Archive for Player Analysis

Byron Buxton and Francisco Lindor were called up for the Minnesota Twins and Cleveland Indians respectively. Both players are 21 years old, Buxton in the outfield and Lindor at shortstop. SOSA doesn’t officially project players that didn’t play last season in the MLB — I have to manually input data for projections.

One major difference between Buxton and Lindor is that Buxton was called up straight from AA. SOSA calculates its MLE’s (Minor League Equivalencies) by comparing the minor-league level the player is coming from to the MLB. Systems like Oliver directly link them, other systems chain their projections.

Despite both hitting around .280 in the minors, Buxton takes the bigger hit. Because he’s coming from AA his numbers are brought down further. SOSA has Buxton slashing .204/.283/.372 at the Majors, with Lindor slashing .217/.295/.306 with Cleveland. The discrepancy? While Buxton has a worse batting average and on-base percentage, he’s projected for a better slugging percentage than Lindor.

While Lindor is projected for eight more doubles and four more home runs than Buxton, Lindor is projected for 280 more at-bats. With the young outfielder coming up earlier than expected, he can likely outperform his projected four doubles, seven triples, and four home runs. Buxton already had twelve triples and twenty stolen bases; with speed transferring well to the majors, Buxton could find a solid role with the team that his batting average doesn’t accurately represent.

The talented Cleveland shortstop’s projection is considered more accurate. He’s projected for a solid 469 at bats, producing a wOBA (Weighted On-Base Average) of .266, near the bottom of the pack in what I called top-tier prospects this off-season. With projections for twelve doubles, three triples, and eight home runs, Lindor’s offensive value does exist, but he has much to prove at the big-league level

So while Buxton and Lindor’s offensive value doesn’t match that of some prospects called up earlier, they’re both above-average defenders, and they will find ways to contribute to their teams. If you have questions about Minor League Equivalencies, SOSA, or stats feel free to hit me up on twitter @AthleteInvictus.

When Xander Bogaerts was a 16-year-old kid playing baseball down in Aruba, looking up to his idol Derek Jeter, I’m sure he too had dreamed of one day playing in an All-Star game. Well, that dream may soon become real for the 22-year-old shortstop.

In 2009, when Bogaerts was just 16-years-old he was offered a professional contract by the Boston Red Sox, a contract that featured a $410,000 signing bonus.

Cruising through the minor leagues, Bogaerts soon became the talk of the town in Boston when he was ranked the 6th overall prospect in baseball by MLB.com. Entering the 2013 season, it wasn’t expected that Bogaerts would see any time at shortstop that season due to the acquisition of Stephen Drew.

He was brought up though, and good thing he was. Bogaerts exceeded most expectations in the 2013 postseason batting .296 in 12 games eventually helping the Red Sox to their third World Series title in 10 years. He returned to his homeland of Aruba and was treated to a red carpet as well as a parade with the natives lining the streets.

In 2014, he was shuffled back and forth from shortstop to third base, messing up his development and also taking a toll on him offensively. He did, however, manage to hit 12 home runs in 144 games. His average though dropped considerably from what many thought it would be. Bogaerts also showed how you can have awesome plate discipline in Triple-A, but it doesn’t always translate right away into your big-league career.

This year, Bogaerts is excelling in all aspects of his position. On offense and defense, the youngster has the temperament like that of a seasoned veteran. And it’s great. He doesn’t take his at-bats into the field and he certainly doesn’t take his fielding miscues (or lack thereof) into the batters box.

In 56 games this season, Xander is batting .297/.343/.401. Not to mention he has been one of the hottest hitters in the game in the last 30 days batting .352/.383/.466 while posting a 137 wRC+ in that span as well. And, Bogaerts also is getting base hits on pitches all over the strike zone. That shows a huge improvement in his plate discipline and pitch-selection skills over the past season.

On the defensive side, Bogaerts has shown a strong improvement from last year when he posted a Defensive Runs Saved of -9. This year, thanks to his offseason work in Arizona with Dustin Pedroia, and also adjusting his footwork with infield coach Brian Butterfield, Bogaerts has a Defensive Runs Saved of -1.

So why isn’t Bogaerts being talked about as if he’s an All-Star? His numbers show that he is certainly All-Star-Game worthy. The top five vote collectors for the All-Star Game at shortstop are Alcides Escobar, Jose Iglesias, Marcus Semien, Jed Lowrie and Jose Reyes. How does this make sense? I’ll tell you — because for some reason, this year in particular, the All-Star Game is a popularity contest, and it’s not based on skill. What do the numbers say to you? They don’t lie.

If it was based on skill, there wouldn’t be seven Kansas City Royals in the lead to start the All-Star Game this year. Pablo Sandoval wouldn’t be third in the AL third baseman voting. David Ortiz wouldn’t be fourth in the DH voting. And Dustin Pedroia most definitely would not be trailing Omar Infante in the second baseman voting. We will wait and see if Bogaerts has a shot at the All-Star Game this year, but if not, we can then tell that this system is flawed and is not based on skill and skill alone.

With apologies to Clevelanders everywhere, I can think of a number of reasons why Pedro Alvarez would rather be in Pittsburgh than in Cleveland, or C-Town, or The Mistake on the Lake, whichever you prefer. Pittsburgh is located where the first Europeans reached the “Golden Triangle” at the confluence of the Ohio, the Monongahela, and Allegheny rivers. “Golden Triangle” not only sounds very appealing, in a slightly sexual way, but to my knowledge none of the three rivers confluence-ing in Pittsburgh have been on fire like the Cuyahoga River that runs through Cleveland.

Pittsburgh has regularly finished near the top of lists of “Most Livable City in the U.S.”, including last year when the ‘Burgh narrowly beat out Honolulu for the top spot. It was the third time in seven years that The Economist had Pittsburgh at or near the top of the list of the most livable cities in the continental United States. Cleveland, on the other hand, was named the most miserable city in the United States, according to a 2010 poll by Forbes.com.

So it’s clear the Pedro Alvarez is quite fortunate to be with the Pittsburgh Pirates as opposed to the Cleveland Native Americans, just for the pure livability factor of the city.

When it comes to baseball, this pattern continues. The Pirates are 31-25 (.554) and in second place in the NL Central. Cleveland is 27-29 and in fourth place in the AL Central (all data is through June 7^th). According to the playoff odds at FanGraphs, the Pirates currently have a 67.6% chance of winning their division or being a wild card team. The Indians are at 51.5%. If not for the parity of the American League, this gap would be even greater.

More importantly, and the point of this article, there’s something very specific to Pedro Alvarez that makes it fortunate that he’s with Pittsburgh this year and not Cleveland. Pedro Alvarez does not hit well against left-handed pitching. This is not a major revelation. I think most people reading FanGraphs know that Pedro struggles against lefties. In his career, Pedro has hit .193/.263/.315 against lefties, with a walk rate of 8.3% and strikeout rate of 36.7%. Yikes! His wRC+ against lefties in his career is 61. Against right-handed pitchers, Alvarez has hit .248/.321/.474, with an improved walk rate of 9.5%, a much improved strikeout rate of 26.9%, and a 118 wRC+. For reference, based on his career wRC+, Alvarez hits like the 2015 version of Starling Marte (.256/.317/.473 this year) against right-handed pitchers and like the 2015 version of Lonnie Chisenhall (.209/.241/.345 this year) against lefties. Chisenhall was just sent to the minor leagues. Pedro Alvarez, like former Pirate Andy Van Slyke before him, is fortunate that he lives in a predominantly right-handed world.

Adding to the good fortune for Alvarez this season is the limited number of left-handed pitchers the Pirates have faced. The average team in the Major Leagues has had 24.8% of their plate appearances against southpaws. The Pirates have had the lowest percentage of plate appearances against lefties, just 17.5%. That’s more than one standard deviation below the average. The Cleveland Indians are on the opposite side of the coin, having had 38.0% of their plate appearances against lefties, which is more than two standard deviations above the average. Consider the handedness of the starting rotations of the non-Pittsburgh and non-Cleveland teams in the AL Central and NL Central:

The non-Cleveland teams in the AL Central have 12 right-handed starters and eight left-handers (40% lefties) currently in their starting rotations. The non-Pittsburgh teams in the NL Central have 17 righties and just three lefties currently in their starting rotations (15% lefties).

I was curious how big a difference this would make for Pedro Alvarez, so I decided to look at it in two different ways.

For the first scenario, I took Pedro’s batting line against lefties (20 PA) and righties (173 PA) this year. I figured out what percentage of the team’s plate appearances against each type of pitcher Alvarez has had. I then applied those percentages to Alvarez if he were to play on a team that has seen the league average number of lefties and on the Indians, who have seen the most lefties this year. Here are the results:

Alvarez goes from a .244/.316/.453 hitter with the Pirates to a .220/.289/.403 hitter with the Indians. Not only would he be moving from one of the most livable cities in the U.S. to one of the most miserable, his production would take a big hit. He also loses some playing time because the Pirates have limited him against lefties and the Indians and the league-average team have faced fewer righties.

This leads me to the second scenario. The Pirates have rightly limited the number of plate appearances against lefties for Alvarez this year. Just 20 of his 193 plate appearances have been against southpaws. If Alvarez was transplanted to Cleveland, or to a league-average team, perhaps they would also keep Alvarez riding the pine when a lefty is on the mound. In this second scenario, I moved Alvarez to the Indians and to a league-average team and limited his plate appearances against left-handers to his actual number of 20.

If he were limited to 20 plate appearances against lefties, Alvarez would hit similarly well but would lose playing time. In the case of a hypothetical move to Cleveland, Alvarez would have 41 fewer plate appearances, the equivalent of around 11 games based on his current 3.6 plate appearances per game played.

Pedro Alvarez, you are a fortunate man. Not only do you live in a world with many more right-handed pitchers than lefties, but you also play for a team that has seen more right-handers than any other team in baseball. Now go enjoy the spectacular view of Pittsburgh while riding on the Duquesne Incline, then get a Primanti Brothers sandwich and head over to Point State Park near the “Golden Triangle” and enjoy one of the country’s largest fountains on a beautiful Pittsburgh day.

(I feel like I should maybe apologize to any Clevelanders who might be reading this, but I don’t know if they have Internet in Cleveland yet, so it’s probably not necessary.)

Delino DeShields currently has a .395 BABIP en route to a .291 average. A .395 BABIP is probably unsustainable, but I was shocked when I saw the Steamer projection of .287 BABIP for DeShields, going forward. A .287 BABIP for a guy like DeShields is just unreasonable. He’s one of the fastest guys in the league, and his baseline BABIP should be well above .300 as he can turn groundouts into infield singles.

So I decided to crunch some numbers, which ultimately confirmed my suspicions. A BABIP of .286 is too low.  Looking at batted ball data, .315 is what I calculated his expected BABIP to be going forward. I’ll explain below:

DeShields has 13.6% infield hit%.

League average is 6.7%.

DeShields is more than twice as likely to get an infield hit, which is 6.9% more likely than average to get a hit in general. As a side note, he’s also 50% on bunting for hits, which is astounding (also more than twice league average).

Baseline BABIP for groundballs is .232

Add a DeShields speed .069 infield groundball advantage, and therefore you’re looking at a DeShields baseline groundball BABIP of .301.

Line drives are the best — .690 baseline BABIP according the source above. Fly balls have .218 baseline BABIP. Speed shouldn’t have much of an effect on these so I’m not adjusting them, other than accounting for infield fly balls which are guaranteed outs.

I’m going to calculate the expected BABIP for DeShields based on the above data. The expected BABIP will equal the summation of the following:

Flyballs — .218 x .261 (26.1% FB, minus the difference between DeShields IFFB and league average, which is .111 minus .095 = .016; .261-.016=.245) = .05341

Groundballs — .301 x .638 (63.8% GB) = .192038

Line Drives — .690 x .101 (10.1% FB) = .06969

= .315 BASELINE BABIP

We can take that average and take away his strikeouts/walks to determine his expected batting average/OBP going forward.

22.1% Ks. So we’ll take the baseline BABIP multiplied by .779. = .245 expected batting average.

13.1% BBs. So we’ll take the baseline BABIP multiplied by .648 (Ks and BBs out) = .204. Add back the BBs. = .335 expected OBP.

I haven’t even gotten into directional placement of grounders, so it could be true that DeShields is even better than these projections I just calculated.

Regardless, league averages are .252 average and .314 OBP. DeShields is proving to be roughly a league-average hitter by expected batting average, and clearly above-average hitter if you’re looking at expected OBP.

In other words, DeShields is here to stay.

The struggles of Robinson Cano in 2015 have been talked about frequently, especially as the Mariners’ struggles continue. Recently, Mariners hitting coach Howard Johnson suggested that Cano is pressing at the plate. Cano disagreed with the assessment, but the numbers back up Johnson.

The good news is that when Robinson Cano is making contact, it’s been pretty good. Cano is hitting the ball harder than he has over his career. His hard hit percentage is 35.2%, compared to his career 32.9% mark.  The 24.4% of line drives on batted balls would be the third highest mark of his career, exceeding his 21.4% career average.

The bad news is where Cano is hitting the ball.  Cano is hitting out of character. In particular, Cano has had some difficulty, or aversion, to hitting the ball to the opposite field. The chart below shows Cano’s 2015 batted-ball locations and his career batted-ball locations.

This is a big issue because he is muting his best hitting ability. Cano is a .369 hitter when hitting the ball to the opposite field. Last year he hit .417 when going the opposite way; in 2013 he hit .455. This year he is hitting .303, but he is not giving himself the opportunities to take advantage of the success that has been consistent throughout his career and stellar in his most recent seasons.

The impact of this shift can be displayed by taking Cano’s 174 plate appearances in which he has not walked or struck out, and allocating the results of where the ball is hit by his career average Pull%, Center%, and Oppo%. I then applied his career batting averages for the batted ball location to those figures.

The following would be the resulting average on batted balls, batting average, and on-base percentage based upon Cano’s 40 strikeouts and 12 walks:

These numbers are good, but they are still not remarkable, and they don’t look like the numbers we would expect from Cano.

This leads to Cano’s second issue: increased strikeouts. Cano’s 17.5% strikeout rate is well above his career average of 11.2%.

The Baseball Info Solutions Plate Discipline data shows two figures that stand out. (1) Cano’s Contact% is down 3.9% from his career average and (2) Cano is seeing 5.4% more first-pitch strikes than he has over his career.

Lets start with the second figure. This is nothing Cano has control over and the cause is almost certain to be the presence of Nelson Cruz behind him in the lineup. But how can Cano adjust to this? He’s a batter that’s used to being pitched carefully, particularly last year, when he was a hitting oasis in the desert that was the Mariners’ lineup.

The first figure, Cano’s drop in Contact%, may be tied back to where this article started and the point mentioned above: hitting approach and batting count. Cano has performed pitifully when facing sliders and changeups this year, two pitches he has handled well over his career (see the chart below displaying Baseball Info Solution’s runs above average/100 pitches for each pitch type Cano has faced). This makes sense if he is seeing pitches behind in the count, and if he is aggressively seeking to pull the ball, for additional power; to be worth $24 million a year, or whatever reason that may be causing the change in hitting approach.

Howard Johnson is probably right. Robinson Cano is pressing. Cano needs to approach at-bats like he has his whole career and he’ll see a return to what we would expect from Robinson Cano. However, the Mariners can make it easier on him by changing up the order. Maybe Cano isn’t a hitter that thrives on being pitched to. It may benefit the Mariners to swap Cruz and Cano in the order. While Cruz has been great, the Mariners and Cano have been the opposite. A change couldn’t hurt.

But first, Robinson Cano needs to accept the hitter he is, because that hitter is very good.

I’ve been thinking about this one a lot, and I think people in general still view Price as a top-10 pitcher. I’ve seen him appearing in expert lists as such, and that’s the general vibe I’ve gotten from the fantasy community. I just think top-10 at this point is too high, especially when we’ve got such talented young stars ranked below him, both according to the expert lists and public perception (I’m talking about guys like Archer, deGrom, and Cole).

I’d actually have him closer to top 20-25 at this point (there are so many great pitchers). His K/9 has plummeted to 7.6 and K-BB% has fallen nearly nine percentage points to 14.3%.

At 14.3%, David Price is the No. 39 pitcher in the league in K-BB%.

Am I putting too much stock into a small sample, or has the decline begun, but people haven’t realized it yet (he still sports a solid 3.15 ERA)?

His peripherals also support his regression, as his xFIP is 3.94 and SIERA is 3.87.

Encouraging signs: FIP still has him at 3.27. Swinging strikes are similar to last year at 10.4% (only 0.2% difference). No velocity loss — in fact, his fastball is faster this year than last year.

Over his career, however, Price has been only slightly better than average at giving up/suppressing home runs, so I think xFIP and SIERA are the better ERA estimators than FIP. League average HR/FB is 10.8%, and Price was at 9.7% last year, 8.6% in 2013, 10.5% in 2012. So he may be slightly better than average, but unlikely to maintain 6.6% going forward.

It’s also worth noting that last year’s 9.8 K/9 was a career high. In 2013, he had a 7.3 K/9. From 2010-2012 his K/9 hovered in the 8s (and in 2008 and 2009 his K/9 was also sub-8, although I don’t give any weight to that at all as he was still developing as a pitcher). It could be that his high K/9 last year was an aberration.

I’m choosing to give weight to his current K-rate and peripherals (the sample size is now significant), while accounting for some improvement (this is David Price after all). Doing that, by my rough calculations, I’m looking at about a 3.5+ ERA ~8 K/9 pitcher going forwards.

Those are quality numbers, but not top-10 numbers, which is where people still value him. I’d flip Price for any top-20 pitcher with upside in an instant.

I don’t have an answer as to why the K-rate has plummeted so far. I did take a look at his usages, and he seems to have reduced the usage of his two-seam fastball. His entire career, that has been his most-used pitch. Last year he used it 40%. This year, he’s only throwing it 23% of the time, instead favoring a four-seam fastball as his dominant pitch. I believe this *may* be related to his K-rate drop, but it’s just an observation at this point. Regardless, we’ve reached the point in the season where it might be wise to be proactive.

Imagine a batter hits a long fly ball that’s destined for the right-field seats only for the outfielder on the other team to clear the wall and rob him of his home run. In traditional stat sheets, this is treated the same way as any other out and there’s no real way of distinguishing that from a dribbler down the third-base line. But intuitively we know that these are two very different things, and a batter who does more of the first is going to end up being more valuable than one who does more of the second. Thus, if we wanted to truly measure how well a player has performed, we need to separate the performance from the results. The best way of doing that is to break down a batted ball in the most granular way possible and look at the average performance for similar batted balls, and today I’ll reveal a personal tool to do this. This work was inspired by Tony Blengino’s terrific posts on batted-ball data, and I suggest reading his introductory post as background on the theory and methodology that I employ.

This tool uses information on the type, velocity, direction, and distance of a hitter’s batted balls to calculate an expected AVG, OBP, and SLG for him. It divides batted balls into buckets based on the type (GB, FB, LD, PU) and either the direction and velocity or the direction and the distance and calculates the resulting AVG and SLG for all batted balls that meet that criteria. It then goes through all of a batter’s plate appearances and uses these data to calculate both the observed and expected AVG/OBP/SLG for each PA. The table below shows the top 30 hitters by Expected wOBA (xwOBA) as of 5/26/2015.

The tool uses the velocity and direction, rather than the distance and direction, of a batted ball to calculate the expected values with a few exceptions. If the velocity is not available for a fly ball or a line drive, it uses the distance and the direction of the batted ball to calculate the expected values. If the velocity of the batted ball is not available for a ground ball, the tool assumes it was of average velocity and only considers the direction it was hit when calculating the expected values. It does not consider distance for ground balls, as the distances are calculated using where the ball was fielded, so using distance would be describing what actually happened rather than what we expected to happen. For all line drives and fly balls hit over 375 feet it uses distance and direction rather than velocity and direction. The reason for this is that I do not have information on the hang time of batted balls, and in going through the data I found that fly balls and line drives that traveled over 375 feet but weren’t hit very hard were being severely underrated by the tool. As an example of the underlying data, the table below shows the reference data for fly balls hit to center field.

I’m providing a link to a Google Sheets document with a leaderboard for all qualified batters, along with leaderboards broken down by each batted ball type. The document also contains a reference page that contains all the information for how batted balls performed in each bucket based on 2015 StatCast data for velocity references and 2014-2015 MLBAM data for distance references. The numbers in the reference page will continue to be updated as more data becomes available from StatCast. Feel free to look through this section and point out any inconsistencies you may see, and note that all data comes from BaseballSavant.

I’ve also provided a Methodology Example in the document so you can dig through what the behind the scenes data looks like as it’s being processed. Note that you may see some discrepancies in a player’s actual AVG seen here and his AVG seen elsewhere, as I treat sac flies as regular outs. The “Notes” tab gives a general outline of the procedure, and also contains a link to an Excel sheet that you can download to perform these calculations on your own.

https://docs.google.com/spreadsheets/d/1-XohbJlWIceDS2Rc8_7-rOxv9avU3IwMCecPkUNxlYU/edit?usp=sharing

Before I wrap up, I should also mention the limitations. It’s been noted elsewhere on FanGraphs that the StatCast data isn’t always completely accurate. Also, the tool currently doesn’t incorporate a player’s speed in any way, so guys like Dee Gordon are going to be fairly underrated in terms of their ground ball performance. I’ve been brainstorming ways to incorporate this and am open to any input you may have. Furthermore, I’ve noticed the tool can be pretty stingy with labeling balls as pop-ups and occasionally pretty generous with labeling them as line drives. I’ve noticed some fly balls with velocities over 95 MPH that only traveled 300 feet, indicating they were hit almost straight up in the air. Unfortunately, without data on the vertical angle of the ball off the bat or on the hang time of the ball in play, it will be difficult to fix this issue.

Even with these limitations, the tool works extremely well at determining how well guys have been hitting the ball and identifying who has been helped or hurt by factors beyond their control. Take the time to dig through the data and the code and point out areas for improvement, and I’ll incorporate them in future versions.

Joc Pederson: your leading runner for rookie of the year. Pederson started off at the beginning of the year in the bottom half of the order and was mashing the ball with authority. Don Mattingly had us all scratching our heads when he refused to bat Joc Pederson first in favor of the aging Jimmy Rollins. But after tremendous production from Pederson and lack thereof from Rollins, Mattingly finally made the switch.

Batting mostly from the bottom of the order in March and April, Pederson walked at an ungodly 22.1% and batted his way to an inflated .406 BABIP en route to a .440 wOBA. His strikeout sat at an ugly 28.6% but with his walk rate also in the 20’s, it balanced out nicely.

Upon his move to the leadoff spot, Pederson has gotten very aggressive. His walk rate has plunged, and his strikeout rate has increased; sitting at a 12% and 30%, respectively. Despite these negative trends,  Joc has adjusted nicely and is still hitting the ball with authority… against righties. All of 12 of his home runs have come off of righties and he has compiled a total of 19 extra-base hits against them. Against lefties, Pederson has only collected one extra-base hit, a double off of Madison Bumgarner on 5/21, and has walked only once. Given these struggles, Joc has hit a bit of snag lately dropping his average tremendously and has neutralized his BABIP a bit. He has hit for the occasional home run but that’s about it.

Is it time to panic? Absolutely not, Joc is a rookie and has only 35 plate appearances against lefties, making it a small sample size. He’s raised his average against lefties above .200 at the end of May suggesting he is starting to adjust. However, for those who own Pederson, be aware he is young and is still going through the growing pains of being in the majors. Continue to send him out in your lineups, but be aware that the production he had in the bottom order may not continue through the year in the leadoff unless he adjusts. Given his talent, that’s certainly possible.

Gregory Polanco has been one of the most talked-about Pirates prospects in recent years. Baseball America rated him #51 and #10 respectively in 2013 and 2014 rankings. Moreover, prior to the 2015 season, Jayson Stark wrote an article for ESPN entertaining the idea that the Pirates may have the best outfield in baseball with Gregory Polanco, Andrew McCutchen and Starling Marte.

When Polanco came up in June of 2014, the budding 22-year-old looked like the future McCutchen. With a home run in his first at-bat and a strong slash line in his first month (.288/.374/.375), Polanco looked like the real deal. After a few weeks of success, his game leveled out. Most writers attributed his downfall in the latter half of the 2014 season to the fact that he had played 127 games in 2013, then went to Winter Ball, and followed that up with 158 games in 2014. And there may be some truth to that.

Fully rested, Polanco came into the 2015 season with high expectations. To this point, his fielding has been stellar: he is tied for 1st amongst all right fielders with Giancarlo Stanton in UZR at 3.6 and is in sole possession of 1st in DEF at 2.0.

He is definitely holding up on his defensive abilities, but to this point, his offense has been underwhelming. His OPS has dropped from .650 in 2014 to an abysmal .626 in 2015. Thus far, he has only hit one home run on the season and driven in just 12 runs. The gleam of light in his offensive game has been his work on the base paths. Stealing 40 bags in A-ball in 2012 and then a combined 38 from A+, AA, and AAA in 2013, Polanco was projected to be a speedster, but his decline from there seemed alarming. He stole only 14 bases in 19 attempts in 2014 for the Pirates, but is already close to eclipsing that mark. Polanco is 12 for 14 in steal attempts this year, and in most cases is doing so based on good reads and strong jumps rather than on speed alone.

Most casual fans appreciate the defense and see the potential as a real threat on the base paths, and simply hope that he shows more pop as he matures at the plate. But there are also other warning signs with Polanco. He was to work on his plate discipline this year, but his walk rate has actually lowered from 9.6% to 8.9% and his strikeout rate has risen from 18.9% to an unsightly 22.6%. Especially if he will not produce power numbers, these numbers are trending in the wrong direction.

I started thinking back to other Pirates who were young and showed promise only to become mediocre players, and then I found this.

Each through 131 Career Games:

Player A: .286/ .341/ .386 – 26 doubles; 7 home runs; 41 RBIs; 44 walks; 28 stolen bases

Player B: .235/ .306/ .336 – 19 doubles; 8 home runs; 45 RBIs; 45 walks; 26 stolen bases

Player B is obviously Gregory Polanco, and player A is Jose Tabata.

In no way am I saying that Polanco will fall in the footsteps of the powerless Tabata, but the numbers are eerily similar. With Tabata, writers and fans alike hoped for the same changes: better discipline at the plate, the stolen bases to continue to rise, and the power to follow.

The big difference between Polanco and Tabata is that Polanco is an elite fielder today. Through advanced metrics, it is easier to quantify the impact of guys like Jason Heyward and Alex Gordon, and perhaps that will be the same fate of Polanco. Since 2010, Heyward is ranked 19th and Gordon is ranked 13th in cumulative WAR according to FanGraphs. Defensive impact should not be overlooked.

Polanco is still young, and he has tremendous upside. Even if the power stroke never emerges, he can still be a great player. As the 2015 Pirates season continues, Polanco needs his strikeout rate to drop, his on-base percentage to rise, and to run on a more frequent basis.

Here at FanGraphs, it is gospel to say that a pitcher’s ERA is related to both skill and luck. The skill comes from being able to get batters to swing and miss or to induce weak contact, while limiting walks and home runs. The luck comes from how well the other players defend, and also the sequencing of events. That last element of luck, sequencing, merits a brief conversation.

There is little difference, from a pitcher’s skill point of view, between consecutive hits and hits in separate innings. That is to say, a pitcher’s skill is related to how many hard hit balls he gives up; a pitcher’s luck is related to when those hard hit balls occur. So, ERA is affected by the timing of hits, which we can measure easily using LOB%, which is the percentage of base runners that do not score at the end of an inning. It’s not this simple, but basically, a low LOB% rate means the pitcher has been unlucky, and a high LOB% rate means he has been lucky.

The average LOB% in 2015 so far is 72.4%. James Shields‘ LOB% is 87.8%. This is significant! Seven out of eight runners that reach base on him get stranded! His ERA should be anemic, right? Wrong. His ERA is a respectable 3.74, but this is unexpectedly high given what I have told you so far. Clearly, I haven’t told you everything.

There was concern during the offseason that James Shields’s fly ball tendencies would be problematic in the spacious Petco Park with a highly questionable outfield defensively. I guess his home ballpark isn’t spacious enough, because Shields is allowing a career high 2.28 home runs per nine innings, and 25.5% of the fly balls he surrenders leave the ballpark.

Meanwhile, Shields is also striking batters out at a significantly higher rate than his historical numbers indicate he should be. In fact, Shields is striking out batters at a greater rate than any other qualified starting pitcher (and most unqualified ones too!). Opposing hitters are also swinging and missing against Shields more frequently than any other pitcher, even more than highly sophisticated robot and Rust Cohle impersonator Corey Kluber!

When contact is made against Shields, though, it’s been hard contact. According to our new quality of contact statistics, only three starting pitchers have given up a higher percentage of hard contact than Shields. Batters rarely make contact, but paradoxically, when they do make contact, they’re hitting ropes.

This is confusing, and I don’t know why it’s happening, though I can speculate. Shields picked up a knuckle curve a few years ago, and he’s throwing it this year almost a quarter of the time. He’s a good pitcher, and it’s probably a good pitch, which explains the swinging and missing. However, it’s also a new pitch, and he’s probably also making a fair amount of mistakes, which hitters are taking advantage of.

That all made sense to me until a quick PitchF/X search told me that only one of the dingers off Shields were on curveballs. Back to Square 1; I have no idea why this is happening, and it will probably take someone smarter than me to figure it out, or it’s just a sample size issue.

In conclusion, let’s look back at the definition of LOB%. It measures the amount of batters that are left on base when an inning is over. Shields’s high K% probably helps inflate that LOB%. But, it’s also small sample size, and I’m not talking about early season small sample size (although that is probably also a factor). When a high percentage of hits given up are home runs, there are no runners to leave on base in the first place! James Shields is striking out and walking batters, and giving up home runs, all at a career high rate. And it’s kind of working.