Last week, August Fagerstrom handed Cincinnati fans an ice-cold Gatorade after we’d spent the better part of three months wandering through the Sahara that is the 2016 Reds baseball season.

After quickly touching on the wide range of sadness, of which there is no shortage—the historically bad bullpen, the woeful luck of one Joseph Votto, and oh, the losses—he rationally pointed to a reason for optimism: “[The Reds’] two most encouraging comeback stories, Zack Cozart and Jay Bruce, just so happen to be their two most sensible trade chips.”

Of Cozart, he wrote:

He leads the Reds in WAR. He enters his final year of arbitration next season. He’s always been a gifted defensive shortstop, something every team loves to have, but this year, he’s hitting at career-best levels…He’s become more aggressive at the plate, he’s hitting way more balls in the air than he did early in his career, and he’s hitting them with authority (emphasis mine).

This last piece is what’s so interesting. Watch any Reds game this year—or just stay through the leadoff hitter! I swear, that’s all I’m asking!—and you’ll hear announcers remark that Cozart is just hitting the ball differently this year.

As shocking as it may seem given recent broadcaster rankings, they’re right.

Cozart’s 2016 hard-hit rate of 33% is very good, if not earth-shattering. It ranks seventh amongst shortstops, and league-wide places him in the company of (slightly) more celebrated offensive names like Bogaerts, Kinsler, and Beltre.

But there’s a caveat to Cozart’s great contact. The number isn’t just a career high; it’s a massive outlier, sitting 8.5% above his career average. How many other hitters this year are enjoying similar spikes? I pulled all “jumps” above six points for 2016 qualified hitters, with a minimum of four seasons to ensure a stable career average:

This list shouldn’t be too surprising. While not a perfect indicator, we know that hitting balls hard is generally better than the alternative—and these guys, with one giant, Vottoian exception, are all in the midst of stellar years by more traditional metrics. Altuve owns baseball’s third-best WAR; Murphy remains one of baseball’s best bargains; Martinez and Ortiz continue to defy Father Time to the tunes of wRC+ of 142 and 194(!), respectively. Even Votto, recovering from a BABIP 60 points below his career average, is rapidly coming around.

The group presents club evaluators, though, with a very tough question: How “real” are these spikes in hard-hit rate, and by extension the jumps in offensive performance? For Cozart, the question for the Reds front office basically translates to: How likely is he to keep the hard contact up, and how quickly should we trade him?

Let’s start with what we know about hard-hit rate. It’s generally a repeatable skill, as a FanGraphs study from last year puts hitters’ YoY correlation at 0.69. We can also say that there’s no real drop in age to adjust for; I found the r-squared correlation between age and hard-hit rate to be 0.02.

It’s not crazy, then, to think that a career-high spike in hard-hit rate could be the start of long-lasting improvement. And if it is, we should see it in the years around the spike: an increase the year before that hinted at a breakout, or retaining/coming close to the same rate in the next few seasons.

But is that the case?

I pulled all hitters with a hard-hit-rate YoY “jump” above 9% since we began tracking the stat in 2002 to see how they fared in the years immediately before and after. In this chart, “Year Before” is the hard-hit rate versus career average for the season prior to the jump, with Y+1/Y+2 representing the two years following it:

If you’re looking for a pattern, don’t bother: none really exist, aside from the observation that 2007 was, apparently, The Year of Hitting Baseballs Hard (a poorly anticipated sequel to The Year of Living Dangerously).

The “Jump” years make up a few of the better hitting seasons in modern history: Jimmy Rollins’ MVP campaign of 2007, Teixiera’s famous Rangers/Braves split season in which he posted a wRC+ of 146, even a Raul Ibanez “I’m Not Dead Yet” season with Philadelphia at age 37.

More importantly, though, surrounding these seasons on either side is case after case of regression—and not even particularly close regression at that. There doesn’t seem to be any ability to carry over a hard-hit-rate jump into the next year or beyond.

These seasons aren’t necessarily the same as those supported by BABIP-fueled mirages…but they are propped up by a contact rate that just doesn’t seem to hold up in any type of long run. It’s something that makes sense on an intuitive level: no one, even someone as skilled as a big-league hitter, wakes up and says, “Oh, yeah—that’s how I can hit the ball hard from now on,” then keeps it up for the rest of their career.

A 162-game baseball season may seem long, but it’s subject to many forms of chance, including the odds that some years you’ll strike the ball harder than others. For the purposes of evaluating our 2016 list, it’s info that is more “useful” than immediately actionable: every player on the list except Cozart is signed through at least 2018, while Ortiz, in a Breaking Bad-level identity switch, will hang up his spikes to try his skills as a masseur in Minneapolis.

But it’s a worthy piece of evidence that our protagonist will spend next year likely reverting to average or even worse at the plate—and that the Reds, by extension, should pursue every possible trading avenue for Cozart this summer while the hitting is hard.

Recently the Cleveland Indians were able to salvage their four-game series against the Seattle Mariners with a 5-3 victory, thanks to Tyler Naquin. In the top of the 8^th inning with teammate Rajai Davis on first base, Naquin again found himself in an 0-2 count. Once again, it seemed that the rookie would strike out…especially because he was facing an excellent reliever in Joaquin Benoit. Going into the game, Benoit found himself with a respectable 3.27 ERA, 1.09 WHIP, and a BAA of just .154. But when Naquin came to the plate all of that was about to change. On an 0-2 pitch, Benoit threw Naquin a changeup down and in that he promptly golfed into the stands of Safeco Field giving the Tribe a 4-2 lead in the late innings. This advantage would end up sticking for the Tribe as they went on to split the four-game series and remain in first place in the AL Central.

Naquin is no stranger to hitting homers in the big leagues. In fact, at the time that was his fourth homer in his last six games. Before his most recent recall on June 1st, Naquin hadn’t yet hit one out of the park in the bigs. But now it appears that he has found his power stroke, and his team couldn’t be happier. Naquin always had a great swing; even looking back on his days at Texas A&M, that was more than apparent (he won two Big-12 batting titles). It appears now that he’s beginning to develop power. In the minors, Naquin managed just 22 homers in his 1542 plate appearances, a modest 70.1 PA/HR. In his short time in the majors this number has dropped significantly down to 22.3 PA/HR. In other words, around 27 HR in a 600 plate appearances. The power that he’s shown thus far has been quite impressive, and there’s a chance that it’s sustainable.

Naquin has shown the ability, throughout his minor and now major-league career, to possess a great swing with the ability to make good, solid contact which has translated well to this point. Naquin has a 41% hard-hit rate. Qualified players who have a hard-hit rate above 39% this season include the following list:

From the chart, 20 of the 26 players listed are in double digits in homers. If you take their ratio of HR/PA and multiply by 600 you find that they range anywhere from 27 HR to 48 HR potential. There’s no guarantee that any of these power hitters will keep their current pace, but one thing’s for sure, players who have a relatively high hard-hit rate are more likely to hit home runs and extra-base hits, and ultimately are more likely be more productive for their team. If we go back even further now, say the last three seasons (2013-2015), we get the following group:

The chart says it all: the average HR% (HR/PA) of this group is 4.8%, or in other words about 29 HR per 600 PA. The average OPS of this group is an impressive .876, and even more impressive the average wOBA is .374. If Naquin can continue to make solid contact in his plate appearances, as he has proven throughout his career, he could be a very special player.

In the case of Tyler Naquin, he has: 99 PA, 41 Hard%, 4 HR, .870 OPS, 136 wRC+, and a .371 wOBA. His numbers correlate quite well to the rest of the group; in fact, his OPS, wRC+, and wOBA are all above or around the average in comparison. Obviously this is kind of a small sample size for Naquin. It’s nearly impossible to tell what kind of player Naquin will become with less than 100 major-league plate appearances, but there is definitely hope.

The MLB Rule 4 amateur draft was last week and fans will clamor for any sort of information regarding their team’s new, shiny, sometimes 18-year old future stars.  The draft gives fans a chance to dream on what will be in seasons to come, each team’s fans are hoping for their very own Mike Trout.  But for every Mike Trout, there are plenty of players like Hank Congers or Zack Cox who were also selected at pick number 25 and who aren’t exactly rewriting the record books.

In doing research for my latest post on the awful Jim Bowden, I found a concerning lack of recent research on draft success. We have plenty of anecdotes, and plenty of information on top prospects busting, but very little in the way of what to expect from a team’s first-round draft pick.  I found a good piece from 2012 from The View from the Bleachers on Success Rate of MLB Draft Picks by Slot and referenced that, but there’s definitely more here.

There have been nine drafts since the last draft referenced in that post.  Scouting, sabermetrics, and our general collective baseball knowledge feels like it has been increasing exponentially in that time.  Does draft success bear that out? Well, not exactly.

The first thing to set up here is to establish a “successful” player. I pondered it for a minute and settled on basically the same approach that Michael used way back in 2012. If the player hasn’t made the majors, or if they had a WAR of less than 1.5 per year when they got there, that first-rounder is a bust automatically. These players might be useful, but hardly the type that an organization should target in the first round. With that in mind, I established a simple calculation to assign a players success.

I likely should have built in a separate “World’s Best” category for those players who are averaging 8+ WAR.  Oh, that’s just Trout, OK.

The calculation feels like it makes sense on an anecdotal level, too.  Eric Hosmer, Yonder Alonso, and Wade Miley are labeled successful, but not superior.  That feels right.  These guys aren’t changing an organization.  They’re good major league players, but not great.

The trick comes in assigning busts, especially when considering players from more modern drafts.  Jameson Taillon has yet to achieve the mandatory 1.5 WAR, but he’s hardly a bust just yet. And what do we do with guys like Billy Butler? He’s officially a bust by my calculation, but that doesn’t feel quite right. Huston Street, James Loney, and Garrett Richards are all also busts.  Ike Davis, and Pedro Alvarez, too. But the formulas are sound.  A successful major leaguer should be able to produce 1.5 WAR per season. In 2015, Chase Headley, Nick Markakis, and Alcides Escobar all hit that threshold.  It shouldn’t be too much to expect a first-rounder to perform at that level.

Besides, this is baseball and statistics.  There’s no crying in baseball or statistics.

To the results!

First, how many of 1^st rounders actually make the majors? That feels like some basic threshold of success. Is your organization capable of selecting a player in the first round that actually makes his way to the majors?

A few things jump out from the chart above. Of the 55 players selected in the top five between 2000 and 2010, 48 reached the major leagues. That seems like a really good rate. Teams were able to more or less successfully identify the best five players available in a given draft. Of course, there’s probably some bias here as teams are more likely to promote players they took at the very top of the draft to save face, even if they might not be perfectly qualified.

The pattern pretty much holds for the rest of the first round too. There’s more uncertainty as you get later and later in the draft but scouts seem to hit more than they miss. That’s a pretty low bar though. You would hope that scouts would be a bit better than 32/55 (58%) on picks 26-30, considering that there are hundreds and hundreds of players chosen.

Next, let’s look at the chance to find a successful player, as we defined it earlier, in the first round of the draft.

That’s pretty low. Our definition of a successful player was pretty narrow, to be sure, but it seems like 1.5 -2.5 WAR guys should be pretty prevalent. Guess not. Let’s see how front offices do on picking up superior players.

Pretty well actually! Superior players should be pretty rare, at least if we set the criteria correctly, but more than a quarter of top five picks are in that category. That seems pretty good.

I’m starting to wrap my head around a theory, let’s see if this next chart bears it out…

OK, here’s what I’ve got. It’s more likely than not that a first-round selection will be a bust. If he’s not a bust, though, it’s more likely than not that he’ll be a superior player. It seems like the chances of a first-rounder being merely successful — just a decent big-league player — are actually pretty small.

A reasonable conclusion then, is that scouts go for the proverbial home run in first-round selections. They take a bit more risk in order to try and unearth a truly unique talent. They then aim to fill out their system with more average players in the later rounds.

My research gives fans and scouts all the more reason to dream on their first-round picks from last week.

A last little bit of fun.  For the recent draft, I wanted to point out which organizations were selecting in a spot that may not yield quite the results that they are hoping for. Yankees fans, shield your eyes.

So before you go getting all excited about the draft picks in the books, keep in mind that a majority of them are simply going to be busts. The ones that aren’t, though — they’ll probably be stars.

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams

Assessment

The 1975 Houston Astros 

OWAR: 50.0     OWS: 291     OPW%: .535     (87-75)

AWAR: 28.7      AWS: 192     APW%: .398     (64-97)

WARdiff: 21.3                        WSdiff: 99  

The “Original” 1975 Astros fell six games short of the National League Western Division title as the Big Red Machine tallied 93 victories. Joe L. Morgan produced a .327 BA with 17 dingers, 94 ribbies and 107 runs scored to secure the NL MVP Award. “Little Joe” succeeded on 67 of 77 stolen base attempts and coaxed a League-leading 132 bases on balls. First-sacker John Mayberry racked up personal-bests in doubles (38), home runs (34), RBI (106), runs (95) and bases on balls (119). Rusty Staub swatted 19 big-flies and knocked in 105 baserunners. Cesar Cedeno swiped 50 bags and batted .288 while Bob “Bull” Watson delivered a career-high BA (.324) for the “Original” and “Actual” ‘Stros.

Joe L. Morgan is ranked as the top second baseman according to Bill James in “The New Bill James Historical Baseball Abstract.” “Original” Astros teammates listed in the “NBJHBA” top 100 rankings include Cesar Cedeno (21^st-CF), Rusty Staub (24^th-RF), Bob Watson (33^rd-1^st), John Mayberry (49^th-1B), Doug Rader (64^th-3B) and Jerry Grote (66^th-C). “Actual” Astros outfielder Jose Cruz places 29^th among left fielders.

  Original 1975 Astros                                    Actual 1975 Astros

Houston hurlers failed to generate much excitement during the ’75 campaign. Larry Dierker completed 14 of 34 starts and fashioned a record of 14-16 with a 4.00 ERA. Pat Darcy posted an 11-5 mark with a 3.58 ERA in his inaugural season. Dave Giusti furnished a 2.95 ERA and saved 17 contests despite accruing more walks than strikeouts.

  Original 1975 Astros                                    Actual 1975 Astros

Notable Transactions

Joe L. Morgan

November 29, 1971: Traded by the Houston Astros with Ed Armbrister, Jack Billingham, Cesar Geronimo and Denis Menke to the Cincinnati Reds for Tommy Helms, Lee May and Jimmy Stewart.

John Mayberry

December 2, 1971: Traded by the Houston Astros with David Grangaard (minors) to the Kansas City Royals for Lance Clemons and Jim York.

Rusty Staub

January 22, 1969: Traded by the Houston Astros to the Montreal Expos for Jesus Alou and Donn Clendenon. Donn Clendenon refused to report to his new team on April 8, 1969. The Montreal Expos sent Jack Billingham (April 8, 1969), Skip Guinn (April 8, 1969) and $100,000 (April 8, 1969) to the Houston Astros to complete the trade.

April 5, 1972: Traded by the Montreal Expos to the New York Mets for Tim Foli, Mike Jorgensen and Ken Singleton.

Honorable Mention

The 2013 Houston Astros 

OWAR: 26.6     OWS: 218     OPW%: .427     (69-93)

AWAR: 8.3       AWS: 151      APW%: .315    (51-111)

WARdiff: 18.3                        WSdiff: 67

Following a transfer to the American League West prior to the start of the 2013 campaign, the “Original” Astros finished dead last in the division. Nonetheless it represents a WSdiff of 67 and 18 additional wins compared to the “Actual” Astros from the same season. Hunter Pence established career-highs with 27 round-trippers and 22 stolen bases. Ben Zobrist laced 36 doubles and earned his second All-Star nod. Chris Johnson produced personal-bests in batting average (.321) and two-base hits (34). Jason Castro drilled 35 two-baggers and posted a .276 BA. Jose Altuve batted .283 and pilfered 35 bags.

On Deck

What Might Have Been – The “Original” 1984 Giants

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “www.retrosheet.org”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

Like many of you, I spend hours on FanGraphs trying to take in as much information as possible. One of the more fascinating statistics to me is the category of plate discipline. This includes how often a batter will swing on a pitch inside or outside of the zone, how often a batter swings and misses, and many other variables that affect a player’s approach at the plate. While these numbers alone are a good indication on how a player acts at bat, I wanted to know how these numbers affected performance. For instance, it would make sense that a higher O-Swing% could lead to less-than-average hitting. The 2015 season had Adam Jones second in O-Swing%, swinging at 47% of pitches outside of the strike zone. Pablo Sandoval led the league in O-Swing% with a 48% rate. Jones recorded a 109 wRC+ while the Kung Fu Panda had a 75 wRC+.

By breaking down these Plate Discipline statistics for the 2015 season, I believe that we can get a good answer on which statistic leads to the best performance. For my methodology, I used the wRC+ and Plate Discipline leaderboard for the 2015 season. After breaking down each statistic, I compiled a top 10 and bottom 10 wRC+. Additionally, I grouped percentages to get the number of batters and average wRC+ for certain percentages.

O-Swing %

O-Swing% = Swings at pitches outside the zone / pitches outside the zone

Top 10 wRC+ Average: 97

Bottom 10 wRC+ Average: 132

O-Swing% gives us a pretty good indication of a player’s overall performance. It’s no surprise that patience and a good eye are part of a skill set that leads to a higher wRC+. For each 10-percent decrease of O-Swing percentage, batters see an increase of over 10 points for their wRC+. The top 10 wRC+ compared to the bottom 10 also tells a compelling story of what O-Swing tells us. In the top 10, we see a couple of above-average hitters like Starling Marte and Nolan Arenado. However, we also see five of the top 10 with a wRC+ under 100 and one hitter (Marlon Byrd) at 100. On the other side of the spectrum, there isn’t a hitter under 100 wRC+ in the bottom 10. The difference in wRC+ between the top and bottom 10 is 35, the biggest difference between all the statistics.

Let’s look at two very different extremes: Joey Votto and Pablo Sandoval. Sandoval had an O-Swing% of 48 percent while Votto had a 19 percent rate, which means that while Sandoval is swinging at almost half of the balls he faces, Votto is taking a little more than 80% of pitches out of the zone. Sandoval faced 1848 pitches (1287 strikes to 561 balls) while Votto faced 3020 pitches (1644 strikes to 1376 balls). Sandoval’s more than double strike-to-ball ratio and Votto leading the league in walks can both be explained by their O-Swing percentage.

Z-Swing %

Z-Swing%  = Swings at pitches inside the zone / pitches inside the zone

Top 10 wRC+ Average: 111

Bottom 10 wRC+ Average: 115

The first thing that I noticed when looking at the Z-Swing charts is the duplication of names from the O-Swing charts. Adam Jones, Avisail Garcia, Marlon Byrd, and Starling Marte showed up on both the O and Z Swing percentage top-10 while Ben Zobrist, Carlos Santana, and Brett Gardner appeared on both bottom-10 lists. This is a very mixed bag of players for both the top and bottom. Both have a 100 wRC+ hitter, the epitome of average. Both have seven hitters batting above 100 wRC+ meaning that both lists also have two hitters batting below 100. The top and bottom 10 averages are almost even. The one outlier that separates them is Mike Trout in the bottom 10 with a 172 wRC+. Seeing the same name on multiple lists can tell us a lot about a player. Someone like Marlon Byrd will swing at most of the pitches you send his way while Ben Zobrist will take a pitch outside of the zone about 77% of the time but will also take a strike 45% of the time as well.

O-Contact %

O-Contact% = Number of pitches on which contact was made on pitches outside the zone / Swings on pitches outside the zone

Top 10 wRC+ Average: 104

Bottom 10 wRC+ Average: 104

Similar to Z-Swing%, O-Contact doesn’t show much disparity between the top and bottom 10. In fact, they’re identical at 104 wRC+. A higher O-Contact gives a batter more balls in play, but doesn’t always lead to success. My initial thought was that swinging at a pitch way out of the zone can lead to weak contact, and usually an out. The fact the top and bottom are identical shows that this isn’t always the case.  It also makes sense why the middle of the pack (60%-69%) has the greatest wRC+ (besides the small sample size of < 50%). These batters are still able to make contact with pitches outside of the zone more than half of the time, but also miss the pitch enough of the time where they don’t make bad contact.

Z-Contact %

Z-Contact%  = Number of pitches on which contact was made on pitches inside the zone / Swings on pitches inside the zone

Top 10 wRC+ Average: 110

Bottom 10 wRC+ Average: 125

Z-Contact was the most surprising statistic in terms on its effect on wRC+, until you look at the names in the bottom 10. One would expect that hitters that hit more pitches in the zone would be the better performers. However, the bottom 10 is filled with power hitters, leading to the main difference in wRC+. Davis and Cruz were number one and two in terms of home-run leaders in 2015. In fact, besides Michael Taylor, the bottom 10 is all in the top 50 for home runs in the MLB. The list makes sense as players like Chris Davis are trying to “Crush” the ball out of the park and swing harder than someone in the top 10 like Martin Prado.

SwStrike %

SwStr% = Swings and misses / Total pitches

Top 10 wRC+ Average: 110

Bottom 10 wRC+ Average: 105

Not surprisingly, the top 10 for SwStrike looks a combination of both the O-Contact and Z-Contact bottom 10. Obviously if your contact is low, you’re going to have more swings and misses. The main factor that stood out to me looking at the top and bottom 10 is the deviation of wRC+. The top 10 is all over the place, having players like Kris Bryant with a 136 wRC+, Michael Taylor with 69, and every level of player in between. The bottom 10 has less variation, providing a more consistent group of hitters.

Totals

As evidenced by the chart, the main statistic in regards to plate discipline to show a great change in performance that compares the bottom to the top level is O-Swing percentage. Z-Contact seems to also be relevant when evaluating and predicting a player’s performance.

The Rays always seem to be at the forefront of sabermetric innovation. They employ an army of Ivy League baseball analysts in the front office, they fully embrace the shift, and they employ pitch-framing superstars. The Rays like to stay on top of the ball. For the Rays, sabermetric advancement is a means of survival. And for the Rays, in the powerhouse AL East, it is the only way to survive.

Over the past seven years, it seems the Rays have been on to something. Looking at FanGraphs team offensive data from 2009 to 2016, there is a clear pattern with the Rays. They are third in fly ball% at 37.5%. The team with the highest FB% during that time span is the Oakland Athletics. The A’s pursuit of fly-ball-happy hitters was pretty well documented. In a great article over at Deadspin from 2013, Andrew Koo (who now works for the Tigers) shows us the advantages of hitting fly balls. First, Koo highlights how fly ball rates have decreased in the league since 2009. With an increasing trend towards ground ball pitchers, Billy Beane made a clear effort to acquire fly ball hitters. Why? Because as Koo shows us, fly ball hitters are significantly better against ground ball pitchers compared to other batters.  Tom Tango, who is mentioned in the Koo article,  found that this platoon advantage is very minimal, and is really only realized and meaningful when the “advantage is multiplied through several hitters. This is exactly what the A’s and Rays have done over the past seven years. Both teams have stockpiled fly ball hitters.

The Rays have done something else too. They have stockpiled fly ball hitters that also have a knack for pulling the ball. Over the past seven years, they lead the league in Pull% at 42.8%. Looking at this year’s team, the strategy seems to be in full effect once again. Of all the Rays hitters with at least 100 PA this year, only three players (Miller, Forsythe, and Dickerson) are below the league average in Pull%. Now, it could be pure coincidence that the Rays pull the ball so much. But I think we all know this is no coincidence at all. They seem to be preaching the pull-happy approach.

When looking at offensive data on pulled balls vs. data on other batted ball directions, the strategy makes sense. Looking at league data from 2009 to 2015, the average wRC+ on balls hit to the pull side is about 157, compared to 112 on balls hit up the middle. Isolated power on balls hit to the pull side is over 100 points greater than on balls hit up the middle or to the opposite field. There is an offensive advantage to pulling the ball, when the ball is put in play. Given the clear advantage to hitting the ball to the pull side, one might ask why wouldn’t every team stockpile dead pull hitters?

One answer: conventional wisdom says dead pull hitters don’t have the right approach. From the time I started playing baseball, I have been told to hit the ball to all fields. And I don’t disagree with this philosophy. Staying back and being able to drive the ball to all fields definitely makes for a very productive hitter. But it also results in dead pull hitters being undervalued.

Another knock on pull hitters is that when they hit ground balls, they roll over the ball and commit easy outs.  Looking at the soft hit percentage vs ground ball percentage on balls that are pulled for all 30 teams from 2009-2016, I found this to be a valid concern about pull hitters. The data shows a positive correlation between ground balls and soft hit percentage. 

The Rays, however, have the fourth-lowest GB% on pulled balls. During that same time span, the Rays have the sixth-lowest Soft% on ground balls. They aren’t hitting weak ground balls. The Rays have made a concerted effort to pull the ball and they have avoided the weak contact that comes with pulling the ball on the ground.

Conclusion

The Rays have found and pounced on a market inefficiency. They have optimized their offense by targeting and developing players that consistently pull the ball in the air and avoid weak contact on the ground. Since these players aren’t the conventional hit-to-all-fields type of player, they can get these players for cheap. Simply put, the Rays have have capitalized on the offensive advantage of pulling the ball.

Food For Thought

Something to think about further is the trend of Pull% in the MLB from 2002-2016. It is down 5%. Intuitively, this makes sense, as velocity is way up over that time span. With velocity up, it is harder to pull the ball. This trend reminded me of a trend mentioned earlier in the article. As noted by Koo and Tango, ground balls are up around the MLB. As Tango found, fly ball hitters have an advantage against ground ball pitchers, and it is beneficial to utilize that advantage. What if there is a similar platoon advantage regarding pull hitters vs. power pitchers? In line with Tango’s logic, what if dead pull hitters have a platoon advantage against power pitchers? What if the Rays have figured out this advantage and have been exploiting it for years? The platoon advantage makes sense. Dead pull hitters, by nature, go up to the plate looking to pull the ball. Which means they are early on almost everything. As a result, they wouldn’t have as much trouble catching up to gas. This is definitely something to think about, and something I will be certainly researching in the coming weeks.

It seems that 2016 will be the year that Statcast begins to permeate Fantasy Baseball analysis. Recently there has been a wealth of articles exploring the possibilities of using these kinds of data. These pieces have provided relevant insights on how to improve our understanding of well-hit balls and launch angles. Also, they’ve facilitated access to information on exit velocity leaders and surgers, as well as provided thoughtful analyses to the possible workings behind some early-season breakouts.

However, there is still a lot we don’t know about Statcast data. For instance, we are uncertain of how consistent these skills are over time, both across seasons or within seasons. Also we don’t know what constitutes a relevant sample size or when rates are likely to stabilize. All in all, this makes using 2016 Statcast data to predict rest of season performance a potentially brash and faulty proposition. Having said that, we can’t help but to try; so here’s our attempt at using early-season 2016 Statcast data to partially predict future performance.

One of the early gospels of Statcast data analysis posits that the “sweet spot” for hitting homers comes from a combination of a launch angle in the range of 25 – 30 degrees and a 95+ MPH exit velocity. If this is indeed the ideal combination for hitting home runs, one could argue that players that have a higher share of fly balls that meet these criteria should perform better in other more traditional metrics such as HR/FB%.

Following this line of thought we dug up all the batted balls under the “sweet spot” criteria, and divided them by all balls hit at a launch angle of 25 degrees or higher (which MLB determines as fly balls) to come up with a Sweet Spot%. In an attempt to identify potential HR/FB% surgers, we compare Sweet Spot% and HR/FB% z-scores (to normalize each rate) for all qualified hitters with at least 25 fly balls and highlight the biggest gaps.  Here are the Top five gaps considering the games up to May 28^th:

Calhoun seems like a good candidate for a power uptick. He has the third-highest Sweet Spot% of 2016, and he has sustained similar Hard% and FB% to the previous two seasons. Yet somehow he has managed to cut his HR/FB% to less than half of what he put together in either 2014 or 2015.  More so, he has had some bad luck with balls hit in the “sweet spot”; his batting average in these kinds of balls is .500, whereas the league average is around .680. He is not killing fly balls in general, with an average exit velocity of 84.6 MPH, but if he keeps consistently hitting balls in the “sweet spot” range he should improve in the power department. Look out for a potential turnaround in the coming weeks and a return to 2015 HR/FB% levels.

Piscotty holds second place in the Sweet Spot% rankings. However, his FB% is very similar to what he did in 2015 whilst his Hard% is down from 38.5% to 32.5%. Lastly, he plays half of his games in Busch Stadium, which has a history of suppressing home runs. I would be cautious of expecting a major home-run surge, but in any case Piscotty is likely to at least sustain his performance in the power department, which would be welcome news to owners that got him at bargain prices.

Carpenter is another dweller of Busch Stadium, however his outlook might be a bit different. He is the absolute leader in Sweet Spot%. He is posting the highest Hard% and FB% marks of his career. Carpenter is also crushing his fly balls in general, with an average Exit Velocity of 93.7 MPH. Just as a point of reference Miguel Cabrera, Josh Donaldson and Giancarlo Stanton fail to reach an average of 93 MPH on their own fly balls. Lastly, he has had some tough luck with balls hit in the “sweet spot”, posting a batting average of just .420. Carpenter is already putting up the highest HR/FB% of his career, and he is a 30-year-old veteran of slap-hitting fame, but the power looks legit and perhaps there is more to come.

Denard Span and Yonder Alonso show up in this list not because of their Sweet Spot% prowess but rather due to their putrid HR/FB%. They barely crack the Top 50 in Sweet Spot%. They play half their games in two of the bottom three parks for HR Park Factor. Span is putting up his lowest FB% and Hard% rates since 2013, when he ended up with a HR/FB% of 3.4%. Meanwhile, Yonder’s rates most closely resemble those of 2012, when he had a HR/FB of 6.2%. Whilst their batting average of “sweet spot” batted balls is just .500, there is nothing to look here. In any case, their power situation looks to improve from bad to mediocre.

If you are interested in the perusing the Top 50 gaps between HR/FB% and Sweet Spot%, please find them below:

Note: This analysis is also featured in our emerging blog www.theimperfectgame.com

This is the final part of a six-article series on xHR%, a metric devised rather unoriginally by myself. If you feel so inclined, you can look at the other parts here: P.1, P.2, P.3, P.4, P.5.

It’s always nice when things mostly work out. More often than not, when someone devotes countless hours to some pet project, whether it’s a scrapbook of some variety or an amateur statistical endeavor, it doesn’t work out terribly well. From there, one often ends up spending nearly as many hours fixing the project as they did on putting it together in the first place. The experience is incredibly frustrating, and it’s something we’ve all gone through at one time or another.

Luckily, my “quest” went much better than that of Juan Ponce de León.  While I didn’t find the fountain of youth, I did find a formula that works moderately well, even though I can only back it up with one year of data at this point. The only thing Señor Ponce de León has to brag about is being arguably the second most important explorer in colonial history. Somehow those things don’t compare particularly well.

Nonetheless, things do look quite good for xHR% v2. I culled data from a variety of sources, but mainly from FanGraphs and ESPN’s selectively responsive HitTracker. I used FanGraphs for FB%, HR, AB, and strikeout numbers (in order to find BIP, I subtracted strikeouts from at-bats). On the other hand, HitTracker was used just for home run distance numbers and launch angle data. I studied all players with at least 1200 plate appearances between 2012 and 2014 in order to ensure some level of stability for the first sample taken.

And so, without further ado, take a few seconds to look at some relatively interesting graphs (I forgot to title the first one, but it’s xHR vs HR).

Here, it’s fairly easy to discern that there’s a strong relationship between expected home runs and home runs. It doesn’t take John Nash to figure that out. What is fairly interesting, however, is that the average residual is quite high (close to 2.5), indicating that the average player in the sample hit approximately +/-2.5 home runs than he should have. That difference comes from a number of factors which the formula attempts to account for. They include home ballpark, prior performance vs. current performance, and weather. One of the issues, and this was bound to be a problem because of the sample size, is that there aren’t enough data points for players who hit 40+ home runs, so it’s hard to say how accurate the formula actually is as a player approaches that skill level.

This is a slightly zoomed-in version of expected home run percentage vs home run percentage. Clearly, there’s a much stronger relationship between HR% and xHR%, due in large part to the size of the digits and because the formula was written to come out with a percentage, not a solid number. But I won’t waste too much time on xHR% because, quite frankly, it’s far less interesting and understandable than actual home run numbers.

For the interested and worldly reader, here are the equations for each:

xHR: y=.0019x²+.9502x+.1437

xHR%: y=1.0911x²+.9249x+.0007

If either of these equations gets used at all, I expect it will be xHR because home run numbers are far more accessible than home run percentage numbers. Frankly, I regret writing the formula for xHR% for that very reason. This is supposed to be a layman’s formula, so its end result should be something understandable to the average baseball fan. It should be self-evident and easy to comprehend.

Thank you for following along as the formula developed over time. Obviously, it isn’t done yet and it requires some changes, but it’s close enough to where it needs to be. It’s very similar to getting to the door of the room where the Holy Grail is, shrugging, and turning around with the intention of coming back in a few weeks (although in this case it must be noted that the Holy Grail isn’t the real one, but a plastic one covered in lead paint). Expect a return under a different name and a better data set.

You’ll notice that I didn’t include very much statistical analysis at all. I figured that was rather boring to write about, but you can feel free to contact me for the information if you would like a nice nap.

Edit: Since this piece was submitted (May 23), several developments in the Martin narrative have arisen, notably some more astute analyses than mine (namely Jeff Sullivan’s great piece on Martin’s batted-ball profile & an extremely in-depth look at his swing mechanics by Jason Churchill over at ProspectInsider, do go check him out) as well as this walk-off dinger against the Oakland A’s. 

A lot has gone right for the Seattle Mariners in new GM Jerry Dipoto’s first season. At time of writing, they sit in first place in the AL West with the third-best record in the American League and the best road record in baseball. One potential factor in Seattle’s success that has, until recently, taken a backseat to Robinson Canó‘s resurgence and Dae-Ho Lee’s power-hitting heroics is the sudden onset of what could turn out to be an offensive breakthrough for outfielder Leonys Martin.

The Mariners’ acquisition of Martin and Anthony Bass in exchange for Tom Wilhelmsen, James Jones, and a PTBNL (Patrick Kivlehan) is one of several moves last offseason that seem to follow a common guiding principle: bring in players who’ve struggled in recent seasons but demonstrated real value in seasons past. This category includes the likes of Steve Cishek and Chris Iannetta, both of whom seem to have (thus far) rebounded from uninspiring 2015 campaigns.

Meanwhile, Leonys Martin is having the best season of his life. This is mostly remarkable due to the fact that his hitting isn’t, and has never really been, the source of his value. He’s never topped 89 wRC+ in any season, and his career high for home runs in a year is eight. He’s also been historically abysmal against left-handed pitching. From 2012-15, Martin slashed .233/.274/.298 with 53 wRC+ against southpaws; no outfielder in baseball posted fewer wRC+ in that same span (min. 300 PAs). His poor performance in the second half of 2015 (.190/.260/.190 with 22 wRC+ after the All-Star break) earned him a demotion in early August. That lackluster second half, coupled with the emergence of Delino Deshields Jr. as a capable replacement, made it a lot easier for the Rangers to part with him in the offseason (incidentally, DeShields was demoted in early May and Wilhelmsen has been the worst reliever in the majors this year by fWAR, so that’s something).

Going into this season, Steamer projected him for around 492 PA with a .241/.292/.350 slash line and 79 wRC+, in addition to eight homers and 22 stolen bases, putting him on course for 1.2 fWAR. While not exceptional, this likely would have been an adequate season for Jerry Dipoto given the cost, especially at Martin’s $4,150,000 salary, but Martin’s already managed to match that mark, posting 1.4 fWAR as of May 23rd, and he’s providing a great deal of that value with his bat.

Martin seems to have shook off a bit of whatever seemed to be plaguing him at the tail end of 2015. He’s slashing .252/.331/.467, which would, over a full season, leave him with a career-best OPS of .798 and 124 wRC+. He still hasn’t been able to hit lefties, but that’s what platooning is for. But by far the most eye-popping aspect of Martin’s game this year is what looks like a sudden influx of power. Martin’s mark of .215 ISO is easily the best of his career — his eight home runs have already matched his career-best single-season total — and it’s not even June yet. With no context, one could look at Martin’s line thus far and notice that he might be on pace to post a 30 HR/30 SB season, if not for the slight inconvenience called “At No Point In His Career Has Martin Demonstrated That He Might Even Touch 30/30”. And yet this is baseball, and this is 2016, the Year of the Bartolo Colón Home Run. Anything is possible.

So — what’s changed for Martin? And perhaps more importantly, where the heck did all these home runs come from?

We turn first to Martin’s batted-ball profile. For the last two-and-some seasons, Martin’s fly-ball percentage has actually increased. His 2015 mark of 33% was actually a career-best at the time, especially considering it was brought down by his abysmal second half. He’s picked it back up in 2016, with a gaudy 45% fly-ball rate. Of course, the sustainability of this figure is questionable (one might also point out Martin’s likely inflated HR/FB rate of 20.5% — opposed to a current league average of 12.1%), but at no point in his career has Martin hit fly balls with such consistency:

Other indicators of improved power add credence to this positive trend. Martin’s quality of contact also seems to have improved this year, as his hard-hit ball rate of 34.4% is vastly superior to his pre-2016 range of about 23 – 25%. It’s also true that home/road splits affect the narrative somewhat, as only one of his eight home runs occurred at Safeco Field. But I suspect that there may be more to Martin’s offensive resurgence than just hitting balls harder.

One of the feel-good narratives of this season is the positive influence that new hitting coach Edgar Martínez has introduced to the Mariners offense, which currently ranks 2nd in the AL in runs scored. Martinez was brought in to replace Howard Johnson in June 2015, hoping to fix an anemic Mariners offense that struggled early and often. To date, that new appointment has been received with praise from Seattle media and fans, but more importantly from the players themselves. Could it perhaps be the case that Edgar’s tutelage, along with Jerry Dipoto’s promise to mold the 2016 Mariners to fit his “Control the Zone” philosophy, has brought about a positive change in the way Leonys Martin approaches hitting?

Overall, Martin’s plate discipline metrics show that his approach at the plate hasn’t changed too drastically from last season. If anything, his 70.4% contact rate is his lowest since 2012. One other thing sticks out here, namely that Martin seems to be more patient on pitches out of the zone and more aggressive on pitches in the zone. Compare the percentage of pitches he swings at in 2015 (left) to 2016 (right), courtesy of BrooksBaseball.net:

There is a relatively noticeable difference here, especially on high and outside pitches. According to PITCHf/x, his O-Swing% of 27.9 is easily the lowest of his career. Likewise, his Z-Swing% of 67.0 is his highest since 2012. These are generally good indicators that Martin is seeing the ball better or, at least, cut down on his tendency to chase pitches out of the zone.

And then there’s the matter of his batting stance.

Take a look at his stance for this home run on May 27, 2015, facing off against Scott Atchison:

Now check out his stance almost a year later, on May 22, 2016 in this at-bat against John Lamb.

An important thing to note about these stills is that I picked them mostly because of their similar camera angles. Martin’s foot position in other highlights is often obscured by the pitcher, or the pitcher is already in the middle of his wind-up, giving Martin time to square up before the pitcher’s delivery (as is slightly apparent in the at-bat against Lamb). But the vast majority of video evidence from this season is consistent with the idea that Martin has generally closed off his stance and now begins pretty much every at-bat with his feet squared to the pitcher. Now, I am aware that the batting stance is a rather fluid component of any baseball player’s oeuvre and can change for a number of reasons, not all of them being deliberately engineered to improve performance. I can’t seem to find anything about Martin having changed his stance online, aside from this ESPN piece from February of this year — but the focus of that article is on a legal issue Martin dealt with over the offseason, and the only comments offered on Martin’s approach seem to indicate that his stance hadn’t actually changed:

Martin also worked with a hitting instructor during the offseason in Miami. He altered his approach at the plate — his stance remains the same, he said — and he was pleased with the results when he faced pitchers in winter ball.

The most significant changes I’ve noticed as a result of comparing film from 2015 to film from 2016 are the aforementioned foot positioning and the fact that his hands are a little bit closer to his body this year. Generally speaking, though, it’s hard to really quantify the connection between a player’s stance and his performance. If this change in stance is deliberate, we can only really speculate as to the reasoning behind it. There are certainly good reasons to make the adjustments Martin has made. Bringing the hands closer to the body is often a nice starting point for a player who wants to make his swing a little more compact and less erratic. As for the foot positioning, there are a few benefits to batting with an open stance, especially for a left-handed hitter. One is that it enables left-handed hitters to see the ball better, especially when facing a left-handed pitcher. Another is that it eliminates the problem of the front foot stepping away from the plate on the swing, as batting from an open stance requires you to bring your front foot towards the plate in order to square up to hit the ball. It’s hard to say if Martin has previously had this issue in the past, but the fact that he’s changed from an open stance to a square stance likely indicates to me that whatever advantage he gained from an open stance may no longer be necessary. We don’t know if Martin has made these adjustments for the reasons listed above or if he has made them for any real reason at all, but he’s still made them all the same, and as it happens, they’ve been working out quite nicely for him.

That said, let’s not go overboard about a quarter-season of statistics just yet. Though Martin is posting career bests in almost any meaningful batting metric, there is still reason to believe he might still turn out to be an average or below-average hitter for the rest of the season. His on-base record is rather inflated by recent performances, he strikes out too much, and he continues to sport uninspiring numbers against left-handed pitching. All the same, his eight home runs this season aren’t going away, even if his fly-ball rate might. It’s unlikely, barring injury, that he’s not going to hit any more home runs for the rest of the year, so 2016 will most likely be a career year for him in the power department, and if his BABIP mark of .302 this year can regress back to his 2013-14 average of .326 rather than his poor 2015 mark of .270, 2016 may turn out to be a career year for him across the board. Martin’s offensive production has certainly been a pleasant surprise for the Mariners, and it would be interesting to know if altering his batting stance was a deliberate factor in producing an improved approach at the plate. If the Leonys Martin we’ve seen so far this year is anything like the Leonys Martin we’re going to see for the rest of the year, Jerry Dipoto may have stumbled upon a surprisingly high return on what was initially a low principal investment.

The Rays are known as one of the most sophisticated organizations in the MLB, mostly thanks to an advanced analytics department. They have been first adopters of some of the now prevalent advanced baseball strategies today. They perennially are winners with annually low payrolls.

The Rays sometimes blow me away with the strategies discovered and implemented by their analytics departments. One of the most fascinating strategies they implemented at the beginning of last year was getting their pitchers to throw fastballs with more rise high in the zone, causing pitchers like Drew Smyly and Matt Moore to make drastic improvements in their results. Now I believe they are having their hitters implement a new strategy.

Swinging hard.

I cannot be positive they are telling their hitters to swing harder, but there is some evidence to lead me to believe this is true.

The Rays strike out a lot. Almost all their players have strikeout rates above their ZiPS and Steamer projections and they currently have one of the highest strikeout rates in baseball. A strikeout is the worst outcome possible for a hitter, so at first glance the Rays appear to have a lot of hitters who have gotten a whole lot worse. It’s clear looking at the data this isn’t just variance. Across the board for the Rays, the contact rates of their hitters have been much worse this season than the previous, an average decline of about 5%.

There are a few possibilities that come to mind that could explain the decreased contact rates. The first one is luck. It is possible most of the Rays contact rates have decreased because of chance alone. This is certainly possible, but also unlikely. The 5% decrease in team contact rate is by far the highest margin in the league.

Because of the degree of the contact rate change, it’s unlikely that the Rays’ worse contact rates are happening purely by chance. That leaves two possibilities in my mind. One possibility is the Rays have advised some or all of their hitters to take more of an uppercut swing. A steeper or uppercut attack angle of the bat theoretically should lead to less contact, so this is a possible explanation. If this were true, we would expect the Rays to have more fly balls from their hitters. And they do. Their fly ball rate is up about 3% from last year

But the increase in fly ball rate is only the sixth highest in the league, and can mostly be accounted for by the addition of extreme fly ball hitter Steve Pearce and the loss of extreme groundball hitter John Jaso. I’m also skeptical that a team would try to drastically change all their players’ swing planes. I can’t rule out this possibility though.

That leaves us with the explanation I believe to be true: The Rays have adopted a grip it and rip it mindset. The Rays currently have the highest ISO in baseball, meaning they hit for power better than every other MLB club, a 30% improvement year to year. They also have a large increase in hard contact percentage across the board, an average of about 5% per player, by far the highest increase in the league.

Hitting for power and hitting the ball hard are not unrelated. With MLB Statcast data, we can now see their is a clear and strong relationship between hitting for the ball well and hitting the ball hard. The harder you swing, the harder the ball will be hit. That is if you make contact at all.

If contact wasn’t an issue, swinging hard would be a no-brainer. But there is a trade-off here. While a home run is the best outcome for a hitter, the strikeout is the worst. If you hit the ball in play, you can advance runners and get on base. With a strikeout, neither of those things will happen.

But is the increased power really worth increased strikeouts? The Royals would beg to differ. They won the World Series last year with historically good contact and strikeout rates. However, no one would argue that hitting was the biggest reason for the Royals success. On the contrary, it was really their bullpen and defense that carried them to a championship.

I can only imagine that the Rays have done the math and have decided: Yes, it’s worth the trade-off. Hitting the ball high and hard is good, and the Rays are doing that better than practically everyone else in the majors. Yes they are getting less contact, but the Rays do not have an abundance of talent in the batting department, so given their results I would have to say this change in approach has been a success.