Archive for Player Analysis

In my last post on explaining pitchers’ BABIPs by way of their batted ball rates, I was very careful to say that it was applicable in the long run, as it’s hard to be accurate over a short number of innings pitched, due to all the “noise” in BABIP (Batting Average on Balls In Play).  I only used pitchers with a qualifying number of innings pitched (IP) in the calculations, for that reason.  After writing the post, I did some messing around with the data, to find out just how much of an effect IP had on the predictability of BABIP.

Hold on to your propeller beanies, fellow stat geeks: the correlation between xBABIP and BABIP went from 0.805 when the minimum IP was set to 1500, to 0.632 at a 200 IP minimum, down to 0.518 at 50 IP.  OK, maybe it’s not that surprising.  Still, I thought I’d better show you how confident you can be in my xBABIP formula’s accuracy when you take the pitcher’s innings pitched into account.

The formula, again: xBABIP = 0.4*LD% – 0.6*FB%*IFFB% + 0.235

And remember, that formula is primarily meant to be a backwards-looking estimator of “true,” defense-neutral BABIP.  My next article will (probably) discuss another formula I’ve come up with that’s more forward-looking.

Read the rest of this entry »

Hi everybody, this is my first post here. Today, I’ll be sharing some of my BABIP research with you. There will probably be several more in the near future.

Now, I don’t know about you, but Voros McCracken’s famous thesis stating that pitchers have practically no control over their batting average on balls in play (BABIP) always seemed counterintuitive to me, ever since I heard it about 10 years ago. Basically, my thought this whole time was that if an Average Joe were pitching to an MLB lineup, the hitters would rarely be fooled by the pitches, and would be crushing most of them, making it very tough on the fielders. Think Home Run Derby (only with a lot more walks). Now, the worst MLB pitcher is a lot closer in ability to the best pitcher than he is to an Average Joe, but there still must be a spectrum amongst MLB pitchers relating to their BABIP, I figured. After crunching some numbers, I have to say that intuition hasn’t completely failed me.

This is going to be a long article, so if you want the main point right here, right now, it’s this: in the long run, about 40% or more of the difference in pitchers’ BABIPs can be explained by two factors that are independent of their team’s defense: how often batters hit infield fly balls and line drives off of them. It is more difficult to predict on a yearly basis, where I can only say that those factors can predict over 22% of the difference. Line drive rates are fairly inconsistent, but pop fly rates are among the more predictable pitching stats (about as much as K/BB). I’ll explain the formula at the very end of the article.

Read the rest of this entry »

This past October, David Laurila conducted an interview with Zach Britton, the 23-year-old lefty who just finished up his rookie season with the Orioles. As a highly touted prospect, Britton didn’t put up impressive strikeout totals, but his groundball-inducing heavy sinker allowed him to enjoy much success in the minors. When Laurila asked Britton for his thoughts on his underwhelming major league 1.56 K/BB ratio, Britton responded as follows:

“I know that it could be better, obviously. I’m not going to be a guy who strikes out a ton of people; I’ll never lead the league in strikeouts. And with the movement I have, I’m going to walk guys. That’s something I can improve upon as I get older and more experienced, though. I can learn to make better adjustments… I pitch to contact. If I get a guy 0-2, I’m not necessarily looking to strike him out; I’m looking to get him to hit a ground ball. It’s a mindset. I’m not a huge believer in having to strike guys out in order to be successful. I’d rather keep my defense on their toes and get outs. Most times, when I strike guys out, it’s not on three or four pitches; it usually takes five, six or seven. Pitching to contact allows me to be more efficient.”

My first instinct was to be a bit skeptical of the effectiveness of this “mindset.”  Numerous studies have indicated that is issuing walks, not striking batters out, that ultimately increases pitch count to the point of being “inefficient.” Yet in sabermetric analysis, it is not uncommon to find outliers in these aggregate models — some players simply don’t fit the mold of generally accepted principles. Britton, after all, ought to know his own tendencies better than anyone else.

Read the rest of this entry »

It’s been nine months since the trade that brought Ryan Theriot to St. Louis, and the shortstop picture for the Cardinals is no clearer today than it was then. With their playoff hopes all but officially extinct, the prospect of another offseason spent looking for up-the-middle help looms large.

The trio of players who have garnered playing time at short for the Cards this season have been unimpressive, producing a combined 0.4 WAR in approximately a season’s worth of plate appearances. Theriot is an obvious non-tender candidate, while newly acquired Rafael Furcal will almost certainly have his $12 million option declined and become a free agent at the end of the season. This leaves the Cards with only Tyler Greene as an internal option, and the free agent market for shortstops is about as thin (the obvious exception being Jose Reyes, who the Cardinals have almost no hope of signing if they expect to keep Chris Carpenter and/or Albert Pujols). While the Cardinals will likely either give Greene a shot to hold down the job, or pick up another bargain during the free agency period, I’d like to propose that the Cardinals consider a radical alternative that could provide the team with a definitive edge: Albert Pujols.

Read the rest of this entry »

On the surface, Brett Gardner looks like a Bobby Abreu protege (without any power). Since 2010, Brett has shown off his great eye for pitches, posting the 2nd lowest chase rate in baseball at 18.1%.

His ability to make contact with pitches is also astonishing, as he makes contact with 97.2% with pitches in the strike zone, behind only Juan Pierre and Marco Scutaro. Of the 2789 pitches Brett has seen since the start of 2010, he has only swung and missed at 265 pitches.

Where Brett Gardner lacks is in his ability to swing at pitches in the strike zone. Over the last two seasons, Brett has swung at a major league low 45.2% of pitches in the strike zone. He owns this record almost 6% (next lowest is Elvis Andrus at 50.9%) and is almost 20% below the league average. Combined with his low chase rates, its only natural also that Brett has the lowest swing rate in MLB at 31.3%, compared to the league average of 45.6%.

Read the rest of this entry »

This article was originally published on WahooBlues.com.

There’s no question that Albert Pujols is one of the greatest players in the history of Major League Baseball. As the active leader in wOBA (.434) and wRC+ (169), it’s not easy to come up with a superlative that sounds like hyperbole for Pujols. But a commenter on his stats page gave it a try last week:

ALBERT Einstein had an IQ of 160. That means he is 60% more intelligent than the average human.

Albert Pujols has a wRC+ of 177 during the past 3 seasons. That means he is 77% better at the plate than the average big leaguer.

Read the rest of this entry »

After an apparent breakout season in 2009, Adam Lind regressed somewhat drastically trying to follow it up in 2010. The idea of Lind being able to play left field was all but completely abandoned in 2010 and he served as the team’s primary DH for much of the season. That was a move that couldn’t be argued against heading into 2010 because his defense was well below average and he looked to have a good enough bat to bring solid value from a DH role.

That however, is not what happened last season as Lind went from being a 3.5 WAR player in 2009 to a -.3 WAR player in 2010. His health had nothing to with the drop in production either, Lind played in 150 games last year after playing in 151 in 2009. The drop came entirely from his bat as his batting runs above average fell by 40 runs, from 35.9 to -5.9. Moving almost exclusively to DH actually helped his fWAR in 2010. His fielding and positional adjustment cost him 22.4 runs in 2009 but only 17.2 runs last year.

Read the rest of this entry »

If you haven’t heard anything about what Albert Pujols’ value will be over the next 7-10 years, I suggest you go here…Or here…Or here…Or take a look at the discussion here. Haven’t had enough? Read on.

Somebody is going to sign Pujols to a massive contract in the next 12 months. That contract will likely be hard to justify in projected on field value alone. If you are a fan of the team that gets him, after you get done saying PUJOLSAWESOMEBASEBALLYAY, you may want to know what his expected value will be and then take some time rationalizing the contract to yourself and justifying it to rival fans.

Read the rest of this entry »

In two previous articles, I considered the ability of freely available forecasts to predict hitter performance (part 1 and part 2), and how forecasts can be used to predict player randomness (here).  In this article, I look at the performance of the same six forecasts as before (ZIPS, Marcel, CHONE, Fangraphs Fans, ESPN, CBS), but instead look at starting pitchers’ wins, strikeouts, ERA, and WHIP.

Results are quite different than for hitters. ESPN is the clear winner here, with the most accurate forecasts and the ones with the most unique and relevant information. Fangraphs Fan projections are highly biased, as with the hitters, yet they add a large amount of distinct information, and thus are quite useful.  Surprisingly, the mechanical forecasts are, for the most part, failures. While ZIPS has the least bias, it is encompassed by other models in every statistic.*  Marcel and CHONE are also poor performers with no useful and unique information, but with higher bias.

Read the rest of this entry »

This article explores the ability to predict the randomness of players’ performance in 5 standard hitting categories: HRs, Runs, RBIs, SBs, and AVG. There have been efforts to do so by forecasters, most notably by Tango’s “reliability score.” (See Matt Klaassen’s article) I also test the idea that variation among forecasts (among ESPN, CHONE, Fangraphs Fans, ZIPS, Marcel, and CBS Sportsline) can predict player randomness as well.

I find that 1) variance among forecasts is a strong predictor of actual forecast error variance for HRs, Runs, RBIs and Steals, but a weak one for batting average, 2) Tango’s reliability score serves as a weak predictor of all 5 stats, and that 3), the forecast variance information dominates Tango’s measures in all categories but AVG.

Now let’s set up the analysis. Say, for example, that three forecasts say that Player A will hit 19, 20, and 21 home runs, respectively, and Player B will hit 10, 20, and 30 home runs. Does the fact that there is agreement in Player A’s forecast and disagreement in Player B’s provide some information about the randomness of Player A’s eventual performance relative to Player B’s?

To answer this, we need to do a few things first. We need a measure of dispersion of the forecasts. To do this, I define the forecast variance as the variance of the six forecasts for each stat, for each player.  If we take the square root of this number, we get the standard deviation of the forecast. So, the standard deviation of the forecasts of Player A’s HRs would be 1, and the standard deviation of the forecasts for Player 2 would be 10.

Next we turn to some regression analysis.* The dependent variable is the absolute error for a particular player’s consensus forecast (defined as the average among the six different forecasts). For both players A and B in the example, this number would be 20. This is my measure for performance randomness. Controlling for the projected counting stats, we can estimate this absolute error as a function of some measure of forecast reliability.

Tango’s reliability score is one such measure, and the forecast standard deviation is another.  What we would predict is that Tango’s score (where 0 means least reliable and 1 means most) would have a negative effect on the error. We would also predict that the forecast standard deviation would have a positive effect on the error. Now let’s see what the data tell us:

Runs:

HRs:

RBIs:

SBs:

AVG:

We see that HRs are the statistic for which errors are most easily forecasted, errors for Rs, RBIs, and SBs are moderately forecastable, and errors for AVG are not very forecastable. We see this because of the negative and statistically significant coefficients for Tango’s score and the positive and statistically significant coefficients on the standard deviation measure.  In regressions with both measures, the standard deviation measure encompasses Tango’s measure, except in the AVG equation.

So what does this all mean? If you’re looking at rival forecasts, 80% of the standard deviation between the HR forecasts and about 50% of the standard deviation of the forecasts of the other stats is legitimate randomness. This means that you can tell how random a player’s performance will be by the variation in the forecasts, especially home runs. If you don’t have time to compare different forecasts, then Tango’s reliability score is a rough approximation, but a pretty imprecise measure.

*For those of you unfamiliar with regression analysis, imagine a graph of dots and drawing a line through it. Now imagine the graph is 3 or 4 dimensions and doing the same, and the line is drawn such that the (sum of squares of) the distance between the dots and the line is minimized.