Archive for Player Analysis

Wow. Craig Kimbrel traded right before the start of the season. I have to admit to being rather shocked. I know the Braves are rebuilding this off-season and it made sense to trade him. He is very highly valued for a player who only pitches 60 innings a season, perhaps over-valued. If the Braves are going to be hopeless this year then who needs a dominant single-inning pitcher?

The trouble is I love watching Kimbrel pitch, no matter the situation. I live in London, in the UK, so a lot of Braves games happen from 1-4am and I don’t get to watch them live. Every morning I use my MLB.com subscription to check the last night’s action. If I don’t have the time to watch the whole game, which is common, I skip to the innings where the Braves scored plus any inning Kimbrel pitches. Pace, a banana curveball and strikeouts, Kimbrel is one of those rare players who is worth watching every minute he plays. Even when he is (rarely) hit you feel a strikeout is coming next. So emotionally, I hate to see him traded (just like I hated seeing Heyward traded). Lots of reporters are saying the trade is a good deal for both sides or an outright win for the Braves, so in emotional despair, I thought I’d have a proper look into it.

The facts of the trade

To the San Diego Padres:

• Craig Kimbrel – 3 years at $34.75m (includes option buyout) or 4 years at $46.75m
• Melvin Upton Jr – 3 years at $48.15m

To the Atlanta Braves:

• Carlos Quentin – 1 year at $11m (includes option buyout) or 2 years at $18m
• Cameron Maybin – 2 years at $16.2m (includes option buyout) or 3 years at $24.2m
• 2 prospects and 41^st pick 2015 draft

N.B. Bold text highlights the likely choices.

I’ll not be analysing the prospects in much detail, instead ignoring the less relevant trade pieces and looking at the end outcomes. My method is below, but if you like, skip to the summary, that’s the important bit.

Methods

From the Padres POV

• Upton not wanted/needed. Treat him as a league-minimum replacement-level 5^th outfielder for 3 years (cost $1.5m). Add the rest of his salary to the Kimbrel contract.
• Dumped 2 unneeded players and $27.2m in contracts off the books. Remove these values as savings for the Kimbrel contract
• Gained Craig Kimbrel. Assume option taken (it is great value – see later*). Contract for 4 years at $46.75m – $27.2m (from Quentin and Maybin savings) + $46.65m (Upton cost)
• Given up 3 prospects (effectively); 1 good (Wisler), 1 risk (Paroubeck) and 1 draft pick

I feel these are all reasonable assumption/treatments. The Padres want Kimbrel, don’t care much about what they get from Upton (assuming he continues as in 2013-14) and used the Quentin and Maybin savings to pay for it all.

From the Braves POV

• Quentin not wanted/needed (not sure why – seems a better bench bat than most and nobody will trade for him as they know they can get him for minimum once the Braves cut him). Add his contract to the 2015 payroll – $11m
• Maybin – Assume continues poor health/form and option buyout is taken. Treat as decent defensive replacement OF (23 career DRS in 8 years). Possibly gets 75 games a season but produces nothing more than T.Cunningham in AAA so set effective salary to league minimum – $1m for 2 years. Add rest of his contract to 2015-16 payroll ($15.2m over 2 years)
• Payroll changes:
  • Savings – Kimbrel ($46.75m – 4 years), Upton ($48.15m – 3 years)
  • Wastings – Quentin ($11m – 1 year), Maybin ($15.2m – 2 years) – both include buyouts
• Receive 3 prospects (effectively); 1 good (Wisler), 1 risky (Paroubeck) and 1 draft pick

Again, I feel these assumptions/treatments are reasonable. Maybin may produce better than this, but his batting numbers were as bad as M. Upton the last few years (70-80 wRC+) so I don’t think we can expect much more of him than Melvin (apart from his defence being better).

Summary

Padres POV

• Get Craig Kimbrel – effectively 4 years for $66.2m ($16.55m/year)
• Get spare replacement-level 5^th OF at minimum salary for 3 years
• Lose 3 prospects; 1 good, 1 risky, 1 draft pick

Braves POV

• Lose Kimbrel (and M.Upton)
• Get spare replacement-level 4^th OF at minimum salary for 2 years
• Payroll savings $67.7m over 4 years ($16.9m/year average)
• Get 3 prospects; 1 good, 1 risky, 1 draft pick

Analysis

Lots of contract money going back and forth, but the end result is that the Braves get payroll savings of around $17m a year for 4 years and 3 prospects and the Padres give up 3 prospects to get Kimbrel at a reasonable free agent price* of around $17m a year for 4 years.

If you consider that the Padres would have lost that 3^rd prospect (the draft pick) if they signed Kimbrel as a free agent, the deal starts to look pretty good for San Diego and AJ Preller. The Padres almost certainly wouldn’t have been able to sign Kimbrel as a free agent with other teams competing (everyone needs a Kimbrel and the Dodgers/Yankees/Tigers/Red Sox etc all have the money for him). The contract would certainly have been longer as well (see footnote on Kimbrel’s historic value*). The Padres are paying Kimbrel a lot, but the amount is fair and they didn’t give up much.

The Braves had signed Kimbrel to a much friendlier contract than he would have got as a free agent (he’s homegrown and a Braves fan so gave a large discount – again see footnote*). Kimbrel gets $13m /year for his free-agent years, when he could have had much more. John Hart effectively used Kimbrel’s generosity to swap the spare value for 3 prospects, one of whom is extremely risky (Paroubeck) and one who is completely unknown (the draft pick). The Braves have rid themselves of Upton, but in taking back other contracts they have effectively only saved the money they should have been paying Kimbrel (had he not given a home discount).

In conclusion, John Hart basically declared he didn’t want a well-paid but high-value closer and swapped it for one good (but not great) prospect and two unknown prospects. So how do I feel now? I would have preferred to watch Kimbrel play for my team every week… Enjoy it San Diego.

*A footnote on Kimbrel’s free agent value

Craig Kimbrel is currently 26 years old and 10 months. Below is a summary list of contracts for comparable relievers and their ages when signing.

You’ll notice that Kimbrel is younger than them all and although the average yearly value is not as high as Kimbrel’s $13.0m 2016 salary, the elite arbitration-eligible relievers are likely to beat them all (apart from maybe Rivera). If he were a free agent this last winter, you can assume that he would have been offered 5-year (and possibly longer) contracts.

Kimbrel’s career numbers are also historically unprecedented at his age. This has been said many times before, but my favourite Kimbrel stat is the WAR leaders for relievers over the last 10 years. Kimbrel has the 5^th highest WAR from 2005-2014. He entered the league at the end of 2010. Since entering the league in 2010 he leads reliever WAR by 1.5 over Holland and Chapman (who have comparable service time). Before signing his (very team friendly) extension Matt Swartz estimated his first year of arbitration salary should be $10.2m. For a detailed analysis of how much Kimbrel is worth I recommend you read his article (http://bit.ly/1GEjKyT). The point being, he is probably worth at least a $17m/year, 4 year contract.

References

http://www.spotrac.com/mlb/san-diego-padres/melvin-upton/

http://www.spotrac.com/mlb/atlanta-braves/cameron-maybin/

http://www.spotrac.com/mlb/san-diego-padres/craig-kimbrel/

http://www.spotrac.com/mlb/atlanta-braves/carlos-quentin/

http://www.fangraphs.com/statss.aspx?playerid=5015&position=OF

http://www.fangraphs.com/statss.aspx?playerid=5223&position=OF

http://www.fangraphs.com/blogs/evaluating-the-prospects-san-diego-padres/

http://www.baseball-reference.com/players/r/riverma01.shtml

http://www.fangraphs.com/leaders.aspx?pos=all&stats=rel&lg=all&qual=y&type=8&season=2014&month=0&season1=2005&ind=0&team=0&rost=0&age=0&filter=&players=0

http://www.mlbtraderumors.com/2013/10/arbitration-breakdown-craig-kimbrel.html

It’s September 10^th, 1999, and the small flame-throwing right-hander from the Dominican Republic just struck out Scott Brosius and Darryl Strawberry. He’s about to get Chuck Knoblauch swinging (and missing) on 1-2 count for his 17^th strikeout of the night to finish the game. He does, and the fans at the old Yankee Stadium go nuts, for they’ve just seen Pedro Martinez’ finest start in the greatest pitching season of all time. The final score is 3-1, with the only Yankee run, and hit, coming off a Chili Davis home run. Pedro is 5’11’’ and 170 lb, one of the smallest pitchers in baseball. While most players tower over him off the mound, Pedro writes a different story when he’s pitching. The Yankee hitters fail to notice his height when he kicks his leg up, down, and serves a 95-mph fastball from a three-quarters delivery at their eyes.

The average male height in the U.S. is 5’10’’. You’d never know this from watching a baseball game, where the average height is about 6’2’’, with pitchers just a little taller at about 6’3’’. We all remember the success Randy Johnson had at 6’10’’, and his height was always considered an advantage. When we watched Pedro Martinez, however, commentators and baseball men viewed him as an exception to some obscure and unwritten rule: that shorter athletes can’t become successful pitchers.

Six feet, like 30 home runs or a .300 batting average, has become a number associated with a distinct meaning. If you hit 30 home runs, you’re a power hitter. Hit 29 homers, and you have some pop. If you hit .300, you’re a great hitter. Hit .299, and you just missed hitting .300. Similarly, if you’re six feet, you can pitch. If not, you’re short, but at least you might get an interesting nickname like Tim Lincecum’s (5’11”) “The Freak.”

Most Major League pitchers fall between 6’1’’ and 6’4’’. We can look at the height distribution for pitching seasons of the last 5 years and see that it’s approximately normal:

By this approximation, the chance of randomly selecting a pitcher of the last 5 years who is shorter than 5’11’’ is about 5%.

Are short pitchers really destined to fail? We’ve all been told that it’s better to be taller if you pitch. But is this true? Let’s consider short pitchers to be 5’11’’ or under and examine their effectiveness and distribution in comparison to taller pitchers, who we’ll consider to be 6 feet or taller.

The top ten best pitching seasons for shorter pitchers of the last 5 years are:

We notice that Tim Lincecum appears on this list twice and Johnny Cueto appears on it three times. All of these pitchers are 5’11’’ with the exception of Kris Medlen, who is 5’10’’. So, we see that successful pitching seasons by short pitchers don’t come completely out of the blue. Short pitchers can be successful and can dominate batters, most of whom are much taller, as Cueto did last year and in 2012.

In fact, short pitchers aren’t all that rare to come by, although they’re considerably rarer than taller pitchers. In the last 5 years, there have been 23 instances of short starting pitchers throwing at least 150 innings. In comparison, there have been 402 instances of this type for taller starting pitchers.

Shorter pitchers are generally relegated to the bullpen; there have been 95 instances in the last 5 years of full-time short relief pitchers and 968 instances of full-time taller relief pitchers.

We can see the average WAR breakdowns for all of these pools of players in the following table, along with P-Values for a two-sided t-test comparing the short relievers against the tall relievers and the short starters against the tall starters:

What the 0.0005 is telling us, here, is that we would observe these results by chance alone with probability 0.0005. Thus, there is actually a significant difference in the mean WAR for short relievers and the mean WAR for tall relievers (obviously favoring short relievers). On the other hand, the difference between the starters is not significant. Either way, we have no evidence to suggest that shorter pitchers are any less effective than taller pitchers.

Are shorter pitchers undervalued in the baseball market? If so, to what extent? We can approach this by examining the WAR value of a pitcher relative to his salary in free agency. We can do this by comparing the height groups within relievers and starters (since relievers are generally valued differently than starters).

However, we find that in the last five years, there are only 4 instances of a starter 5’11’’ or shorter pitching for a team that acquired him via free agency; and all of them are Bartolo Colon seasons from 2011-2014.

Fortunately, there are more instances of this in relievers, which is what we’ll examine. We notice the distribution of WAR and relievers’ salaries in free agency:

We see that short and tall relievers are clustered between -1 and 1 WAR and $1 million and $5 million dollars. However, we see several taller relievers past the $7.5 million mark with unremarkable WARs, which we don’t see for shorter relievers. From this, we would suspect that taller relievers are being overvalued while shorter relievers are being undervalued.

This is, in fact, the case: short relief pitchers are producing 2.33 WAR for every $10 million they earn in free agency while taller relievers are producing 1.36 WAR for every $10 million they earn. In comparing these values with a one-sided t-test, we acquire a P-Value of 0.0018, meaning these are results we would acquire by chance only .18% (a significant value) of the time. And so it goes, relievers under 6 feet are actually about 1.7 times as valuable as their taller counterparts.

Is there something inherently different about shorter pitchers that makes them less capable of pitching successfully in the big leagues? The evidence says no. In fact, it might be more worthwhile for General Managers to draft pitchers under 6 feet tall and reap the rewards.

Just because an athlete doesn’t tower over his opponents off the mound, doesn’t mean he can’t bring 55,000 dumbfounded Yankee fans to their feet on an unassuming September evening.

Robinson Cano’s power vanished in 2014 without a clear explanation.  Most believe that he will be valuable even if the power does not return.  I think Cano’s risk going forward is greater than meets the eye.

After sporting an ISO of at least .199 every year from 2009 to 2013, Cano posted a mark of .139 in 2014.  There is reason to believe that this power outage is permanent.  Robinson Cano was a different kind of hitter in 2014.  His ground ball percentage was 53% (up from 44% in 2013), and his average HR/FB distance plummeted from 292 to 278.  Cano was mostly incapable of hitting fly balls to his pull side, which is where his home-run power used to be, despite swinging at more pitches middle-in.  Cano’s aging bat may be unable to turn on major-league pitching the way it used to.  As noted elsewhere, Cano’s 2014 power numbers had little to do with the move from Yankee Stadium to Safeco.  His problem was that he hit the ball in the air less frequently, with less authority, and to the wrong side of the ballpark.

Aging may have played a role, but it is unusual for an elite slugger’s power to disappear at age 31 without something else going on.  Perhaps Cano was dealing with an injury.  Perhaps his amazing run from 2009-2013 was fueled by PEDs.  We don’t know.  But consider the similarities between Cano’s pre-elite 2008 line and his line from last year:

It’s easy to forget that Cano was a replacement level second baseman in 2008.  BABIP (along with the changing run environment) is mostly what separates his 2008 replacement level performance from the five-win version of Cano we saw in 2014.  The stability of last year’s BABIP may be the key to Cano’s value going forward—a terrifying thought for the Mariners, who presumably did not intend to invest $240 million in the vagaries of BABIP.

There is conflicting data on what to expect from Cano’s balls in play in 2015.  For example, ZIPS predicts .323—not so bad.  Jeff Zimmerman’s xBABIP formula predicts .299—much closer to the 2008 disaster scenario.  Neither of these predictions fully accounts for shifts, and Cano’s performance against them in 2014 is concerning.  His BABIP was .388 against the shift and .303 without it.  This is disconcerting because Cano displayed no such shift-beating prowess before last year, and his 2014 spray chart suggests no change in his approach that would justify any BABIP spike.  To the contrary, last year Cano hit an alarming number of grounders to the right side of the infield, which should have favored the shifted infield defenses.  It appears that Cano got lucky—perhaps very lucky—with his 2014 balls in play.  My money is on something closer to the xBABIP prediction for 2015.

Cano went from an elite slugger to a BABIP-fueled slap hitter in a short period of time.  His 2014 output was akin to an early-career Ichiro, except unlike Ichiro, we lack assurances that Cano will maintain the high BABIP.  If the power is truly gone and the BABIP craters, he’s toast—or at least something closer to league average.  The risk of collapse is higher than most want to believe, if for no other reason than this same risk was once realized by the same player.

2014 was a disappointment for David Wright, posting his lowest career numbers in almost every offensive category: OBP, SLG, OPS, ISO, wRC+, wOBA, and WAR. Cries of Wright being washed up began springing up immediately – he’s a 31-year-old who saw significant drops in almost every offensive category possible. However, everything might not be as it seems.

Wright injured his left shoulder early in the season and tried to play through it before finally getting shut down in September. Here’s Wright’s home run chart for 2014.

Now here’s his home run chart for 2012-13.

Do you notice a difference? Wright did not hit a single home run to right or center field the entire season last year, and that’s always been something of a trademark for him. The injury to his front shoulder had a clear effect on his opposite field power, and that effect (or lack thereof) was apparent in yesterday’s Mets-Nats spring training game, where Wright did this.

Now that right there is something that Mets fans haven’t seen since Wright’s 2013 season, where he did it rather regularly, such as this home run against Craig Kimbrel.

Look at those two swings: the exact same swing, both demolishing the ball to the same spot of the field. By all accounts, David Wright is healthy. His shoulder is 100% and he’s in The Best Shape of His Life. In baseball, you never want to use a sample size of one to draw a conclusion, but when Captain America comes into the season showing off the trademark power he didn’t show in the Mets’ previous 162 games, there’s plenty of reason to get excited.

Just look at this swing. That’s the swing of a man ready to put America (and the Mets) on his back.

Note: Post was written on March 12. Truths may be falsehoods by the time you read this.

So Nook Logan was trending on FanGraphs this morning. Still is, in fact, as of this writing—he is eighth on the “Major League Players” list, sandwiched between Clayton Kershaw and Yasiel Puig—and that piqued my interest. So I looked at his FanGraphs profile. Upon this inspection, I found that Logan seems to compare quite favorably to another center fielder who started with an AL Central team and moved to a NL East team: Ben Revere. Logan, from a quick glance at his stats, appears to have good speed, but no power and a good glove, but no arm: all traits possessed by Mr. Revere.

First, let’s establish each of these characteristics.

Logan stole exactly 23 bases in each of his two full seasons: 2005 with Detroit and 2007 with Washington. Revere has played three full seasons, with at least 34 steals in each of those seasons, plus another 22 in an injury-shortened 2013. So maybe Revere has more speed. According to their Speed Scores (Spd), where Logan is rated at a 7.4 and Revere at a 7.0, Logan is faster, or at least utilizes his speed better. Both are “Excellent” scores, though, and the FG glossary tells us to look at UBR, so we do. “Hmm,” we say. “Revere has a much higher UBR than Logan.” And yes, this is true. And yes, Revere also has higher values in everything related to base running. So we’ll say that Revere is a far superior baserunner despite Logan’s slightly better speed.

“No power” is not hard to determine. Logan has never had an ISO above .089. Revere has only had two ISOs above .100, and he hit a combined one home run in those seasons (his first two minor-league seasons). He was just hitting and running, and getting a lot of triples. Such is the life of a minor-league speedster. But I will take this time to mention something: Revere has a career K rate of 9.1%, only rising above ten percent three times—once in the minors, once in his “cup of coffee” 2010, and once in his injury-plagued 2013. His walk rate, though, is bad. Like really bad. Like it was barely above the amount of fat in my milk last season bad. Logan, on the other hand, had a better (and more consistent) walk rate, hovering around six percent his entire career (save for an 8.6% rate his rookie year). But he struck out a ton. Or, rather, he struck out a normal amount, then got sent down by the Tigers and started striking out a lot more, and continued to strike out at high rates after being dealt to the Nats. I don’t know what to make of this data, but it is a dissimilarity.

Now we turn our attention to a section I didn’t mention in what an English teacher might call my thesis sentence: batted-ball rates. Both men hit a high amount of ground balls, over 50 percent in all but one season (Logan’s short 2006 campaign, when he hit only 46.7% ground balls, is the lone exception). This doesn’t give us the full picture: while Logan certainly hit his fair share of grounders, he also hit a fair amount of fly balls, checking in at 29 percent for his career, with a 7.9% infield fly rate. Revere, on the other hand, hit so many ground balls that it might be considered unhealthy if he weren’t so doggone fast. Revere has managed to hit about one-seventh of his BIP in the air, and only 3.4 percent of that has been represented by popups. So, um, Logan hit more fly balls, but the same amount of home runs (two). Say what you want about the fact that he would have four in Revere’s sample size.

And now, defense: the hardest part to talk about, because there are so many ways to statisticize (that can’t be a word) it and none of them have become the “standard” method of measuring defensive contributions. First, we’ll only be discussing Logan’s and Revere’s performance in center field, because it is their primary position and also because they have played a not-dissimilar amount of games at that position. First of all, when normal people (I’m weird, you’m weird, everybody’m weird) talk about defense, they think of errors and assists, probably. Or just they think of how many times they saw that one guy make that one catch—you know, the one where he dives, makes the catch, makes another, leaping, catch at the wall, and then throws out all twelve baserunners, including the guy going from fifth base to shortstop, saving the game and making it onto the Top Ten playlist seven hundred million times. Okay, that was a lot of mumbo-jumbo that basically meant, “normal people don’t think in terms of UZR and TZL, they think in terms of highlight plays and errors”.

And now, the actual discussion of defense. Logan had eight assists, 11 errors, and a .985 FLD% in 306 games. Revere had 13 assists, 13 errors, and a .986 FLD% in 362 games. So it can be said that both are players who do their job mostly, but also have little to nothing in the way of arm strength. ARM thinks that they’re basically the same player, but UZR thinks that Logan is eighty times better at defense than Revere, so okay. They have very similar Fielding values, though. ¯\_(ツ)_/¯

So yeah, maybe this served no point, but I like writing semi-pointless things about semi-obscure players. Maybe you can expect more of the same in the future.

Matt Harvey is getting all the hype these days, touching 99 mph on the gun, throwing nasty 84 mph curves, and looking healthy. I think he will have an excellent year. For some reason though, the world at large is still underrating Jacob deGrom.

First off, I recommend you read this FanGraphs article from midsummer, detailing the changes he made to his pitching mechanics to make this “rags to riches” leap into the upper echelon.

I’ve been notoriously high on deGrom since I watched him pitch. I wrote about him on reddit back in July 2014. I’ll update the numbers I used, infra:

He’s been excellent — and not in any flukey kind of way. deGrom’s pitch types and peripherals support that what he did last year is VERY REAL.

Let me reiterate last year’s line: 140 IP (178.1 IP of usage), 9.2 k/9, 2.69 ERA, 1.14 WHIP, 2.67 FIP, 3.03 xFIP, 3.19 SIERA. Those are top-20 numbers. And unlike phenoms that regress with time (see Jesse Hahn in 2014), deGrom only got BETTER as the innings racked up.

That is what we love to see — for three reasons:

(1) His body can withstand the rigors of a 200 IP season,

(2) He IMPROVED, rather than regressing, and

(3) Hey, for those of us in H2H leagues, we want our guy pitching well for the fantasy playoffs!

His control improved with time, with increased strikeouts. As of my last post, he had an 8.8 k/9 and 2.7 K/BB. He ended year with a 9.3 k/9 and 3.4 K/BB. We love to see improvement in both those respects. Keep the walks down and strikeouts up, and success often naturally follows!

He’s generating a lot of swinging strikes. For reference, the league average sw/str% is approximately 8.6%.

Jacob deGrom has an overall 11.9 sw/str%, which is well above league average. Looking at pitch F/X data, his slider (12.4% sw/str%, 46/370 pitches), changeup (20.2%, 55/272), both fastballs (10.8%, 108/1000), and curveball (16.0%, 34/212) are all above-average, strikeout-quality pitches.

deGrom essentially features a five-pitch arsenal. Of 2,225 MLB pitches thrown:

44.9% (1000/2225) Fastballs averaging 93.5 mph. Max Velocity, 97.3 mph.

16.5% (368/2225) 2-Seam Fastballs averaging 93.2 mph, Max Velocity, 97.4 mph.

16.6% (370/2225) Sliders averaging 86.8 mph, Max Velocity 91.3 mph (adding mph to his slider is a huge part of his success).

12.2% (272/2225) Changeups averaging 83.9 mph.

9.5% (212/2225) Curveballs averaging 79.3 mph.

3 Cutters–not really a pitch he uses.

deGrom has a diverse arsenal of pitches, with some legitimate velocity differentials, and a good fastball, topping out at 97+ mph. He has 7 mph between fastballs and slider. 10 mph between fastballs and changeup. 14+ mph between fastballs and curveball. 22.5 mph between the high-end spectrum of his fastball and low-end spectrum of his curve.

Essentially, deGrom is legit. His peripherals and Pitch F/X data don’t really suggest that he’s due for any significant regression. Citi Field is still an excellent pitcher’s park, despite the fact that the fences were recently moved in (3-11 feet). I don’t think it will make a significant difference; maybe a home run or two leaves the park that wouldn’t have before.

It’s worth noting that his top speeds increased late in the year, logging his highest speed fastball in the second half of the season. Again, I love a pitcher that doesn’t fatigue.

Concerns: He had Tommy John surgery in 2010, but it seems he has worked his way back from that. Sophomore slump or hitters figuring him out are worth considering. And of course, a couple fly ball outs might turn into home runs.

Fearless prediction: 32 games, 210 IP, 2.80 ERA, 1.05 WHIP, 234 Ks (10 k/9) – and deGrom finally gains some respect withing the fantasy baseball community as a top-15 fantasy pitcher. That bold prediction being said, I think he’s being criminally underrated in fantasy drafts, with his ADP of 112 in yahoo leagues.

112! At that price, go ahead and reach.

I have every reason to hate Dee Gordon.

Prior to the 2012 season, I found myself struggling to figure out who would get the final keeper slot in a longtime, highly competitive fantasy league I played in. It came down to two players: Mike Trout and Dee Gordon. They both would have cost me the same, but Gordon was coming off a rookie campaign where he batted .304 with 24 steals in a miniscule 224 at-bats. Trout, on the other hand, was heading into 2012 with what seemed to me like a more clouded future. He had just posted a pedestrian .671 OPS with a 22.2 K%–albeit as a 19-year old–the year prior. He was also blocked in LF at the time by the great Bobby Abreu, and was looking at possibly another year of seasoning in the minors. In the end I chose Gordon, and the rest is terrible, nightmare-inducing history.

So how strange that I find myself here now, defending Dee Gordon, the very man who hoodwinked me into choosing him over Mike mother-flippin’ Trout.

Ironically, I think the hate for Gordon has gone a bit too far this year. It’s odd to think that there’s any hate for a guy coming off a season where he led all of baseball in steals while also posting a top-25 batting average of .289. But some people seem awfully down on the guy coming into 2015. Perhaps they too were burned by his 2011 breakout, and refuse to make the same mistake twice. Though I can’t fault them if that is the case, there is reason to believe that Dee Gordon’s days of breaking our hearts are over.

The first thing to point out are his batted-ball rates. As the graph illustrates, there weren’t any earth-shattering changes occurring here. It is worth noting, however, that Gordon set a career high in groundball percentage and a career low in fly-ball percentage. And if you’re willing to consider 2013 an aberration like I am (he only managed 106 plate appearances that year), he has actually been gradually trending in the right direction with both his fly-ball and groundball percentages while maintaining a fairly steady line-drive rate. Spikes in groundball percentages are rarely considered ideal, but when a player has the elite speed Gordon does, the odds of turning a weak dribbler or a grounder towards the hole into a hit get a very favorable bump.

Which brings me to perhaps the most eyebrow-raising aspect of Gordon’s 2014 season: his bunt-hit percentage (BUH%). After averaging a 28.5 BUH% over the prior three seasons, Gordon posted a ridiculous 42.6 BUH% in 2014. To put that number into perspective, here’s how it stacked up against the league’s other elite speedsters:

Bunting for hits is a skill. The fact that his success rate rose by nearly 15% last year tells me that he worked on and dramatically improved this skill. Perhaps more importantly, though, it tells me that he’s keenly aware of how dangerous a weapon this skill can be for him when used effectively. When paired with his declining fly-ball rates–and especially his new career low IFFB% of 8%, down from 13.2%–the numbers start to paint the picture of a player who may have finally begun to consciously tailor his plate approach to his strengths.

While I will never forgive Dee Gordon for what he did to me, I do see reasons to be optimistic about his 2015 season. Should his elite ability to bunt for hits carry over into this season, his .346 BABIP shouldn’t see as much regression as people seem to think, and another year of plus average and a stolen-base crown seems well within his reach.

Brandon Inge, Superstar.

How many wins is chemistry worth? Do nice guys really finish last?

As a Pirates fan since birth, I’ve grown used to my baseball fandom engendering a sense of sympathy in others. Born in 1989, I came of baseball-loving age in the mid-nineties, immediately following the halcyon Bonds/Bonilla/Van Slyke & co. days and immediately preceding the less-halcyon days of the Aramis Ramirez-for-Bobby Hill trade, “Operation Shutdown,” the expansion-drafting of Joe Randa, Pat Meares’ general existence, the Moskos pick, the Matt Morris trade . . . (list of soul-crushingly depressing baseball stories truncated for reader’s mental health).

And yet I remained faithful, despite having no conscious memory of a Pirates team being anything other than heartbreakingly awful. I’ve since likened this experience, in conversations with friends, to Linus sitting in the pumpkin patch each year, waiting for the Great Pumpkin to appear. It sometimes seemed that the Great Pumpkin would never come.

It’s ironic, then, that in the year that finally saw the Great Pumpkin arrive in Pittsburgh (2013), the same city also witnessed the end of the career of one Charles Brandon Inge.

Inge, nicknamed ‘Cringe’ by some of the crueler Pittsburgh faithful for his anemic .181/.204/.238 batting line during the 2013 campaign, was at that point in his thirteenth season as one of baseball’s premiere utility men, playing every position on the diamond during his career. During his peak, he was a slick-fielding third baseman who also clubbed 27 HRs en route to a 4.1 fWAR season in 2006. But by 2013, Inge was 36 and on his way out of the league. Signed before the season to provide depth behind Pedro Alvarez and Neil Walker, Inge’s poor performance eventually led to his unceremonious release by the Pirates at the end of July.

And yet, this article has less to do with Inge’s on-field merits (which, as the previous paragraph suggests, were both significant and significantly variable), and more to do with Inge’s impact off the field. Inge won the 2010 Marvin Miller Man of the Year Award, given to the player whose “performance and contributions to his community inspire others to higher levels of achievement,” for his work with C.S. Mott Children’s Hospital. A frequent visitor to C.S. Mott, Inge also donated $100,000 for a new infusion center to treat pediatric cancer and twice hit home runs for young cancer patients. Dude’s a nice guy.

Perhaps more relevant, though, is pitcher and noted stathead Brandon McCarthy’s statement that Inge and fellow veteran Jonny Gomes had been worth twenty-four wins to the 2012 Athletics through chemistry alone. Normative ethics aside, it’s impossible to measure the moral character of a man—but we can measure, or at least attempt to quantify, the impact he has on his teammates.

Intrigued, I set out to determine whether Inge, patron saint of chemistry and all-around good guy, really made such a gigantic difference to his teammates’ performance. Mine is not the first investigation into this topic—Baseball Prospectus’ Russell A. Carleton examined the same issue in March of 2013, and there have been numerous attempts to place a valuation on chemistry over the years. But as you’ll see, there are some methodological differences to our approaches, and the differences expose some interesting conclusions.

Methodology

There is no ironclad way to assess Inge’s potential effect on his teammates, short of cloning entire teams of players, randomly assigning Brandon Inges to some of them, and having them play a large number of seasons.

In order to determine Inge’s value as accurately as possible, I can’t simply measure his teammates’ performance—I’d just be concluding that Inge played with good or bad teammates. Instead, I need to develop a counterfactual, or a method of estimating how we could’ve reasonably expected Inge’s teammates to play in his absence. Fortunately, an excellent one already exists—a ZiPS projection. ZiPS, to my knowledge, does not have a ‘played with Brandon Inge variable,’ so it should be unbiased. Carleton instead used an AR(1) covariance matrix to try to adjust for player talent, but given that ZiPS explicitly incorporates past performance with a view to projecting, as accurately as possible, how a player will perform in the upcoming season, I believe it is a suitable tool.

I chose wOBA as the dependent variable for our study—while Carleton looked at multiple indicators (BB%, K%, etc), one, all-encompassing measure of players’ offensive performance seems best suited to answering the question, “Do players perform better with Brandon Inge on their team?”

In order to develop the requisite dataset for this analysis, I downloaded every player-season since 2006[1] from FanGraphs’ leaderboards and filtered the data to include only those players who amassed at least 200 plate appearances. This yielded 3130 player-seasons. Next, I created a binary variable called ‘IngeTeammate,’ with a value of ‘1’ if the player was on Inge’s team during the given season (and not Inge himself), and ‘0’ if he wasn’t. For the 2012 season, the only one in which Inge played for multiple teams, I counted Inge as having played for the Athletics, with whom he spent the majority of the season.

The next part was a bit tricky—bringing in the ZiPS projections. The latest years, the ones for which ZiPS has been featured on FG, were easy—data was readily available, wOBA already calculated, and records already associated with a player id. But wading deeper into the past unearthed some issues—in order to match records, I had to manually match player names (including the two Chris Carters, and, apparently, two Abraham Nunezes . . . Nunezii . . . who knows?) and hand-calculate ZiPS-projected wOBA for older player-seasons using the weights provided on the FanGraphs Guts page. One potential issue with some of the oldest data is the lack of projections for things like intentional walks and sacrifice flies.

However, forging through all of the record-matching and manual wOBA-calculating eventually yielded ZiPS wOBA projections matched to 3088 of the 3130 player-seasons. Of the 42 unmatched seasons, only one was an Inge teammate (2010 Brennan Boesch). 81 of the 3088 matched seasons were Inge teammates. So unless you think ZiPS would have pegged Boesch, a relatively unknown 25-year-old at the time, for a significantly better performance than the .322 wOBA he posted in 2010, the unmatched records probably didn’t have a huge effect.

What we’re left with is data that look like this:

In the above table, wOBAdiff refers to the amount by which the player outperformed his ZiPS wOBA projection. A negative number would indicate that a player underperformed his projection. So Jose Bautista outperformed his 2010 projection by .100—multiplying by 1000 tells us that this was 100 points of wOBA. It was good to be Joey Bats in 2010.

Results

If we look at the mean wOBA deviation (in terms of points of wOBA) Inge teammates and non-teammates experienced from their ZiPS projections, we see the following results:

In other words, if we weight by plate appearances, Inge teammates outperformed their ZiPS projections by an average of about 4.30 points of wOBA. All other players underperformed their projections by an average of about 3.09 points. Which might not seem like a lot, but if you were to apply that 7.4 wOBA difference to an average-hitting team over a 6000 PA team-season, that’s roughly 34 runs. So 3.4 wins. Which is, you know, quite a bit. The unweighted version is even more extreme, suggesting that players with lower numbers of PA have outperformed their projections even more when teamed with Inge.

If we simply run a regression including the independent variables IngeTeammate (binary) and age and the dependent variable wOBAdiff (unweighted), we can express the story another way:

wOBAdiff = 0.0127064 + (IngeTeammate* 0.0090544) + (age* -0.0005993)

I included age as a control because ZiPS projections, as you can see from the model above, tended to slightly overproject older players in comparison to younger players, and therefore I needed to consider the possibility that Inge simply benefitted from playing only with young players (he didn’t).

Note that in the model above, 0.001 corresponds to one point of wOBA (i.e. a hitter moving from .323 to .324 would have gained a point of wOBA). The r-squared of the model is absurdly low (0.006), but that’s to be expected—after all, I’m not trying to assert that Brandon Inge is responsible for all or even a significant part of the variation between MLB players’ expected and actual performance. More importantly, the variable ‘IngeTeammate’ is significant at a 98.4% threshold.

Considering the possible influence of aging is interesting, as the Inge difference is even more pronounced among younger players, or those whom he allegedly mentored while playing with the A’s. If we filter the data above to include only players 27 and younger, the table looks like this:

We’re starting to run into some serious sample size issues that make me uncomfortable drawing any particularly bold conclusions, but young players who play with Inge have done really, really well, collectively knocking the snot out of their ZiPS projections. There are problems with extrapolating this to a 6000 PA team-season, given that presumably an entire team won’t be composed of young players, but if one did so the result would be a ridiculous 78.6 runs of additional value.

The table below lists every 27-and-under player season for which the player was an Inge teammate:

It’s not as if one year is hugely skewing the results—pretty much every year, whichever young players happen to be playing with Brandon Inge outperform their projections. The graph below illustrates the mean wOBA differential younger Inge teammates exhibited each season. I would’ve imagined, prior to viewing these results, that Inge’s positive ‘effect’ might’ve been almost entirely a product of the 2012 Athletics, but this doesn’t seem to be the case—outside of the 2006 Tigers (when Omar Infante, Curtis Granderson, and Chris Shelton collectively underperformed their ZiPS projections by a modest average of ~5 points of wOBA), Inge’s younger teammates have outperformed ZiPS every single year in the sample.

Perhaps, one could say, Inge has simply benefitted from playing on teams run by intelligent front offices. After all, the Tigers, Athletics, and (more recently) the Pirates all have reputations as relatively savvy management teams. Maybe they’re just collectively able to out-forecast ZiPS.

When we look at ZiPS wOBA differentials by team, however, the Tigers (+1.36 points of wOBA), Athletics (+0.11) and Pirates (-0.31) all had weighted mean differentials less than the Inge gap. The average over all teams was -2.89, so while all three front offices ‘beat the market,’ so to speak, they still don’t explain the huge Inge effect. It looks as though there’s something here.

After observing the results for Inge, I was curious about whether other veteran players might also exhibit similar correlations—while we’d expect to find no correlation with ZiPS wOBA differential for most players, it might be the case that, as with Inge, patterns emerge. Specifically, I looked at two players with diametrically opposite reputations—A.J. Pierzynski and Jonny Gomes. Below, I replicate the initial summary table used for the Inge analysis and note the magnitude of the effect:

A.J. Pierzynski

The game’s most hated player didn’t fail to disappoint, as his teammates collectively underperformed their ZiPS projections by an additional of 4.9 points of wOBA when compared to non-teammates, an effect worth -22.6 runs to the team over the course of a full season. I should note that I assigned Pierzynski to the 2014 Red Sox (with whom he spent considerably more time) instead of the 2014 Cardinals—both teams underperformed their ZiPS projections, but the Red Sox did so by a larger margin.

Pierzynski’s unweighted results, while still negative, are less damning, and using a regressed model reflects this:

wOBAdiff = 0.0128794+ (AJTeammate* -0.0033689) + (age* -0.0005939)

The intercept and coefficient for age are, understandably, almost identical to those I observed in the Inge model. The significance level for AJTeammate, however, is only 64.1%, suggesting that we can’t really conclude much of anything with the same level of confidence as for Inge.

Still, twenty-plus runs is a non-negligible amount, and Pierzynski’s numbers have been negative across all four teams for whom he’s played (White Sox, Rangers, Red Sox, Cardinals). It may be that more historical data would reveal a broader trend, given that we’ve limited our sample size to only the latter half of Pierzynski’s career.

Jonny Gomes

The phenomenally-bearded Gomes, Inge’s running partner in the Brandon McCarthy quote that triggered this analysis, also appears to be a potential chemistry star, though his results are less extreme than Inge’s. His teammates outperformed non-teammates by 5.6 points of wOBA, worth an estimated 26 runs per season.

wOBAdiff = 0.0124387+ (GomesTeammate* 0.0055032) + (age* -0.0005873)

The effect, as with Pierzynski, is not statistically significant—the significance level is 87.4%.

Conclusions

We can’t make firm statements about causality from this analysis, but we can say pretty conclusively that being on the same team as Inge during the last nine years correlates positively with hitting better than ZiPS projects you to hit.

Maybe you don’t believe Inge should get credit for the extra 3.4 wins of value each year. We don’t have a ‘chemistry above replacement’ metric to account for the fact that some other player with a modicum of veteranosity might plausibly have a positive effect if analyzed the same way. And there’s no feasible way to develop one on the horizon—you can only start to do this sort of analysis retrospectively, and it requires a large number of plate appearances and player-seasons before we can conclude that any pattern has emerged. I’m not really arguing that Inge deserves all the credit for his teammates’ overperformance, only that we have reason to believe a nonzero effect may exist.

But let’s entertain, for a minute, the possibility that the 3.4 win-per-season gap we see *is* entirely attributable to Inge. That maybe all the minute, unnoticed interactions between players over the course of a season can add up to improved performance at the plate. The effect could even be greater than 3.4 wins—I didn’t examine pitching and fielding at all. After all, everything we know about human psychology suggests that happier workers are more productive, and I’ve yet to hear any compelling reason that ballplayers constitute an exception. We sometimes, in the analytics community, fall into the trap of assuming that because we can’t measure something accurately, it doesn’t deserve a meaningful place in our analysis. And yet our inability to measure a phenomenon is not proof of its nonexistence—just ten years ago, we lacked meaningful metrics for catcher framing, for instance.

Perhaps Inge contributed more hidden value over the last decade than anyone this side of Jose Molina, and Brandon McCarthy’s twenty-four wins were, if still hyperbole, grounded in a subtle truth. 3.4 wins currently has a market value north of $20M, making Inge a substantially underpaid man over the course of his career.

It’s a shame, on some level, that it’s only after he’s retired that we recognize the unheralded Inge for who he might secretly have been: Brandon Inge, Superstar.

[1] Before 2006, I struggled to find ZiPS projections in a readable format to develop the counterfactuals.

Data retrieved from FanGraphs and Baseball Think Factory.

If you were feeling charitable, you could say this post owes a lot to Jeff Sullivan’s recent set of articles examining pitch comps. If you weren’t feeling charitable, you could say this post is a shameless appropriation of his ideas. Either way, you should read those articles! They were very good, and very entertaining, and directly inspired this post. There were seven, in total: here, here, here, here, here, here, and here. I’ll wait.

Back? Good! In the comments of the third article, someone asked Jeff about finding the “most signature” pitch, or the pitch with the worst/fewest comps. Jeff said: “Wouldn’t be surprised if it was Dickey or the Chapman fastball. That math… I’m afraid of that math, but I might make an attempt.” Jeff has looked at unique pitches twice (Carlos Carrasco’s changeup and Odrisamer Despaigne’s changeup, the last two articles linked above), but I wanted to attack the question in a less ad-hoc fashion, looking at all pitches rather than singling some out.

Jeff wasn’t wrong, though – the math is not simple. His methodology doesn’t really work here for a couple reasons. First of all, I’m looking for uniqueness rather than similarity. I could just flip Jeff’s method around and look for high comp scores, like what he did for the Carrasco/Despaigne changeups, but I also want to consider all pitch types. Again, Jeff sort of did this in the Despaigne article, by comparing his changeup to a few different pitch types, but that is not really feasible for every pitch thrown.

What this means is that a new method is needed to directly calculate dissimilarity. We could find the maximum distances from the mean (basically Jeff’s method), which would work for a single pitch type: if all the pitches are clustered together, with similar velocities and breaks, calculating the distance from the mean to find the weirdest pitch makes sense. But consider this hypothetical set of pitches, graphed on two axes for simplicity:

Obviously, the pitch that corresponds to the red point is the sort of thing we’d like to identify as unique. It’s also exactly at the center of that dataset, and would show up as the least unique pitch, if distance from the mean was used to determine uniqueness. Luckily, there’s an algorithm that is designed to find outliers in a more rigorous way.

This is where the math gets scary. The algorithm is called Local Outlier Factor analysis, which identifies outliers in a dataset based on the density of data around that point as compared to its neighbors. In this context, the density around a point is a function of how similar the best comps are for each pitch. Each point gets a score, where anything near 1 indicates normal, and higher values indicate greater isolation. I’m not going to go into detail, but if anyone wants to learn more, feel free to ask in the comments, or just Google it. It’s fairly simple to run it on all pitches, with the relevant variables of velocity, horizontal break, and vertical break.

Any pitch thrown more than 100 times in 2014 was included, and righties and lefties were considered separately (since pitches that move the same way obviously are very different based on what side of the rubber they come from). But enough about methodology! Here are the top five most signature pitches, for righties and lefties, along with their LOF scores, followed by some gratuitous gifs.

RIGHTHANDERS

LEFTHANDERS

It’s nice when things work exactly like you expect them to. The top pitches on the two lists are incredible, and incredibly unique, and while it’s not a surprise to see them here, it does provide some reassurance that this measure is doing what it’s supposed to. Everyone knows about Dickey’s knuckleball, and if anything, it’s underrated by this measure. Since it moves so randomly, the knuckle’s season averages end up being slow and pretty much neutral horizontally and vertically. While that’s enough to make them show up as very odd under this measure, the individual pitches don’t often follow that straight trajectory, as seen in the above gif. The same can be said for Steven Wright’s knuckleball in third, but it’s nice that this measure still picks them out as unique pitches.

As for Chapman, there’s not that much to say about his fastball that hasn’t already been said. It feels wrong in some way to call his fastball strange, since it is disturbingly direct in practice, but there was truly no pitch like it in 2014. The velocity is the carrying factor behind the massive outlier score, almost a full 2 MPH greater than the next fastest pitch. Interestingly, Chapman’s pitch was the only one in either top five with notably high velocity.

Looking at the weirdest pitches in baseball, what can we conclude about them as a group? First, the pitchers throwing them are generally not bad. While you’d expect someone to be at least halfway decent to get in the position to throw 100 pitches of a single type, the owners of these pitches averaged about 1 WAR in 2014. With eight of these 10 throwing primarily in relief, and having only 710.2 innings collectively, that comes out to a very respectable 2.4 WAR/200.

The pitches themselves varied in usage, from Neshek’s change, thrown 13.4% of the time, to Britton’s sinker, thrown 89.3% of the time. They also varied in effectiveness, as measured by run values, from Neshek’s 3.6/100 to Marshall’s -1.63/100. Overall, the best pitch is probably Chapman’s fastball, followed by Britton’s sinker, given both the results on those pitches and how often they use them, but as a group, these pitches are pretty good. Maybe that isn’t totally surprising, but weird does not necessarily equal effective. Any pitcher could immediately have the weirdest pitch in baseball, if he threw 40 MPH meatballs, but less absurdly, mix and control matter just as much as the movement of the pitch.

Finally, all this stuff tracks fairly well with what Jeff identified previously. Obviously, he called Dickey and Chapman, but he also wrote this article about how Zach Britton’s sinker is pretty much comp-less, and we see that very pitch in fifth for lefthanders. Odrisamer Despaigne’s change was 12^th for righthanders. Interestingly, Carrasco’s change is 98^th on that same list, indicating this method doesn’t think he’s incredibly unique. Overall, this was mostly just a fun exercise, but maybe there’s more to this list, so if you want to poke around, it’s in a public Google Doc here. And like I said, if you have any questions about the methodology or anything like that, I’d be glad to answer them in the comments.

Jackie Bradley Jr. is not a terrible baseball player, and honestly he probably didn’t have a terrible 2014 season. Well at least, it wasn’t as bad as what people perceived. That, however, is due to his impeccable fielding and good baserunning. What follows will include none of that. It is rather a complete and utter breakdown of Bradley’s hitting performance, for 2014, and the trends he displayed. They, as you might have guessed, are not pretty.

First, it seems important to mention that Bradley’s numbers were great in the minors. Not just fielding but hitting as well. After A ball (Greenville), Bradley never had a wRC+ below 120 and he never had a BB% lower than 10%. Now BB% is not always predictive, as Chris Mitchell has displayed through his KATOH metric. KATOH, however, does show that BB% is predictive in AA and AAA, and Bradley’s BB% was good in AA and AAA.

Now on to 2014. This was suppose to be Bradley’s big break, it was supposed to be his year, he was going to replace Jacoby Ellsbury in center, and become the next great Red Sox center fielder. None of that happened; Bradley did play good defense but his offense was atrocious, finishing with a 47 wRC+.

So what happened? How did a player lauded for not just his defense but also his hitting ability, finish the year with a 47 wRC+? First, let’s acknowledge that hitting is extremely difficult, especially at the major-league level. There are also many components that go into hitting and all of them have an impact on a why a hitter hits a certain way. It’s also important to look at how pitchers work a hitter, and I think that’s where will start. Below is a graph, of the hard, breaking, and off-speed pitches Bradley faced in 2014.

From this, it’s pretty evident that pitchers predominantly attacked Bradley with fastballs. This was after all his first major league season, and pitchers will often test young hitters or rookies with fastballs. If the hitter starts to hit the fastball well, then typically a pitcher will make an adjustment. As you can see, no adjustments were made because no adjustments were needed.

Now that we know what pitchers were throwing at Bradley, lets look at what Bradley did with those pitches. The graph below will display the outcome of Bradley’s at-bats in 2014.

This is where my eyes started to hurt. Bradley, as you can see, got off to a good start, but everything fell off quickly after that. In fact, things fell apart so badly that Bradley didn’t get a single extra base hit in the last two months of the season. While I like this graph, in explaining Bradley’s struggles, I think the pie chart below will give you an even better example of just how bad Bradley was in 2014. The graph was provided by Baseball Savant.

There are many outcomes that can come from a pitch: a foul, a whiff, a called strike, a ball, a ball in play, and finally a hit. Bradley, got a hit considerably less often than any other outcome. This is not a recipe for success. Hold on, let me clarify that. The fact that Bradley’s hits were his most infrequent outcome was not the problem. Mike Trout’s most infrequent outcome after all was his hits. The problem was Bradley’s 4.6 hit%.

Another problem here is that Bradley was simply not putting the ball in play enough, and the balls in play, unfortunately, were not resulting in enough hits (.284 BABIP). This, however, is only one of the problems. To get a better understanding of why Bradley didn’t get enough hits, it seems imperative that we examine where Bradley was hitting the ball. For this, we’ll look at a spray chart provided by Brooks Baseball, to examine exactly where Bradley was hitting the ball, and if there are any consistent trends.

Here are the outcomes when Bradley put the ball in play. What is distinctively clear is that Bradley pulled the ball a lot, especially in the infield. He also doesn’t seem to have been hitting a lot of hard ground balls, which would explain his lack of hits in the infield. As you can see Bradley over a full season of baseball only mustered four hits in the infield and none the other way. The Red Sox have talked about working on Bradley’s swing, they’ve suggested that his swing is too uppercut-y and he needs to start swinging down on the baseball. From this chart it seems pretty evident why they want to do that. They probably want Bradley to be able to hit the ball the other way, not just in the air but also on the ground, as to maximize his ability to get hits.

While fixing a swing is important, it’s only one of the problems. There are more elements that go into hitting and someone doesn’t end up with a 47 wRC+ without some kind of approach problem. This is where we’ll take our final investigation, into Bradley’s plate approach and the tendencies he’s been displaying.

There are a few factors and components that can be attributed to a hitter’s approach. One of them is the hitter’s tendency to swing. The more one swings the less he is likely to be a patient hitter, and the less likely he is to have a good approach at the plate. Below is a graph of Bradley’s month-by-month swing percentage on hard, breaking, and off-speed pitches for 2014.

This as you might be able to tell is not good. Bradley’s tendency to swing got gradually worse as the year went on. This meaning that as the year went on Bradley either got further away from his approach or he simply got frustrated. Let’s not panic, however, just because a hitter has a high swing% doesn’t mean that he can’t be a successful hitter, especially if he makes contact on a lot of his swings. Vlad Guerrero was a great hitter and he swung at everything; he also hit everything. So let’s look at Bradley’s whiffs per swing (whiff/swing). Why? Well because if you’re swinging a lot, you don’t want to have a low whiff per swing rate because it means that most of the pitches you’re swinging at aren’t going to become hits. It also probably means you’re striking out a lot and that you’re chasing a lot of pitches.

As you might be able to tell, Bradley in 2014 swung and missed a lot. I think it’s also important to note that in the last two months of the year, Bradley’s plate appearances were significantly reduced. He only got 35 plate appearances in August and only 36 in September. So while it might seem that in the last month, Bradley started swinging and missing less, that was in a very small sample size.

Finally, lets look at Bradley’s overall plate approach tendencies. What follows is a chart provided by Brooks Baseball that examines a players overall plate approach. It examines, through the use of PITCH f/x data his passiveness and his aggressiveness at the plate. It does this through the use of detection theory, which analyses the decisions one makes in face of uncertainty. There are essentially two parameters to detection theory, C and d’. C, which is the one used for this graph, reflects the strategy of the response. Ok, that’s enough on the subject.

Just like Bradley’s swing tendencies, his overall plate approach was going in the wrong direction. Throughout the year, Bradley had consistently gotten more and more aggressive. He’s essentially lost what made him a successful hitter in the minors. These are the signs that probably made the Red Sox sign Rusney Castillo from Cuba to a seven-year deal. It also might be a reason why the Red Sox are in serious talks with the Braves about a potential trade involving Bradley.

That being said, while it is certain that Bradley’s tendencies and approach were all heading in the wrong direction, this doesn’t mean that he can’t turn things around. Players make adjustments all the time, and I’m not sure that these stats are necessarily predictive of future performance. Baseball after all is a game of adjustments, pitchers make adjustments on hitters, and then the hitters counter with their own adjustments. It doesn’t seem that Bradley will ever be a great hitter or even a good hitter, but what he can be is a league-average hitter. I’ve spent a lot of time discussing Bradley’s offense and not nearly enough on his defense. Bradley is a great defensive center fielder, maybe the best, and that has real value. If Bradley can simply become an average hitter, he should have a spot in the majors for many years to come.

All graphs can be found on Brooks Baseball and the circle graph on Baseball Savant. A lot of the stats can also be found on FanGraphs.