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2012 Cubs Preview: Corner Infield

First Base: Bryan LaHair | Is Bryan LaHair a Quad-A player?

Al recently put a good story together on this, but I figured we’d use some analytical tools to determine whether or not he has shown signs of being a Quad-A player.

The skill that differentiates Quad-A players from major league players is their ability - or inability in this case - to hit breaking pitches. Quad-A players face a lot of bad breaking pitches in the minors, and tend to lay off these in favor of fastballs. However, once they come up to the majors, they begin facing breaking pitches that are much better than the Triple-A pitches they have been accustomed to, and have trouble hitting them.

If we look at LaHair’s Pitch f/x Pitch Values, we see that he hits fastballs really well, (5.00 wFA/C), but that he struggles mightily against curveballs, (-2.64 wCU/C), and changeups, (-2.06 wCH/C).* To put this into context, I looked at two other players who recently reached the majors - Starlin Castro and Matt LaPorta. We know how good Starlin has been - he’s clearly not a Quad-A player. LaPorta on the other hand looks like a classic Quad-A player as he doesn’t hit curveballs or changeups well. Here are their 2011 Pitch f/x Pitch Values:

As we can see, both LaHair and LaPorta struggle against breaking and off-speed pitches, while on the whole, Castro has shown an ability to hit both breaking and off-speed pitches.

I then looked at Pitch f/x data to determine swing-and-miss rates on certain pitches. Here are their swing-and-miss rates:

As we can see, both LaHair and LaPorta swing-and-miss at a very high percentage of curveballs and changeups. While Castro swings-and-misses at a high percentage of curveballs, he makes up for this by making strong contact on the curveballs that he does hit, as evidenced by his 1.54 wCU/C. Unfortunately, we can’t say the same thing for LaHair or LaPorta. They not only have negative pitch values on breaking and off-speed pitches, but they also have very high swing-and-miss rates. In other words, even when they do end up making contact, they end up hitting them poorly, (or at least not well enough to make up for their high swing-and-miss rates).

Finally, I looked at LaHair’s Pitch f/x swinging strikes charts.

As we can see, there are a lot of squares, (curveballs), and diamonds, (changeups). 17 of LaHair’s 32 swings-and-misses came on the curveball and changeup. He was thrown one of these two breaking pitches 25.9% of the time. In other words, 53.1% of his swings-and-misses came on 25.9% of the pitches thrown; another indication that LaHair has trouble with major league breaking pitches.

Each of the three analytical tools that I’ve used shows us that LaHair struggles with major league breaking pitches, suggesting that he could be a Quad-A hitter. However, there are two things that give us some hope: (i) not only does he make contact on sliders, (something LaPorta doesn’t do well), but he hits sliders well, (0.78 wSL/C), and (ii) we’re looking at a very small sample size as LaHair only had 69 plate appearances last year. And if this isn’t enough hope for you, Bryan did say that he’s “probably in the best shape of [his] life.”

Third Base: Ian Stewart | What was wrong with Stewart in 2011?

Stewart’s 2011 was horrendous, plain and simple. There are some numbers that point to a potential bounce back - especially a career low .224 BABIP. Furthermore, Stewart has had an above average walk rate each year he’s been in the majors, and an above average ISO in three of the four years. However, I dived into some Pitch f/x numbers and found some glaring issues. Here are Stewart’s career Pitch f/x Pitch Values:

In the past, Stewart has clearly had some issues hitting curveballs and changeups, which made me wonder if he could be another potential Quad-A player. However, Quad-A players don’t typically have 25-home run seasons, even those who are lucky enough to call Coors Field their home, (although, in 2009, Stewart only hit 13 of his 25 home runs at home). I took a quick look at his HR%, and, as we can see from below, it’s been significantly higher than the league average, (3.42 HR%), in every year except 2011.

Getting back to his pitch values, we can see that while Stewart has struggled with breaking and off-speed pitches in his career, he was well above average against the changeup in 2008, only slightly below average against the changeup in 2009, and above average against the curveball in 2010. In other words, in each of his first three years, Stewart only really struggled with one of the three breaking and off-speed pitches. However, this trend didn’t continue in 2011 - he was well below average against the slider and changeup, and below average against the curveball. In fact, Stewart was even below average on the pitch that he made his living off of during his first three years in the league: the fastball. No longer could his production against fastballs make up for his lack of production against breaking and off-speed pitches. I then took a look at Stewart’s swing-and-miss rates:

Stewart has always had high swing-and-miss rates on sliders, curveballs, and changeups. In the past, despite his high swing-and-miss rates, he managed to be a productive hitter because of his ability to hit pitches well when he made contact. His high swing-and-miss rates finally caught up to him this past year; because of his negative pitch values, we can infer that even when he managed to make contact, his contact wasn’t strong enough to be productive, and he was consequently one of the worst hitters in the majors.

While we can hope that Stewart’s struggles were a result of his injuries and lack of consistent playing time, there are some warning signs that his issues may be too large to overcome with just good health and playing time. Here’s to hoping that I’m wrong.

As always, let me know what you guys think. Time permitting - I’ll be busy with exams over the next couple of weeks - my next post will focus on our pitchers. If you have any suggestions for questions you’d like me to answer about our rotation or bullpen, then please let me know in the comments.


* Here’s a short primer on pitch values.

Thanks to FanGraphs for Pitch f/x Pitch Values data.

Thanks to Joe Lefkowitz for his Pitch f/x data.

2012 Cubs Preview: Middle Infield

Catcher: Geovany Soto | Soto’s 2009 season was hampered by a low BABIP, was this what plagued him in 2011 too?

In 2009, Soto posted a .246 BABIP - the 13th lowest in the majors - well below the league average BABIP of .299. As we can see from the chart below, the slope of his BABIP decline from ‘08 to ‘09 is steeper than the slope of his wOBA decline during the same period of time. This indicates that while his BABIP fell, his wOBA didn’t fall as much as his BABIP might indicate.

On the other hand, in 2011, Soto posted a .280 BABIP - only slightly below the league average .295 BABIP. Flip back to the chart and we now see that the slope of the BABIP is less steep than the slope of the wOBA indicating that wOBA fell by more than what BABIP would suggest. What’s the most likely explanation? Enter BB% and K%.

In 2009, Soto’s walk and strikeout rates both improved, which is likely part of the reason that his wOBA didn’t fall by as much as his BABIP suggested it would. In 2011, however, both Soto’s walk and strikeout rates went in the wrong direction: he posted a career low walk rate and a career high strikeout rate. These two developments are likely why his wOBA fell by more than his BABIP suggested it would. While his 2009 season was likely a result of bad luck, his 2011 season was likely a result of his declining skills. Before you bring up his age, take a look at the following chart.*

The blue curve consists of all players. The red curve consists of 70 players since 1981 who spent at least four seasons with a minimum of 800 inning per season at the catcher position. The yellow curve consist of players who spent at least one season as a part time catcher since 1981.

Looking at the red curve, we see that catchers usually peak at the age of 26 and 27, and then begin declining at a rate similar to the all players sample. It’s definitely possible that, at the age of 29, Soto is in his decline phase.

In the event that his BABIP climbs closer to his career average of .303 in 2012, I wouldn’t be surprised to see Theo & Jed trade him at the deadline. Soto’s not getting any younger, and a team desperate for offense may be willing to overpay come July.

Second Base: Darwin Barney | Did Barney run out of steam near the end of the season?

Below is a chart of Barney’s 2011 cumulative wOBA on a daily basis.

While Barney’s wOBA was between .320 and .340 for most of the first quarter of the season, it fell below .300 mid-season. While he managed to pick it back up to about .310 soon after coming back from his time on the disabled list, he ended the season with a .293 wOBA. Which version is the true Barney: the .330 hitter who just ran out of steam, or the .293 hitter who played above his true talent level early on in the season?

Research says the .293 wOBA hitter.* Statistics such as OBP, SLG, and OPS have been shown to stabilize after 500 plate appearances. Since wOBA is an extension of these statistics, we can expect it to stabilize and be a reliable indicator of true talent level after 500 plate appearances. Looking at Starlin’s 2011 cumulative wOBA chart, we can see his wOBA stabilize almost as soon as hits 500 plate appearances.

Looking back at Barney’s wOBA chart, we see his wOBA hovering between .290 and .300 after 500 plate appearances, suggesting that his true talent level is likely closer to his sub-.300 wOBA than the .330 wOBA he displayed at the beginning of the season. Here’s to hoping I’m wrong.

Shortstop: Starlin Castro | How has his ability to hit for power developed since he broke into the majors?

The offensive numbers Castro put up in his age 21 season were phenomenal, and while there’s much to appreciate, I’m sure that many of us are interested in the development of his ability to hit for power. Below is a chart of Castro’s career cumulative ISO on the basis of plate appearances, (the first 506 plate appearances are from 2010, with the following 715 plate appearance occurring in 2011).

Once again, using research on the stabilization of statistics, ISO tends to stabilize at 550 plate appearances. If we look at Castro’s ISO at 550 career plate appearances, we see that it’s at around .110. By the end of the 2011 season, Castro’s career ISO was much closer to .120, constantly hovering between .118 and .120. While Castro’s ISO has clearly improved, his improvement might become even more apparent when we take a closer look.

If we compare Castro’s two seasons, the increased power is much more apparent. If we look at Castro’s 2010 ISO, we see that it stabilizes at .108. In 2011, Castro’s ISO stabilized at .125 - a 17 point increase over the course of a season, which is tied for the 22nd highest positive ISO change of last season, (among batters with over 550 PA). Castro’s 2012 ISO rates will show us if these gains are permanent and whether or not he can continue to build upon his power improvements.

Look out for a preview of the rest of the 25-man roster - or as much of it as I can get to - over the course of the next few weeks. In the meantime, let me me know (i) what you think of these three questions, and (ii) if you have any question suggestions for the rest of the 25-man roster.


* Thanks to FanGraphs for the catcher aging curve.

* Thanks to Pizza Cutter for his research on stabilization of statistics.

Cubs 2012 Season Preview

I put together some player previews over at Bleed Cubbie Blue. When I initially thought about constructing player projections, I quickly realized that there were other projections that were far better than anything I could produce, and that we were all likely projectioned-out anyway. That said, I decided to address one meaningful question regarding each Cubs player in advance of the upcoming season.

I will post one preview per day on each of the next six days. Here is a list of the previews and when they’ll be up.

April 10 | Middle Infield: Geovany Soto, Darwin Barney, and Starlin Castro

April 11 | Corner Infield: Bryan LaHair and Ian Stewart

April 12 | Outfield: David DeJesus, Marlon Byrd, and Alfonso Soriano

April 13 | Starting Rotation Part I: Matt Garza and Jeff Samardzija

April 14| Starting Rotation Part II: Ryan Dempster and Paul Maholm

April 15 | Starting Rotation Part III: Chris Volstad and The Rest

Value of Various Plate Approaches

Analysts and commentators frequently rave about a player’s ability to hit to the opposite-field. Extreme pull-hitters are often knocked by many fans who consider hitting to the opposite field a key indicator of a great hitter. However, some of the best hitters in the game - Albert Pujols, Alex Rodriguez, Ben Zobrist, Carlos Beltran, Paul Konerko, Mark Teixeira, and Ken Griffey Jr. - could be considered pull-hitters: hitters who have pull rates above the 84th percentile, or one standard deviation above the mean, among all players. Thus, I wanted to understand the value of hitters with extreme split tendencies versus hitters without any extreme split tendencies, (spray hitters).

I looked at the past seven years worth of data, evaluating 310 hitters in the process. These 310 hitters had at least 300 plate appearances that resulted in balls that were pulled, hit up the middle, and hit to the opposite field, (so a total of at least 900 plate appearances per player). 

I started off by studying the correlation between a player’s pull wOBA, up-the-middle wOBA, and opposite-field wOBA against his overall wOBA. Below are the scatterplots with the directional wOBA on the x-axis and the overall wOBA on the y-axis, followed by the correlation coefficient - the measure of the strength of linear dependence between two variables - of the data.*

Pull wOBA vs. Overall wOBA

r = .6027

Up-the-Middle wOBA vs. Overall wOBA

r = .7042

Opposite wOBA vs. Overall wOBA

r = .5057

It’s interesting to see that a player’s up-the-middle wOBA has the strongest relationship with a player’s overall wOBA, (r=.7042). However, this analysis doesn’t bring about many tangible results. While it’s an interesting conclusion, there is a glaring issue with this analysis: directional wOBA does not include walks or strikeouts - it only records a player’s wOBA on the balls that he puts in play to each part of the field - so these correlation tests aren’t very meaningful.

Once I realized the relatively fruitless analysis I had just performed, I tweaked my methodology in the hopes of finding something more relavent. Instead of looking at directional wOBA, I looked at the relationship between the directional frequency of their plate appearances - how frequently these 310 hitters pulled the ball, hit it up the middle, and hit it to the opposite field - and their overall wOBA. Below are the scatterplots with the directional frequency on the x-axis and the overall wOBA on the y-axis, followed by the correlation coefficient of the data.

Pull Frequency vs. Overall wOBA

r = .2032

Up-the-Middle Frequency vs. Overall wOBA

r = -.1858

Opposite Frequency vs. Overall wOBA

r = -.1730

Here, pull frequency has the strongest positive association with overall wOBA, while both up-the-middle frequency and opposite-field frequency have negative associations with wOBA. In other words, the more frequently a player pulls the ball, the higher his wOBA likely is; however, the more frequently a player hits the ball up-the-middle or to the opposite field, the lower his wOBA likely is. This makes a lot of intuitive sense - players who can pull the ball will have an easier time hitting extra base hits and home runs, resulting in higher overall wOBAs. On the flip side, players who hit up-the-middle and to the opposite field will have a harder time hitting home runs, (the centerfield fence is usually the deepest, and hitting home runs to the opposite field is significantly more difficult than pulling home runs), and extra base hits, (hitting to the opposite field with power is significantly more difficult than pulling the ball with power), resulting in lower overall wOBAs. Let’s see if this logic applies when we test it on individual players and their overall wOBAs.

To do this, I needed to test the relationship between players with extreme directional frequencies and wOBA. I began by calculating the distribution of each directional frequency. I then categorized each of the 310 hitters into four plate approaches: pull, up-the-middle, opposite-field, and spray. Those who were categorized within one of the pull, up-the-middle, and opposite-field approaches had to have a directional frequency greater than one standard deviation above the mean, (higher than the 84th percentile), in that specific frequency. So, for example, in order for a hitter to be categorized as a pull-hitter, the hitter must have a pull frequency greater than 45.5%, (the 84th percentile of pull frequency amongst the 310 hitters). Those who did not fall into any of the first three categories, fell into the spray category - these hitters did not have extreme directional tendencies and are the ones who essentially hit to all fields. Once the players were categorized, I calculated the average wOBA of each category and compared it to the weakest wOBA of the four categories. Here are the results:

Hitter Splits wOBA

Pull hitters have the highest wOBA, followed by spray hitters, opposite-field hitters, and up-the-middle hitters. When compared to the weakest category, pull hitters were worth, on average, an additional win over the course of 600 plate appearances.* As we found in the correlation study, hitters who pull the ball more frequently tend to have higher wOBAs than those who hit the ball up-the-middle and to the opposite field.

Thus, we can conclude that, on average, pull-hitters are significantly better than up-the-middle and opposite-field hitters, and only slightly better than spray hitters.


* The higher the correlation coefficient, the stronger the relationship between the two variables.

* Assuming that 10 runs equals a win.