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Park Factors to (Maybe) Monitor

Every baseball stadium is different.  This is an obvious fact, but its obviousness can obscure its importance.  Every baseball stadium is different, so baseball is different in every stadium.  Some of these differences are easy to discern such as HRs in Denver and Cincinnati.  Others though are more easily masked — did you know that the White Sox’ U.S. Cellular Field raises walks by 7%?  Each game is a combination of outcomes affected by each team’s talent and, to a lesser extent, these park factors.  FanGraphs is nice enough to publish its park factors here.

With the league-wide increase in exit velocity and home runs, I was interested to know if any park factors may be changing as well.  With roughly half of the 2016 season in the books, I thought now was as good a time as any to take a look.  Rather than go through the laborious calculations necessary to find park factors like those at FanGraphs, I came up with a quick and not at all exact way to look at just this season.  Essentially, I found each team’s home and away rates of 1B, 2B, 3B, HR, SO and BB per plate appearance.  I then compared each to league average on the same scale as wRC+ (100 is average).  I then calculated a quick park factor on the same scale for each of the above stats as follows (1B factor shown below):

((Team Home 1B Rate – (Team Away 1B Rate – 100)) + 100) / 2 = 1B Park Factor

For example, the Marlins have hit 4% more singles than average at home (104 1B+), and 27% more singles than average on the road (127 1B+), so the Marlins Park 1B park factor would be 88 (depresses singles by 12%).

I am fully aware of the many problems with the methodology (ignores half of the data, small sample, not enough regression included, team road schedules aren’t guaranteed to have average park factors, etc.), but like I said, I wanted something quick, and I am only focused on the extremes anyway.  This should at least show us which parks to consider monitoring or examining further.

2015 FanGraphs vs. 2016 Observed Park Factors
2015 FanGraphs 2016 Observed
Team 1B 2B 3B HR SO BB Team 1B 2B 3B HR SO BB
Angels 100 96 91 93 102 97 Angels 98 87 80 105 101 103
Astros 99 100 108 105 103 101 Astros 93 103 138 101 104 102
Athletics 99 100 105 93 97 101 Athletics 97 97 145 90 98 94
Blue Jays 97 108 105 106 102 99 Blue Jays 107 116 74 90 103 102
Braves 100 99 93 96 103 101 Braves 106 85 125 94 99 102
Brewers 99 100 102 112 101 102 Brewers 95 106 131 113 98 104
Cardinals 100 99 95 94 98 99 Cardinals 101 104 42 88 96 98
Cubs 99 99 102 102 101 102 Cubs 96 84 105 100 98 111
Diamondbacks 99 99 102 100 98 111 Diamondbacks 99 105 120 102 100 99
Dodgers 98 98 78 102 100 96 Dodgers 98 91 69 116 98 101
Giants 99 97 115 84 100 100 Giants 103 97 163 83 100 109
Indians 100 103 81 101 101 99 Indians 109 121 21 105 93 120
Mariners 98 87 85 98 102 97 Mariners 96 96 92 108 97 108
Marlins 101 100 117 88 98 101 Marlins 88 109 42 102 99 102
Mets 96 95 87 101 101 100 Mets 98 86 80 108 98 111
Nationals 104 102 84 97 97 98 Nationals 104 90 70 98 93 109
Orioles 101 99 86 108 99 100 Orioles 103 93 118 105 89 109
Padres 98 95 97 98 102 101 Padres 99 98 100 94 96 102
Phillies 98 99 92 107 103 102 Phillies 93 87 128 94 104 104
Pirates 101 99 89 90 96 96 Pirates 106 88 157 101 92 110
Rangers 103 101 110 105 98 102 Rangers 106 105 153 86 95 113
Rays 99 95 98 96 102 100 Rays 99 99 98 84 105 95
Red Sox 103 114 105 96 100 100 Red Sox 102 123 90 87 94 109
Reds 99 98 92 113 103 101 Reds 97 94 100 121 102 99
Rockies 110 108 128 113 95 102 Rockies 103 134 170 109 86 114
Royals 101 103 114 93 96 99 Royals 104 113 141 100 90 106
Tigers 101 98 126 98 95 99 Tigers 105 97 135 105 96 105
Twins 102 101 106 98 98 99 Twins 105 101 171 86 87 98
White Sox 99 97 91 108 103 107 White Sox 100 99 86 108 97 107
Yankees 100 97 84 110 101 101 Yankees 94 102 86 120 97 116
Data pulled at All-Star Break

I know that is a lot to digest, and I apologize it is not sortable due to my lack of coding skill — but there are some interesting differences buried in that table.

1B Park Factor

Two parks stick out at the extreme ends for singles.  The aforementioned Marlins Park went from slightly single-friendly to the worst park for singles.  I don’t have a good explanation for this, though the fences were moved in prior to this season which we would expect to set off a ripple affect with the park factors.  The Blue Jays’ Rogers Centre went the opposite direction of the Marlins, showing a move from slightly below-average for singles to the second-best park for singles.  The Jays did change to a dirt infield from turf for 2016, but I would expect that to decrease 1Bs rather than increase them.  Maybe dirt slows infielders down giving them less range?  The Jays have recorded more infield and bunt hits at home than on the road as well, which would increase singles.

2B Park Factor

Coors Field has seen a marked increase in doubles (and triples) in 2016 with a small decrease in HRs, which is very interesting considering they raised several areas of the outfield walls.  The Cubs, Braves, Nationals, Phillies and Pirates have all seen at least a 10-point decrease in 2Bs.  Of that group, the Braves, Phillies and Pirates seem to have traded those doubles for triples which I wouldn’t necessarily expect to hold up as a change in the park factor given the limited samples.  The Phillies also made a change to a longer-cut grass, so a decrease in 1Bs and 2Bs makes some sense.  I am not sure what is going on in Chicago (wind patterns?) and Washington as the decrease in doubles does not seem to be offset by an increase in other similar batted balls.

3B Park Factor

As expected with the extremely limited number of triples, there is a ton of variation across the half-season sample.  The two most likely to represent a true change to the park factors in my mind are the decrease in triples in Marlins Park (moved fences in) and the increase in triples at Coors Field (raised fences), though both likely won’t hold up to this magnitude.

HR Park Factor

There have been large and unexpected decreases in home runs in Toronto and Texas, while the Marlins and Dodgers have seen upticks in homers at home.  Probably nothing but small-sample noise here.  It will be worth checking more rigorously to see if these hold up, particularly at Marlins Park given the change to the fences.

Strikeout and Walk Park Factors

Given the way I have calculated each component park factor, I expected all of them to need an adjustment for home-field advantage.  Interestingly, that was not the case for 1Bs, 2Bs and HRs as the average observed park factor for each was 100 across the league.  I wrote off the 108 average observed 3B factor as small-sample noise, but I believe I picked up some measure of home-field advantage in strikeouts and walks.  On average across the league, home parks decreased strikeouts by 3% and increased walks by 5%.  These have been regressed and the samples for each are among the largest of the component park factors (more PAs end in a K than any specific batted-ball outcome, and there are more BBs than anything except 1Bs), so it feels like this reflects something.

The extreme parks for changes in strikeouts are the Twins’ Target Field and Diamondbacks’ Chase Field.  Adjusting for the home-field difference (the unadjusted numbers are shown in the table above), the Twins’ park seems to be decreasing strikeouts by about 8% more than usual, while the Diamondbacks’ stadium is increasing Ks by 8% more than FG expects.  The Twins did make a change to their CF seating that could be affecting the hitters’ ability to pick up pitches (and thus strike out less), but if that is the case an increase in walks would also be expected — and that is not the case, as the Twins have actually walked less than expected when including the home-field adjustment.

For changes in BBs (after adjusting for home field), the parks in Oakland and Cleveland stick out.  The Coliseum has allowed 12% less walks than expected, while the Indians’ Progressive Field has inflated walks by 16%.  These may be worth exploring as both parks have also affected strikeouts, with the A’s park increasing strikeouts and the Indians’ park decreasing Ks.  It is possible hitters are not picking up the ball in Oakland while they are seeing it well in Cleveland.


So there you have it.  Noisy, likely inaccurate 2016 park factors.  It will be very interesting to see if any of the observed changes detailed above turn out to reflect a true change in the park factors.  My best guess is Colorado, Miami and Toronto will need some type of adjustment from the 2015 park factors given the fairly significant changes to each park debuting in 2016.  It would be fascinating to hear thoughts from the players on the extreme differences found above as well.  The fact that each park is so different is part of baseball’s appeal to me.  Every game really is totally unique, all the way down to the field itself.

Inverse Clayton Kershaw

Clayton Kershaw is great. Really really great. Maybe hurt — but definitely great. But I’m not interested in examining Clayton Kershaw; I’m interested in examining Inverse Clayton Kershaw. I want to find the pitchers that have been most unlike Kershaw during the last calendar year. Kershaw has been the best — I want to find the worst.

Clayton Kershaw vs. League Average – Past Calendar Year
Clayton Kershaw 1.50 1.56 2.08 33.8% 2.9% 0.36 16.1%
League Average 4.13  4.11 4.07 20.8% 8.5% 1.11 10.0%
Difference -2.63 -2.55 -1.99 13.0% -5.6% -0.75 6.1%
Inverse Kershaw 6.76 6.66 6.06 7.8% 14.1% 1.86 3.9%
Data pulled 6/29/16

So wow. Did I mention Kershaw is great? Anyway, time to find Inverse Kershaw. First, I want to point out that the players below are still incredible at baseball. They are some of the most elite in the world, way better than all of us. Caveat aside, I’ll now examine the pitchers over the past calendar year who are most unlike Kershaw in each of the stats above — i.e. if Kershaw’s ERA is 2.63 below league average, whose is 2.63 above league average. When in doubt, I’ll defer to the guy with the most IP. At the end, I will name the Inverse Kershaw!


So, whose ERA has been a whopping 2.63 runs above league average? Coming in with an ERA of 6.75 we have Carlos Contreras. Contreras pitched 18.2 innings within the last year for the Reds out of the bullpen. You probably expected some 2016 Reds relievers to qualify, but Contreras posted these numbers exclusively in 2015 and then did not make the 2016 Reds bullpen. Yikes.


Noe Ramirez has worked to a 6.65 FIP in 24 IP for the Red Sox over the last year. Prior to 2016, then lead prospect analyst Dan Farnsworth said of Ramirez, “his stuff likely isn’t good enough to be more than bullpen filler.” Maybe not even that good.


Well I’ll be damned. With 18.2 IP with an xFIP of 6.05 out of the Reds bullpen we have…Carlos Contreras.


With a K% of exactly 7.8%, we find the final 13 IP of Dodger right-hander Carlos Frias‘ 2015 season (he hasn’t pitched yet in 2016). As a Cistulli darling, I imagine this is just a speed bump in Frias’ journey to becoming a Cy Young winner.


In 39.2 IP, Elvis Araujo of the Phillies has walked 14.0% of batters faced. In related news, Araujo was optioned to Triple-A Lehigh Valley on June 26.


Matching our criteria exactly with 1.86 HR/9 allowed in 67.2 IP is Toronto starter Drew Hutchison. This figure doesn’t factor in his excellent work in Triple-A (.77 HR/9 allowed), and according to the Toronto Sun, Hutchison figures to be called up soon. Hopefully he can get the gopheritis under control and contribute for the Jays down the stretch.


I made a judgement call here. The pitcher with the most IP within 0.2% of the required 3.9% SwStr% is Jon Moscot and his 4.1% SwStr%. Moscot has posted that rate across 21.1 IP in five starts for the…gulp…Reds this year. Poor Reds fans.

The Inverse Kershaw

It is all fine and good (bad) to post inverse Kershaw numbers in one category, but I wanted to know the single pitcher that was most unlike Clayton Kershaw. More accurately, I wanted to find the pitcher whose performance has been as far below average as Kershaw’s has been above average. To do this, I began with a sample of all pitchers appearing in MLB over the last calendar year. I then calculated the number of standard deviations each of their component statistics were from the Inverse Kershaw numbers in the table above. The pitcher with the lowest sum of standard deviations will be named the Inverse Kershaw. This is exactly the methodology used by Jeff Sullivan for his pitch comps.

And the winner (loser?) is….Matt Harrison, formerly of the Texas Rangers, currently of the Phillies Disabled List. You may remember Harrison as the salary dump portion of the Cole Hamels to the Rangers trade. You will hopefully now remember him as the past calendar year’s Inverse Kershaw. The final numbers are below.

Clayton Kershaw vs. Matt Harrison – Past Calendar Year
Clayton Kershaw 1.50 1.56 2.08 33.8% 2.9% 0.36 16.1%
League Average 4.13  4.11 4.07 20.8% 8.5% 1.11 10.0%
Inverse Kershaw 6.76 6.66 6.06 7.8% 14.1% 1.86 3.9%
Matt Harrison 6.75 6.07 5.66 7.3% 8.7% 1.69 3.3%
Data pulled 6/29/16

So there you have it, the pitcher coming closest to being as far below average as Clayton Kershaw has been above average over the last year is Matt Harrison — the Inverse Kershaw. Just for fun, here is the same table as above, subbing out the 2016 Reds Bullpen for Matt Harrison.

Clayton Kershaw vs. Matt Harrison – Past Calendar Year
Clayton Kershaw 1.50 1.56 2.08 33.8% 2.9% 0.36 16.1%
League Average 4.13  4.11 4.07 20.8% 8.5% 1.11 10.0%
Inverse Kershaw 6.76 6.66 6.06 7.8% 14.1% 1.86 3.9%
2016 Reds Bullpen 6.08 6.02 5.16 18.9% 11.9% 1.95 9.7%
Data pulled 6/29/16

Poor Reds fans.

Over- and Under-achieving FIP

I have always been fascinated by pitchers that consistently post ERAs that differ significantly from their FIPs.  As a Braves fan, this interest is particularly relevant in the valuation of ace/not ace Julio Teheran.  Unfortunately for me — but very fortunately for readers — Eno Sarris tackled the specific case of Teheran and the more general case of FIP-beaters with high pop-up rates here before I could finish this post.  Regardless, the research is done, and I believe it is still relevant.

While Eno focused on a specific subset of FIP-beaters in his discussion of Teheran, I wanted to examine pitchers with extreme ERA/FIP gaps more broadly.  I included not only pitchers who overachieved based on FIP, but also those who underachieved.  I began with a sample of all pitchers since 1960 who reached 500 IP through age 25.  I then calculated the difference between ERA- and FIP- for each pitcher (FIP overachievers would have a negative number, underachievers positive).  I selected these metrics 1) because they were readily available here at FanGraphs, and 2) because I was interested in the gap relative to league average — hopefully stripping out any differences in era (should any even exist).  

I chose this age cutoff so that I had a sample of three “in-prime” seasons afterwards (age 26-28) to compare to the initial numbers below.  After I found Z-Scores for all of the u25 pitchers, I set the threshold for over/underachiever at +/- 1 standard deviation from the mean, which turned about to be an ERA- / FIP- difference of right around eight.  It is certainly arbitrary, but I felt like this adequately separated the sample so I could examine the ends of the population.

Extreme FIP Over/Underachievers
Group ERA- minus FIP- n
ALL u25 -.02 297
Overachievers (Z<1) -11.91 48
Underachievers (Z>1) 11.35 47
Since 1960, min. 500 IP through age 25.  Average ERA- minus FIP- weighted for IP.

As you can see, the spread in ERA- between over/underachievers is pretty large.  Overachievers posted ERAs 12% lower (relative to league average) than expected based on FIP, while underachievers posted ERAs over 11% higher (relative to league average) than expected based on FIP.  The group as a whole posted an ERA- nearly identical to its FIP-, which is more in line with DIPS theory expectations.

The big question remains: how “sticky” is the gap between ERA- and FIP-?  To determine this, I compared the ERA- / FIP- gap for these same samples from age 26-28.

Extreme FIP Over/Underachievers Age Comparison
Group u25 E-F- o25 E-F- Raw Diff Diff Adj. for Sample Avg. % Retained
ALL -.02 .42 -.44
Overachievers (Z<1) -11.91 -3.41 -8.50 -8.06 32.3%
Underachievers (Z>1) 11.35 4.64 6.71 7.15 37.0%
Since 1960, min. 500 IP through age 25.  Average ERA- minus FIP- weighted for IP.

From age 26-28, the sample as a whole posted an ERA- above its FIP-.  Even adjusting for that change, the over/underachievers both regressed heavily towards the mean, retaining 32.3% and 37.0% of their difference in ERA- and FIP- respectively.  While regression is powerful, both samples did continue to post differences in ERA- and FIP-.  The overachievers continued to post lower ERAs than FIPs, while the underachievers kept on allowing more runs than FIP suggested they deserved.  Interestingly, the percentage of the gap retained is similar for over and underachievers, though it is slightly smaller for FIP beaters.

The methodology isn’t perfect, but I found the results very compelling.  It does seem like consistently beating FIP is partially skill (which jibes with Eno’s results), and consistently allowing ERAs above FIPs is more than just bad luck.  As usual, this analysis leads to more questions than answers.  How many innings are needed before one can be considered a DIPS outlier?  Do FIP underachievers actually regress less than FIP beaters?  How does age-related decline affect the gap in ERA- and FIP-?  As the sample for a DIPS outlier grows, does he retain more of the difference going forward?  Etc.  I may try to dive into one or more of those questions later.  For now, hopefully this analysis is helpful as you consider how likely a pitcher on your team is to continue over/underperforming his FIP.

Is Freddie Freeman Broken?

The Braves’ offense is terrible.  Absolutely putrid.  Jeff Sullivan already covered that here.  If the offense has been historically bad, odds are the best player in that lineup is performing below expectations as well.  Sure enough, that is the case with the 2016 Atlanta Braves and Freddie Freeman.  Look at any stat you want and they all tell the same story.  Sabermetrically inclined?  His wRC+ (as of when this was written) is down 65 points from his career average, with his ISO down a whopping 107 points.  Prefer the more traditional numbers? He’s batting .203 with three extra-base hits in 82 PAs.  Regardless of how you want to measure them, the results have been bad, but the real question is, what is driving the poor results?

The tempting answer is bad luck.  His BABIP is down, he’s faced several of the game’s toughest lefties and it is only April after all.  Another easy explanation is lack of protection around him. Why would pitchers throw Freeman anything he can hit when he is the only one in the lineup that can punish them?  Oh wait, his Zone% is UP and he has seen more fastballs than last year? Hmm.

This leads to questions about his health — is Freddie Freeman still battling the wrist issue that plagued him last season?  He said it was fine headed into spring training but then felt some discomfort in mid-March before calling it a false-alarm.  Maybe the wrist is still a problem for Freeman — either directly with discomfort or indirectly through altered mechanics stemming from the injury — but regardless, it is my opinion Freddie Freeman is broken.

Even this early in the season, there are data indicators to watch.  Swing%, Contact% and (in my opinion) exit velocity are all useful to an extent over small samples.

O-Swing% Z-Swing% Swing% O-Contact% Z-Contact% Contact% Avg. Exit Velocity
Freeman 2015 29.1% 75.6% 49.5% 66.6% 82.4% 77.2% 91.2
Freeman 2016 23.7% 73.9% 47.2% 63.4% 77.0% 73.4% 88.1

Swing% is down across the board, with a marked drop in O-Swing% which is actually a good indicator most times.  The real concerns lie in the decreases in Z-Contact%, Contact% and Avg. Exit Velocity.  A large drop in Contact% accompanied by a large drop in exit velocity and ISO is a recipe for disaster — typically players trade Contact% for an increase in power.  A decrease in contact and exit velocity points to a bat-speed issue, and Freeman himself believes that to be the case telling the reporters earlier this season that “My bat speed is just not there.  I don’t know if I’m tensing my shoulders and I’ve got to get loose; that’s what I was just working on.” I agree with his assessment, the bat speed has not been there.  Freeman has made in-play contact on just eight of his 39 swings against pitches with a perceived velocity of at least 93 MPH according to Baseball Savant.  This is down compared to 66 of 194 in 2015 (the only other year with data, when he was still battling the wrist issue).  His production on fastballs in general is down as well, with a negative wFB/C mark for the first time since his debut in 2010.

The reduced bat speed also shows up in his spray chart.  Freeman’s percentage of balls hit up the middle or pulled have both decreased, leading to an increase in his Opp% of over 10%.  This increase in opposite-field hitting alone is not crippling, but combined with an unbelievable decrease in production on these balls in play — 2015 wRC+ of 180 to the opposite field, wRC+ of 31 so far in 2016 — it creates a major problem.

Clearly, the Braves and Freeman are focused on adjustments at this point, but if the struggles continue much longer it will be hard to silence questions about the health of his wrist.  Credit Freeman for working on a solution, but if he and hitting coach Kevin Seitzer cannot figure out a way to get him back to normal (or if he misses extended time due to reemergence of the wrist issue), then the Braves’ offense from the first 20 games may not improve much after all.  That is a possibility that could lead to a terrible, long and historic season in Atlanta.

Hector Olivera as a Player

I wrote the article below before the news that Hector Olivera had been arrested on suspicion of domestic assault.  Obviously, if true, those allegations are horrible, and take precedent over any analysis as a player. 

As you may know, the Atlanta Braves have entered a full-scale rebuild.  Nearly every player of note from the 2014 Braves has been shipped out of town: Justin Upton, Jason Heyward, Evan Gattis, Andrelton Simmons, Melvin Upton, Craig Kimbrel, Alex Wood, etc.  Most of the transactions the team has made can be characterized as typical for a rebuilding club — exchange short-term assets for long-term assets with a focus on youth.  You can argue the emphasis on stockpiling pitching is unique, but the general idea of the Braves rebuild fits the standard template.  That is, with the exception of one transaction.

Just before the 2015 MLB Trade Deadline, the Braves sent 24-year-old left-hander Alex Wood and organizational top infield prospect Jose Peraza to the Los Angeles Dodgers for 30-year-old Cuban rookie third baseman Hector Olivera.  The teams exchanged other pieces in the deal (including a 2016 draft pick headed to Atlanta), but the backbone of the trade was Wood and Peraza for Olivera.  In making the deal, the Braves bucked the conventional rebuild philosophy (particularly theirs) in sending out young, cheap, controllable assets while acquiring a more expensive player who was already 30 years old.  It was a bold move that made Olivera and his development hugely important in making the tear-down to build-up strategy a success.  So, eight plus months later, what do the Braves have in Hector Olivera?

The short answer is no one knows.  There simply is not enough of a sample to have any confidence projecting Olivera.  When the Braves acquired him, Olivera was nursing a hamstring injury, so he began his Braves career with a rehab stint in the middle of August.  After a combined six games between the Braves’ rookie and Single-A affiliates, Olivera played another 10 games at Triple-A before making his major-league debut September 1.  He finished 2015 with 87 plate appearances and has added 21 more thus far in 2016 for a grand total of 108 major league PAs.  While 108 plate appearances is not much to go on, this is FanGraphs, so we can do better than shrugging and throwing our hands in the air until the sample grows.

Plate-discipline numbers are some of the first to stabilize after a player is called up.  During his time in MLB, Olivera has walked less than average (BB% of 5.6%) while also striking out less than average (K% of 15.7%) and making contact at a rate just above league average (Contact% of 81%).  A low walk rate combined with a low strikeout rate and near average contact rate means he must be swinging the bat.  Sure enough, that is what is shown on his player page.

O-Swing% Z-Swing% Swing% O-Contact% Z-Contact% Contact%
MLB Average 2015-16 31.0% 67.2% 47.4% 65.0% 86.8% 79.0%
Hector Olivera 37.6% 70.1% 51.7% 71.1% 88.0% 81.0%

Olivera swings at over 4.0% more pitches than the MLB average player.  That alone would not be concerning, except the reason that his Swing% is elevated is mainly because he is swinging at pitches outside of the strike zone, as evidenced by an O-Swing% 6.6% above the league average.  These are the hardest pitches to get the barrel of the bat on, and Olivera’s batted-ball numbers show the effects of swinging at balls outside the zone.

ISO BABIP LD% GB% FB% IFFB% HR/FB Soft% Med% Hard%
MLB Average 2015-16 .153 .301 21.0% 45.0% 34.1% 9.5% 11.4% 18.4% 52.4% 29.2%
Hector Olivera .133 .272 14.5% 50.6% 34.9% 24.1% 6.9% 32.5% 51.8% 15.7%

Despite showing the ability to hit the ball hard with a maximum exit velocity of 110 according to Baseball Savant (approximately 86th percentile thus far in 2016), Olivera has posted ISO and BABIP figures well below the MLB average.  His struggles to make consistent solid contact show up throughout his profile with a low LD%, high IFFB% (a BABIP killer), and low HR/FB ratio.  Perhaps the best summary of Olivera’s MLB batted-ball authority is found within his soft/medium/hard contact percentages.  His medium contact rate is nearly identical to the league average, but Olivera’s hard contact percentage is well below league average with the entire difference and more being accounted for in his soft contact percentage.  Essentially, Olivera’s offensive output has been sunk by a poor approach.  He has swung at too many pitches outside the strike zone, leading to weak contact and therefore poor production on balls in play. 

I haven’t yet touched on his fielding and baserunning numbers.  The Braves were not confident in his ability to stick at third base, so they moved him to left field this past offseason.  Obviously that does not suggest much confidence in his fielding ability, but it remains to be seen how he will perform as an outfielder.  The early returns are not promising as both DRS and UZR have him rated negatively (-2 and -3.7 respectively) in an admittedly microscopic sample of 43 innings.  As for his baserunning, BsR numbers of an exactly average 0.0 leave little reason to expect him to contribute or hurt much on the base paths.  It seems safe to say the bat will be what determines Olivera’s future success. 

Fortunately, the potential in that bat is obvious given the hype surrounding him and ultimately the contract he received coming out of Cuba.  He has also shown the ability to hit the ball hard on occasion at the major-league level, but particularly given the Braves decision to move him from third base to left field, Olivera will need to learn to make much more consistent hard contact to post acceptable offensive numbers.  For the Braves, there is plenty left to see to determine if this trade was a wise investment, but the early returns are not promising.