal with hitting statistics today.
I’d like to share what I will normally look for when trying to value a batter and at the end we will do a quick evaluation of a player who has a wide array of opinions as to how ‘valuable’ they really are. Again this opinion is not necessarily shared or true for the trained stat head but not everyone who enjoys the game of baseball will think or research in the same matter.
First I do look at most of the same stats that are universally known, a common misconception is that these stats have no place in the statistical analysis world but without them we cannot get a proper read on a player.
I like to have PAs (plate appearances) handy to see what type of sample size we are working with, obviously the more the better. Next, there is nothing wrong with looking at the good old ‘slash line’ (AVG/OBP/SLG) or counting stats such as HRs as they are still important. You can also see I check the HR/FB ratio to see if the homerun rate is sustainable or normal.
I always look at the BABIP (batting average on balls in play) to see if the batter is benefiting from luck when he does make contact – make sure to look at his career BABIP for comparison sake. The LD/FB/GB % is simply the percentages of times the player hits a line-drive, fly ball or ground ball. A quick look at their batted ball profile can give us a good indication of how hard they are hitting the ball.
Finally there is nothing wrong with seeing how many stolen bases a player has though it is always a good idea to see what the steal percentage is, anything less than 76% is actually costing his team runs over the long term given there are a finite number of outs available to a team during a game/season.
I definitely have a good idea of what type of player based on the above information (or what type of season he is having) but I ultimately suspend forming an opinion until the following key sabermetric statistics are viewed to get a full and complete picture.
The first stat is probably the first thing I look for in a quality offensive player – BB%. How much a player walks in proportion to his plate appearances is huge for a number of reasons. Batting average can be greatly affected by the year-to-year swings of a player’s BABIP but a player who consistently takes a free pass should produce a solid on-base percentage most years.
Obviously in direct competition is the K% though they can often go hand-in-hand. A player who takes a good amount of walks will presumably be working the count making them susceptible to strikeouts as well. ISO simply subtracts the players SLG% from his batting average to see what type of power the player hits with – the higher the better.
Now to the real heart of advanced stats –wOBA and wRC+. Both are relatively new stats but are the most useful and should be one of the first referenced statistics in baseball.
From Fangraphs website:
Weighted On-Base Average (wOBA) is based on a simple concept: not all hits are created equal. Batting average would have you believe they are, but think about it: what’s more valuable, a single or a homerun? Batting average doesn’t account for this difference and slugging percentage doesn’t do so accurately (is a double worth twice as much as a single? In short, no). OPS does a good job of combining all the different aspects of hitting (hitting for average, hitting for power, having plate discipline) into one metric, but it weighs slugging percentage the same as on-base percentage, while on-base percentage is more valuable than slugging.
Weighted On-Base Average combines all the different aspects of hitting into one metric, weighting each of them in proportion to their actual run value.
Conversely OPS (on-base plus slugging) says that two players with 800 OPS are identical but what if one has a 300/500 OBP/SLG split and the other 400/400. From what we know about scoring runs, they are clearly completely different in terms of value.
For those curious here is the formula for wOBA:
0.72*uBB + .75*HBP + .9*single + 1.24*double + 1.56*triple + 1.95*homerun + .92*rboe / PA – IBB.
Next we have wRC+ is one of the best new statistics in town, see the description below.
Weighted Runs Created (wRC) is an improved version of Bill James’ old Runs Created (RC) statistic, which attempted to quantify a player’s total offensive value and measure it by runs. This way, instead of looking at a player’s line and listing out all the details (e.g. 23 2B, 15 HR, 55 BB, 110 K, 19 SB, 5 CS), you could synthesize all the information into one metric and say, “Player x was worth 24 runs to his team last year.” While the idea was sound, James’ formula has since been superseded by Tom Tango’s wRC and is based off of wOBA.
Similar to OPS+, Weighted Runs Created Plus (wRC+) measures how a player’s wRC compares with league average. League average is 100 and every point above 100 is a percentage point above league average. For example, a 125 wRC+ means a player created 25% more runs than league average. Similarly, every point below 100 is a percentage point below league average, so an 80 wRC+ means a player created 20% fewer runs than league average.
wRC+ is also park and league adjusted, meaning you can use it to compare players that played in different years, parks, and leagues. Want to know how Ted Williams compares with Albert Pujols in terms of offensive abilities? This is your statistic.
Defensive metrics and statistics are not without controversy as almost everyone will agree the data is not perfect but for now I’d like to think UZR (ultimate zone rating) is as good as it gets. If scoring runs is the goal of the offense than preventing runs is clearly the goal of the defense.
Playing quality defense at a premium position (centre field, shortstop) will make even the some of the worst hitters valuable to the team. Players that can do both well (Troy Tulowitzki, Andrew McCutchen and Jacoby Ellsbury for example) are among the most valuable players in baseball.
Ultimate Zone Rating (UZR) is one of the two best publicly available defensive statistics, if not the best. UZR is tougher to understand intuitively than the Dewan +/- system, but the basic gist is that UZR puts a run value to defense, attempting to quantify how many runs a player saved or gave up through their fielding prowess (or lack thereof). There are a couple different components to UZR, including:
Finally, we get to one of the preeminent stats in baseball, WAR – or “wins above replacement”.
Wins Above Replacement (WAR) is an attempt by the sabermetric community to summarize a player’s total contributions to their team in one statistic. You should always use more than one metric at a time when evaluating players, but WAR is pretty darn all-inclusive and provides a handy reference point. WAR basically looks at a player and asks the question, “If this player got injured and their team had to replace them with a minor leaguer or someone from their bench, how much value would the team be losing?” This value is expressed in a wins format, so we could say that Player X is worth 6.3 wins to their team while Player Y is only worth 3.5 wins.
WAR is important and often used when trying to determine if a player is earning his salary or is expected to earn his salary. A win on the free agent market will cost a team approximately $5MM so for a player to earn say a $20 million dollar per season salary he should be worth roughly 4 wins or have a 4.0 WAR.
There are currently two different versions of WAR, fWAR (WAR provided by Fangraphs) and bWAR (WAR provided by Baseball Reference), click the link for a more detailed breakdown of both.
Let’s put this into practise for a popular player who when acquired a good majority of their fans really thought they had scored a valuable, indispensable asset based. Let’s see if you can guess who it is.
When you summarize even the basic stats it is hard to see what exactly was valuable about this player, maybe the 12 HRs? Maybe his 2010 counting stats (21 HRs and over 100 RBIs) had a lot of people fooled or maybe his more advanced stats and defensive ability will help and tell a different story?
For those curious Player A in question is Delmon Young of the Detroit Tigers. Delmong Young fails to add value on four major fronts as he doesn’t contribute power, on-base skills, speed or defense. If you want a good idea of what WAR is really trying to measure, think of it as a wins above Delmon Young, as he is basically a replacement level talent.
For his entire career Delmon Young is worth a paltry 1.6 WAR, or essentially one month of what Jose Bautista contributes during a season! When someone says Jose Bautista was worth 8.3 wins in 2011 what they are truly saying is he was worth 8 more wins than if Delmon Young were given the exact same playing time for the season – a scary thought for Blue Jays fans.
According to WAR calculations Delmon Young contributed like a $2MM dollar player in 2011, unfortunately the Tigers are going to be paying him $6.75MM for 2012.
Next time we will dive into the pitching side.