The war (not to be confused with WAR) between “counting statistics” and sabermetrics is still in full vigor.
The latest skirmish was started over Triple Crown winner Miguel Cabrera being awarded the MVP trophy over Rookie-of-the-Year Mike Trout. Trout beat Cabrera in the category known as WAR (hmm Good God y’all…) and that is just about the only significant thing other than defense in which he prevailed.
Ted Williams was the last Triple Crown winner to be shunned by the sportswriters (twice), in 1942 and again in 1947. In both instances he thoroughly trounced the winners, Joe Gordon and Joe DiMaggio respectively. In case you wonder, he also won the WAR, but lost the battle.
The cultural aspects of the two camps are essentially old-school baseball purists (I will consider myself in league with these guys) and younger, well-meaning mathematicians who insist on finding a reason or assumption for every play in the game.
For openers, I do not profess to have even a working knowledge of most of the metrics employed by Fangraphs.com and Baseball-Reference.com.
If you are over 55 it is a safe bet not to trust any statistic or metric that you can’t compute yourself without the aid of an electronic device.
Another easy call is to not trust a number that is thrown into the mix as a “theoretical”, “estimated” or “variable” figure.
By way of explanation it is easy to fortify my claim by looking at the metric known as WAR. While trying to school myself in the newer ways of the baseball world, I took a look at this metric on the Baseball-Reference website, as provided by Sean Smith.
Wins Above Replacement, as it is formally known, was introduced to that site in 2010. The reason for this metric is to “know how much better a player is than what a team would typically have to replace that player.” Sounds a little “iffy” already, but stay with me.
They then compare the player to “average in a variety of venues and then compare our theoretical replacement player to the average player and add the two results together.”
It is difficult for me to visualize this alleged “theoretical replacement player.” Does he look like Raul Ibanez or maybe Wilson Valdez?
I can come up with the average player by adding the stats for everyone in MLB and dividing the total by the number of players computed. Check?
Hold on though. Do I have to include every statistic for every player? Where would I be without my computer, calculator or tablet?
I don’t know about you, but by this time I am becoming tired and haven’t even computed the WAR for the first guy yet. But wait, there is more!
If you are a purist you will have to love this next statement. “There is no one way to determine WAR.” Does that give me license to add another make-believe statistic to the pile?
I do not really enjoy quoting all of this, but I can’t rework the words to give you a more formidable description. So be patient please.
“There are hundreds of steps to make this calculation, and dozens of places where reasonable people can disagree on the best way to implement a particular part of the framework. We have taken the utmost care and study at each step in the process, and believe all of our choices are well reasoned and defensible. But WAR is necessarily an approximation and will never be as precise or accurate as one would like.”
Yet many, if not most sabermetricians take this metric (almost said statistic) as the cream-of-the-crop, uber-powerful number with which we can determine and ultimately rank each player of all time.
You know what isn’t complicated? Home runs, runs batted in, batting average, ad nauseum. If a guy had 500 AB and got 175 hits his average is .350 (175/500 = .350). Easy, eh?
This qualifier here really does it for me: “We present the WAR values with decimal places because this relates the WAR value back to the runs contributed (as one win is about ten runs), but you should not take any full season difference between two players of less than one to two wins to be definitive (especially when the defensive metrics are included).”
So that is where I have been off the beaten path. I thought a win was only 9.5 runs (sarcasm).
Do you think Ozzie Smith had more value than Johhny Bench? WAR does, 73 to 72.3. How about Bobby Grich over Many Ramirez?
Enough of WAR, let us move on to a little defense since they may also sometimes be included.
Let us point our browsers to Fangraphs. First off is the UZR (Ultimate Zone Rating).
Here we are immediately adjured to bring into play yet another theoretical formula.
We find that this metric is an “advanced defensive metric that uses play-by-play data recorded by Baseball Info Solutions (BIS) to estimate each fielder’s defensive contribution in theoretical runs above or below an average fielder at his position in that player’s league and year.”
This mumbo-jumbo is beginning to make my old head hurt. “each event is assigned a number of runs, or fraction of a run, which is equal to the average value of that event as compared to a generic PA, generally for that year and for that league.”
It sounds like Pythagoras, Bill James and Norm Crosby got together to make something sound as though it makes sense.
Here is more statistical latin for you. “…with UZR the amount of credit that the fielder receives on each play, positive (if he makes an out) or negative (if he allows a hit or an ROE), depends on how often that particular kind of batted ball, in terms of its location, speed and several other factors, is fielded by an average fielder at the same position, measured over a time span of several years, in addition to whether the batted was a hit, out, or error (or FC).”
On what website do I find the speed of a ground ball to a shortstop? Are we using MPH (not a metric) or generic terms such as hard hit, lazy ground ball, or a seeing-eye grounder?
Look at this. “…the UZR engine estimates that it was a difficult ball to field”. So now are we allowing engines to determine if a ball was catchable or not? Interesting.
In essence here, are we saying that because Ozzie Smith could have made a play, and that since Zack Cozart didn’t, he should be penalized, even though he never laid a hand (or glove) on the ball?
On to pitching. BABIP (Batting Average on Balls In Play) is a metric used to show a pitcher’s true value. Are you listening – or reading?
This is from Fangraphs. “While typically around 30% of all balls in play fall for hits, there are three main variables that can affect BABIP rates for individual players: …defense…luck…changes in talent level.”
The defensive variable for BABIP basically claims that if a screamer is hit to third, a stud would make the play and a dud would not. A pitcher has no control over that.
Next, we look at luck variable. “Sometimes, even against a great defense, bloop hits can fall in. A batter may turn a nasty pitch into a dribbler that just sneaks past the first baseman, or they may blast a shot in the gap that a fielder makes a diving catch on. A pitcher can make the absolute perfect pitch against a batter, yet the hitter could still dribble it up the middle for a hit. That’s just the game.”
That sounds like whining to me, sir.
Now, the changes in talent level. “Maybe a pitcher starts tipping one of their pitchers, their mechanics are off, or they start leaving too many balls over the plate. Balls get hit harder until the pitcher makes the necessary adjustments, but until then, the harder a ball is struck, the more likely it is to fall in for a hit.”
That certainly is stating the obvious.
I can’t go on, big headache coming. Do you even understand what I am trying to say? It may be too simple to understand.
Baseball doesn’t really need theories, hypotheses or estimations. The boys of summer have been using the same yardsticks for scores of years. The numbers they have used have been easily calculated with a pencil in fairly short order.
Do we need a special set of metrics for everything that goes on in a game? I knew I should have studied calculus and chemistry. Now I will have to call Walter White to help me decide that Stan Musial was better than Kirby Puckett.