Despite an influx of information resulting from the advent of Baseball Info Solution’s batted-ball data and the world’s introduction to Statcast, surprisingly little remains known about pitchers’ control over the contact quality that they allow. Public consensus seems to settle on “some,” yet in a field so hungry for quantitative measures, our inability to come to a concrete conclusion is maddeningly unsatisfying. In the nearly 20 years since Voros McCracken first proposed the idea that pitchers have no control over the results of batted balls, a tug-of-war has ensued, between those that support Defensive Independent Pitching Statistics (DIPS) and those that staunchly argue that contact quality is a skill that can be measured using ERA. Although it seems as if the former may prevail, the latter seems resurgent in recent years, as some pitchers have consistently been able to outperform DIPS, hinting at the possibility of an under-appreciated skill.
It is also widely assumed that a hitter’s BABIP will randomly fluctuate during the season, and that changes in this measure often help to explain a prolonged slump or a hot streak at the plate. Hitters’ BABIPs can also vary drastically from year to year, making it difficult to gauge their true-talent levels. Research in this field has been done, however, and there have been numerous attempts to develop a predictive model for this statistic, one that projects how a player should have performed, or perhaps more succinctly, his expected BABIP, or xBABIP. Inspired by the progress, and albeit limited, success of these models, I embarked upon a similar project, instead focusing on the BABIP allowed by pitchers, rather than that produced by batters. What began as a rather cursory look at exit velocity evolved into a much deeper look, and with this expansion of scope, I achieved some success, though not as much as I had hoped.
My research began with a perusal of Statcast data, and I began to use scatter plots in R to visualize each statistic’s relationship to BABIP. Most of the plots looked something like this:
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In the majority of plots, it seemed as if there may have been some signal, but there was quite a bit of noise, making it difficult to detect anything of significance. This perhaps explains the lack of progress in projecting BABIP: after looking at these plots, it appears quite simply difficult to do. Despite these obvious challenges, I remained hopeful that I could perhaps develop something worthwhile with enough data. Therefore, I began aggregating information, collecting individual pitcher-seasons from FanGraphs, Baseball Savant, Brooks Baseball, and ESPN, then manipulating and storing the data in a workable format using SQL. Since Statcast data only became available to the public in 2015, my sample size is unfortunately a bit limited. I also wanted to incorporate the defense that pitchers had behind them along with park factors when creating my model, so I removed all pitchers that had changed teams mid-season from my records. This left me with a grand total of 641 pitcher-seasons (323 from 2015, 318 from 2016), and 188 pitchers showed up in both years. For the remainder of my study, I used the 641 pitcher-seasons to develop the model, but when checking its year-to-year stability and predictive value, I could only use the 188 common data points.
To begin, I fed 29 variables into R: K/9, BB/9, GB%, average exit velocity, average FB/LD exit velocity, average GB exit velocity, the pitcher’s team’s UZR, the pitcher’s home park’s park factor, his Pull/Cent/Oppo and Soft/Med/Hard percentages, and an indicator variable for every PITCHf/x pitch classification. (Looking back on this, I wish I included more data in my analysis to truly “throw the kitchen sink” at this problem, perhaps including pitch velocity, horizontal and vertical movement, and interaction terms to more accurately represent each individual’s repertoire. Alas, I plan on keeping this in mind and possibly revisiting the topic, especially as more Statcast data becomes available.) This resulted in an initial model with an adjusted R-squared of about 0.3; I then ran a backwards stepwise regression with a cutoff p-value of 0.01 to determine which variables were most statistically significant. Here is the R output:
For clarity, the formula: xBABIP = -0.157 + 0.005684 * BB/9 + 0.0009797 * GB% + 0.003142 * GB Exit Velocity – 0.0001483 * Team UZR + 0.005751 * LD%
I again obtain an adjusted R-squared of about 0.3, and I don’t find any of these results to be overly surprising, but to be fair, I had little idea of what to expect. Before examining the accuracy of my entire model, I checked each variable’s individual relationship to BABIP, along with the year-to-year stability of each. These can be found below in pairs:
I was most perplexed by the statistical significance of BB/9, and even after completing my research, I still find no entirely compelling explanation for its inclusion. Typically, BB/9 is considered a measure of control rather than command, but intuitively, these skills seem to be linked, and perhaps pitchers with better command and control are able to paint edges more effectively, thus avoiding the barrel and preventing strong contact. I was disappointed that its relationship to BABIP appeared so weak, but because of its relative year-to-year stability, I hoped that it would retain some predictive power.
Previous research has indicated that ground-ball hitters are able to sustain higher-than-average BABIPs, and thus, its inclusion in my model should not come as a shock. Again, it would have been nice to see a stronger correlation between GB% and BABIP, but there is obviously quite a bit of noise. However, it does seem that generating ground balls is a repeatable skill, which lends itself nicely to the long-term predictive nature of an xBABIP model.
Again, as previous research has suggested, the inclusion of GB exit velocity is to be expected. However, its correlation with BABIP is not as high as I would have hoped; I suspect this may be a result of the unfair nature of ground balls. In a vacuum, one would expect that low exit velocities are always superior, yet a fortunately-placed chopper may actually have better results than a well-struck ground ball hit right at a fielder, and thus, exit velocity’s signal may be dampened. There does appear to be some year-to-year correlation though, which offers some promise of an unappreciated skill.
Here, I’m surprised by the lack of correlation between UZR and BABIP; I collected this data to control for the quality of the defense behind a pitcher, assuming that this could be a pretty significant factor, and although it did remain in my model, the relationship appears to be quite weak. We should expect a very low year-to-year correlation between UZR, as pitchers that changed teams in the offseason were included in my study, and even if they remained on the same roster, teams’ defensive makeups can change drastically from one season to the next. Thus, the latter graph is rather useless, but I chose to include it for consistency.
Unsurprisingly, LD% has the strongest relationship to BABIP, checking in with an R-squared of about 0.15. I obviously wish that there were a stronger correlation between the two, yet despite the noise, when looking at the data, I think it is fairly evident that there is a signal. And although I have read that LD% fluctuates wildly from year to year, I was shocked by the latter graph. It seems as if this is entirely random, and that this portion of a pitcher’s batted-ball profile can be simply chalked up to luck. This revelation is a bit discouraging, as it suggests that my model may struggle with predictive power, since its most significant variable is almost entirely unpredictable.
I anticipated that more variables would be statistically significant, and I am surprised by their disappearance from the model. I assumed that Hard% would be highly correlated with BABIP, but it disappeared from my formula rather quickly. I also assumed that pitchers who generated a high true IFFB% would exhibit suppressed BABIPs, but nothing turned up in the data. And finally, I thought that K/9 may have been significant; it can be considered a rough estimate of a pitcher’s “stuff,” and I speculated that pitchers with high K/9 probably throw pitches with more movement than usual, perhaps making them harder to square up, but my model found nothing.
After considering each of the significant variables individually, I wanted to examine the overall accuracy of my entire model. To do so, I plotted pitchers’ xBABIPs vs. their actual BABIPs, along with the difference:
As mentioned earlier, after incorporating all of the statistically significant variables in my model, I achieve an R-squared of about 0.3, a result that I find satisfying. I obviously wish that my model could have done a better job explaining some of the variation in the data, and I suspect my model could be improved, although I have no idea by how much. There is an inherent amount of luck involved in BABIP, and it is entirely plausible that pitching and defense can in fact account for only 30% of the observed variance, and the rest can only be explained by chance. Despite the lower-than-desired R-squared, I do believe it still verifies the validity of my model, if only for determining which pitchers over- or under-performed their peripherals, saying nothing about why they did so or if they can be expected to do so again in the future. The lack of correlation in the difference plot indicates that pitchers have been unable to systematically over- or under-perform their xBABIP from year to year, and along with the residual plot, suggests that my model is relatively unbiased and doesn’t appear to miss any other variables that obviously contribute to BABIP.
After determining that my metric had some value in a retrospective sense, I set out to determine whether it had any predictive power. Because of the lack of year-to-year correlation for most of the statistically significant variables included in the model, I was quite pessimistic, although still hopeful. I first checked the year-to-year stability of both BABIP and xBABIP:
It seems that both measures are almost entirely random, although xBABIP is perhaps just a bit more stable from season to season. Despite this, comparing 2015 BABIP to 2016 xBABIP revealed that, as expected, my model holds little to no predictive power:
Again, although disappointing, this result was to be expected, as the most powerful variable in my model, LD%, fluctuates wildly. Despite this lack of predictive power, I stand by my model’s validity when considering past performance, and as more data accumulates, perhaps it can be adopted in a stronger predictive form.
Even after concluding that my metric has little predictive value, I thought it would be interesting to look at some of the biggest outliers. 2015’s biggest under- and over-achievers (with their 2016 seasons included as well), along with 2016’s luckiest and unluckiest pitchers can be found below:
Although the model holds no predictive power after quantitative analysis, anecdotally, it appears to do a decent job. Each of the 10 pitchers featured as an over- or under-achiever in 2015 saw the absolute value of their difference fall in 2016 (although the sign did change in some cases); in no way am I suggesting that the model is predictive, I just find this to be an odd quirk. I also find it perplexing that George Kontos appears an over-achiever in both years and can think of no explanation for this. Along with outperforming xBABIP, his ERA has also beaten FIP and xFIP in each of the last two seasons and five of the last six, suggesting a wonderful streak of luck, or perhaps hinting that the peripheral metrics are missing something.
Ultimately, although it would have been nice to draw stronger conclusions from my research, I am mostly satisfied with the results. When developing his own model for hitter BABIP, Alex Chamberlain achieved an R-squared of about 0.4 when examining the correlation between BABIP and xBABIP, the highest I have found. However, his model included speed score, a seemingly crucial variable that I was unable to account for when analyzing pitcher’s BABIPs. With this in mind, I find an R-squared of 0.3 for my model entirely reasonable, and despite its lack of predictive power, I consider it to be a worthy endeavor. As the sample size grows and more Statcast data is released, I plan to revisit my formula in coming offseasons, perhaps refining and improving it.
On December 8, 2010, the Boston Red Sox agreed to terms with Carl Crawford, inking the outfielder to a seven-year, 142-million-dollar deal, the largest ever signed by a position player that had never hit more than 20 home runs in a single season. Although the majority of the population felt that the Red Sox had overpaid for his services, most considered it only a slight reach, and when factoring in Boston’s position on the win curve, their decision to splurge on a premium player could be justified. Crawford was an established elite defensive outfielder coming off the best season of his career at the plate; an increase in power prior to the 2009 season had boosted Crawford to new heights just in time for his pay day, and in the final two years of his extension with the Rays, he posted WARs of 5.9 and 7.7, respectively. In the immediate aftermath of his signing with Boston, FanGraphs’ own Dave Cameron declared him to be a true-talent 5-win player, and at the time, it was not difficult to imagine a scenario in which the 29 year-old Crawford continued to perform at peak levels before gradually declining in the final years of the contract. If we assume that Crawford was in fact a 5-win player, then using the $/WAR figure accepted in the winter of 2010 (5 million dollars/win), 5% inflation, and a standard aging curve, the projection for Crawford’s contract would have looked something like this, with his 6-million-dollar signing bonus excluded from the analysis:
Two things are striking when looking at this table. First, on an unrelated note, league-wide inflation as a result of TV deals and increased revenue streams has far exceeded the expectations of 2010, actually surpassing 10% in order to reach the accepted value of 8-9 million dollars per win today in 2016. Although the methodology did prove to be incorrect, I do still believe the results obtained here to be worthy of inspection, as they offer insight into teams’ valuations of Crawford as a player available in the free-agent market. Second, the divergence between industry consensus and the arithmetic presented here is worth noting; most insiders felt that Boston had spent too much, while the data presented here suggests that the contract actually provided a bit of upside. This disparity could perhaps be explained by a skepticism of defensive metrics in 2010, along with doubts about Crawford’s ability to age well, as a large portion of his value on the bases and in the outfield was tied up in his legs. In order to account for this discrepancy, perhaps it is better to use a “worst-case scenario,” to subject Crawford’s performance to a more punitive aging curve over the life of the contract. Instead of docking Crawford 0.5 WAR for his age-31 through -35 seasons, instead, he will lose 0.75 wins each year. This steeper decline is forecast below:
It appears that this table is a more accurate representation of front offices’ opinions about Carl Crawford, as shown by the deficit in the bottom right corner. Using the more aggressive aging curve, the contract offered by the Red Sox does appear to be a slight overpay, and if they conformed to the opinion that the outfielder would decline more swiftly than other players of similar age, then they agreed to a contract in which there was no upside. However, if Crawford’s performance fell anywhere between the standard aging projection and the “worst-case scenario,” as it was likely to, it seemed that both sides would be satisfied with the outcome.
As we all know now, this “worst-case scenario” projected in 2010 was a far cry from reality. After suffering through a tumultuous year and a half in Boston and undergoing Tommy John surgery to repair a partially torn UCL, Crawford was unceremoniously dumped by the Red Sox and shipped to the Dodgers on August 25, 2012 as part of the infamous Nick Punto trade. It appears that Crawford has finally hit rock bottom, with Los Angeles designating him for assignment on Sunday. Crawford will almost certainly clear waivers, and assuming he asks to be released rather than assigned to the club’s AAA affiliate in Oklahoma City, the Dodgers will eat the remainder of his contract and essentially pay Crawford nearly 35 million dollars to disappear. Rather than projecting future performance, let’s instead take a look back at Crawford’s production since signing with Boston:
Although up-to-date $/WAR figures could have been used, I stuck with the estimates of 2010, in order to further emphasize how poorly this pact has been when compared to the organization’s expectations when they chose to sign Crawford. Elbow injury notwithstanding, it is difficult to imagine how this contract could have soured so quickly. Crawford has already cost his employers 85 million dollars more than his production would have warranted, and even if he latches on somewhere and plays out the remainder of the deal, it’s very likely that figure ends up more than 100 million dollars in the red. So, how did this happen? How did Carl Crawford, he of the 7.7 WAR in 2010, second in all of baseball, flop so badly, producing only 5.3 WAR since signing with the Red Sox?
Well, the most obvious answer is the boring one: Carl Crawford got old. Fast. From 2008-2010, Crawford’s final three seasons in Tampa Bay, he was actually the best defensive player in the MLB, posting a UZR/150 of 20.6 in left field, two runs better than his nearest competitor on the leaderboard. A bit of regression and decline were certainly expected, as it is incredibly difficult to sustain this level of performance, but nobody could have expected the utter evaporation of his defensive value upon arriving in Boston. Crawford posted a negative UZR during his time patrolling the Green Monster; some criticize the Red Sox for wasting his defensive abilities in what is considered to be the smallest left field in all of baseball, suggesting that he should have been moved to Fenway’s right field in order to better leverage his extraordinary range. However, even before his elbow injury, Crawford was known for having a weak throwing arm, and with a partially torn UCL, it would have been nearly impossible for him to play anywhere other than left.
Even so, his damaged elbow fails to explain the mysterious loss of range that sent him tumbling down the UZR leaderboards, and instead of providing value as an elite defensive player, Crawford instead resembled an average corner outfielder during his time in Boston. After being dealt to Los Angeles, Crawford’s defensive numbers did improve slightly, perhaps indicating that he never felt comfortable playing in front of the 37-foot wall, but by the time he arrived in Chavez Ravine, Crawford had already lost a step or two, placing a ceiling on his future defensive contributions.
This loss of speed was evident on the base paths as well, with Crawford never again imposing his will upon opposing batteries like he did during his time with the Rays. From the time of his promotion to Tampa Bay in 2002 until the end of 2010, Crawford had stolen 409 bases in 499 attempts, for a success rate of nearly 82% and the second-highest stolen base total in all of baseball during that timeframe. However, after signing with Boston, it seems as if Crawford became more timid as a runner, never attempting more than 30 steals in a single season. Since 2011, Crawford owns a 79% success rate, quite similar to his career average, yet he’s running far less frequently, stealing only 71 bases in 90 attempts. Whether due to a loss of speed or a lack of aggression, or perhaps a combination of the two, Crawford never regained his form as a base-stealer, resulting in the loss of a huge chunk of his base-running value.
Unlike his collapse in the outfield and on the base paths, Crawford’s decline at the plate cannot be explained by a loss of speed simply chalked up to age. This dilemma is a bit more perplexing. After posting the two best seasons of his career at the plate in Tampa Bay immediately prior to hitting free agency, Crawford’s production in the batter’s box cratered after signing with Boston in 2011, falling to levels only experienced by the outfielder during his first full season in the majors in 2003. Since joining the Red Sox, Crawford has sported a more aggressive approach leading to fewer walks and more strikeouts, has exhibited less power than he did during his time in Tampa Bay, and his problems against left-handed pitching have only been exacerbated. In a vacuum, none of these changes themselves would be damning, but in conjunction with one another, this trio has formed a nasty combination, only hastening Crawford’s demise.
Starting in 2006, as Crawford entered his offensive prime and started to become a force at the plate, his Zone%, the number of pitches he saw in the strike zone, began to decline as pitchers decided to carefully pitch around him rather than challenging him, falling from a high of 57% to only 43% in 2010. During his MVP-level campaigns in 2009 and 2010, Crawford adjusted to these changes appropriately, cutting his swing rate and accepting the free passes being handed to him by opposing pitchers, adopting what could be considered somewhat of a slugger’s profile. However, in 2011, perhaps feeling the weight of his new contract and worrying that hits rather than walks were needed to justify the nine-figure deal and appease Boston fans, Crawford gave these gains back, as his O-Swing% jumped by nearly three points.
Surprisingly, Crawford actually controlled the outside corner of the plate, but he expanded the zone in nearly every other direction. By chasing balls rather than selectively punishing mistakes, Crawford effectively got himself out more than ever, posting a career-high K% and his lowest BB% since 2003. Even when Crawford did make contact, the quality was often terrible, as his Soft% rose to a nearly unfathomable 26%, contributing to a 40-point drop in his BABIP and a subsequent, almost identical, fall in batting average. Although some of the walks have returned since his horrendous 2011, Crawford’s strikeouts remain elevated, seriously limiting his offensive production.
The lack of quality contact has also affected Crawford’s power output, because although his fly ball and line drive tendencies have been in line with his career norms, Crawford is doing far less damage. During his time in Tampa, Crawford had an ISO of .148, peaking at .188 in 2010. In Boston and Los Angeles however, this number has fallen to .136, and he’s never posted a single-season ISO higher than .150.
This loss of power is most obvious at the top of the strike zone and on the outside corner, where Crawford is now capable of doing little to no damage. Even in his wheelhouse, down and in, Crawford’s strength has eroded, leaving him as a shell of his 2010 American League MVP candidate self at the plate.
Finally, and perhaps most troubling, since leaving Tampa Bay, it seems like Crawford has forgotten how to hit left-handed pitching. Even in his prime, the lefty struggled against southpaws, boasting only a .308 wOBA, but since signing with Boston, his production against same-handed pitching has collapsed, with his wOBA falling nearly 30 points, leaving with him with a wRC+ of 73 against lefties. And yes, we now have an answer, his platoon split absolutely matters. The final straw came in 2013, when he posted an ISO 0f .084 and a wRC+ of 56 in 115 plate appearances against left-handers; since then, Crawford has become a platoon outfielder, almost never allowed to face lefties and failing miserably when he does, as evidenced by his -64 wRC+ against them this year (granted, in only 12 plate appearances).
So, there you have it. Carl Crawford, the electric baserunner, phenomenal outfielder, and prodigious hitter of less than six years ago is soon to be unemployed, assuming he clears waivers and is released. Does he have any baseball left in him, or is this 142-million-dollar man done? In any other year, he might have been, but given the number of contenders that will need an outfielder and the limited supply, it’s very possible that a team will give him a chance. However, it is unclear if Crawford even wants to continue playing, given that the team acquiring him will almost certainly place him in a platoon role, while he has stated that he doesn’t believe he is a platoon player. If he does agree to play in a limited role, where could he land? An obvious answer is Cleveland, yet during his time in Boston, Crawford didn’t get along well with current Indians’ skipper Terry Francona. Somewhat comically, Boston is another obvious fit, as he could be a nice platoon partner for Chris Young, but we all know how his first stint with the Red Sox went. Other teams that could be interested in the outfielder’s services include the Orioles, Nationals, Mariners, and White Sox, although they may look to make a bigger splash before settling upon Crawford. Whether Crawford returns to the big leagues or not, his time as an impact player almost certainly ended years ago, and that’s a shame.
No matter their team allegiance, fans of the game of baseball have to be disappointed by the outcome of Crawford’s career, as his prime was gone far too soon. One of few players that could truly dominate the game in every phase, through a combination of injury, age, and perhaps a lack of mental toughness, Carl Crawford’s star was extinguished almost immediately after signing with Boston.
Since dominating the Dominican Summer League as a 17-year-old shortstop, Xander Bogaerts has been considered one of the elite young talents in the game, heralded for his on-base ability, and specifically his power. After being promoted to start the 2011 season in Greenville, the Aruban native continued to rake, proving his skills at every rung of the organizational ladder. At each full-season minor league level, Bogaerts never ran a wOBA below .366, and his lowest ISO was a very respectable .169. It seemed as if he were destined to inherit the throne left vacant in Boston since Nomar Garciaparra departed in 2004; fans drooled over his future as the Red Sox’s franchise cornerstone, anchoring the heart of the Boston lineup while playing a premium defensive position.
On August 19, 2013, with Stephen Drew mired in a slump and the Sox struggling, Bogaerts was promoted from Pawtucket and joined the team in San Francisco, thus beginning his tenure in Boston. Bogaerts appeared in 18 games down the stretch, hitting only 1 home run and watching his K% balloon to 26%. However, his struggles were mostly ignored as the team wrapped up the division, and all concerns were quieted by the maturity he demonstrated after being inserted into the Sox’ starting lineup on baseball’s biggest stage, as evidenced by his .386 wOBA during the postseason run culminating with a title. At the tender age of 20, Xander Bogaerts was a World Series champion, appearing poised for a Rookie of the Year campaign in 2014.
Unfortunately, Bogaerts failed to meet expectations in 2014, posting his worst season as a professional by far. After a hot start, he collapsed in the second half. He continued to strike out in nearly 25% of his plate appearances, his 6.6 BB% was a career worst at the time, and he finished with a disappointing 82 wRC+. Bogaerts’s struggles were driven by his inability to hit right-handed pitching, as he posted a measly .105 ISO against righties coupled with a 71 wRC+. The following image should help to explain the decline:
After getting ahead in the count, righties attacked Bogaerts down and away, leading him to chase breaking balls and expand the strike zone. In fact, on a per-pitch basis, the rookie shortstop was the fifth-worst hitter in baseball against the slider. With his confidence shattered after a poor performance at the plate along with Boston’s decision to sign Stephen Drew midseason, outsiders questioned whether Bogaerts could recover from his prolonged slump, while some predicted that he would be the next big prospect to bust.
After admitting that 2014 was probably the “toughest season [he] ever had,” Bogaerts entered 2015 once again as Boston’s starting shortstop, hoping to recapture the stroke that propelled him to the big leagues so rapidly. Although he collected a Silver Slugger and seemingly accomplished his goal, Bogaerts exhibited a vastly different approach, one in stark contrast with his minor-league track record. While he retained his high on-base ability, rather than selectively punishing mistakes, Bogaerts became a more restless slap hitter, sacrificing power in exchange for contact. He boosted his Swing% by almost four points, offering at nearly half of the pitches he seen, but his ISO fell to a career worst .101. This change can be attributed to his increased willingness to use the entire field; Bogaerts boosted his Oppo% by 13 points but showed nearly no power when going to right field as evidenced by a Hard% of only 14.5. He also become an above average hitter on a per-pitch basis when challenged with sliders, improving upon perhaps what was his biggest weakness.
This more aggressive approach resulted in a significant drop in Bogaerts’s K%, coupled with a smaller decline in his BB%. He finished the year with a much-improved 109 wRC+, certainly playable when coupled with league-average defense at shortstop, yet he left much to be desired in the minds of talent evaluators around baseball. Rather than demonstrating the power he had exhibited throughout his minor-league career, Bogaerts instead resembled a weak middle infielder. Once destined for stardom, Bogaerts had been relegated to an average shortstop, definitely a valuable piece on a contending team, but not the player many had projected him to be.
Now over 40 games into the regular season, despite capturing success in 2015, rather than settling, it appears that Bogaerts has once again evolved. A quick glance at his numbers may suggest his improved offensive performance can be chalked up to luck, as evidenced by his high BABIP, but a deeper look at his underlying peripherals indicates that Bogaerts may have once again altered his approach at the plate. First, he is proving that the decrease in K% is legitimate; Bogaerts is once again running a strikeout rate below 16%, nearly five points better than league average. This year, it also appears that he has developed better command of the strike zone, as the has cut down his swing rate while boosting his BB%, both to nearly league-average levels. More important than these, however, may be the reemergence of Bogaerts’s power. Through 40 games, Bogaerts is running an ISO of .157, a level that he never once reached during his miserable 2015 season.
Unlike other unsustainable power surges, it seems as if Bogaerts’s may be viable. His HR/FB has risen by nearly six percentage points, yet it still falls below the league average. Statcast also seems to confirm our findings, as Bogaerts’s average exit velocity has risen by three miles per hour since the end of last season, although this data is still relatively new and cannot be considered a perfectly reliable indicator of future performance. The majority of Bogaerts’s damage this season has come to the pull side, as his wRC+ has jumped by almost 100 points, and it seems as if he is making a concentrated effort to elevate more of the balls that he hits to left, as his FB% to the pull side has increased by nearly four points. His bloated wRC+ will almost certainly fall, as a 44.4% HR/FB ratio to left field is absolutely ridiculous, but Bogaerts’s new offensive approach suits him well.
As seen in the table, Bogaerts is also demonstrating more power going the other way, and although his solid contact has still not resulted in any home runs to right field, the singles of 2015 have transformed into doubles this season. Although he still sees the same number of percentage of pitches in the strike zone, it seems as if pitchers are approaching Bogaerts with more trepidation because of his newfound power, as he is seeing fewer fastballs this season than at any point during his major-league career.
The projections are a bit skeptical, as they forecast a fall in both BABIP and ISO, but if Bogaerts is able to maintain his current level of production, or really anything near it, 2016 will be his most successful season in the major leagues, by far. He has undergone a major transformation at the plate, yet he has essentially reverted to the hitter he was as a prospect shooting through the minor leagues. The strikeout-prone 2014 Xander Bogaerts gave way to the slap-hitter 2015 version, which then evolved into the more selective and powerful current manifestation of the young shortstop. Perhaps most intimidating, however, is the fact that Bogaerts remains only 23 years old, and his evolution may not be complete. Overshadowed prior to this season by the likes of Carlos Correa, Francisco Lindor, and Addison Russell, Xander Bogaerts appears set on mashing his way back into the conversation as the best young shortstop in baseball.