Last season, the New York Mets ranked in baseball’s top 10 in many offensive categories. They were second in batting average and on-base percentage, eighth in slugging percentage, sixth in OPS, fourth in OPS+, third-best in strikeouts and strikeout rate, sixth in wOBA, and third in wRC+.

How did such a team, returning many of the same batters, regress so drastically in 2023? The Mets are now in the bottom 10 in the league in many of those same offensive categories. Players like Pete Alonso, Francisco Lindor, Jeff McNeil, Starling Marte, Mark Canha, and Daniel Vogelbach, all prominent in the Mets’ 2022 success, are now contributing to their demise. How does so much change in one year?

While it’s hard to definitively answer that question, we’ve heard this song before. In 2021, the Mets fielded a lineup of players who suddenly and drastically underachieved, including Michael Conforto, Dominic Smith, Lindor, McNeil, and James McCann. Conforto, Lindor, and McNeil had several years worth of street cred built up as excellent hitters, and out of nowhere, they all tanked.

Let’s take a look at some of the Mets’ numbers this season and try to tease out what’s going on. Today, we’ll dig into the team’s overall hitting philosophy. Tomorrow, we’ll get into players’ individual struggles.

Swinging Philosophy

The Mets’ offensive philosophy is clearly to be very particular in choosing pitches to swing at. There’s a big push toward getting on base. In fact, Mark Canha recently said he’d like to be the player hit by the most pitches across baseball. That may well be a testimony to what his coaches are telling him.

Here are some of the numbers about the pitches the Mets choose to swing at. They definitely indicate the preference not to swing.

• Chase rate: 26.1% (T-6th)
• Zone swing rate: 64.4% (T-25th)
• Meatball swing rate: 73.6% (26th)

The Mets don’t chase very often, but that seems to be at the expense of swinging at pitches in the zone. The numbers get even worse when you consider meatballs. The exact definition of a meatball varies slightly from Baseball Savant to FanGraphs, but the idea is that it’s a ball with an extremely high expected batting average, average exit velocity, and expected slugging percentage. In other words, it’s exactly what has been referred to as a “meatball” in baseball vernacular throughout the years. The Mets simply do not seem to swing at meatballs. You won’t hit 100% of the balls you don’t swing at. Not swinging at pitches that are meant to be driven makes it harder on a team.

However, let’s compare these numbers to the Mets’ 2021-22 stats to see if there are any trends.

• Chase rate: 2022 – 29.5% (15th); 2021 – 29.7% (T-25th)
• Zone swing rate: 2022 – 68% (T-12th); 2021 – 71.2% (T-3rd)
• Meatball swing rate: 2022 – 76.3% (20th); 2021 – 81.4% (T-2nd)

What’s strange is that although the Mets did swing at a much higher rate of pitches in the zone in 2022, their swing rate in the zone was even higher in 2021. They also swung at meatballs at an extremely high rate in 2021 despite ranking poorly in most offensive categories. It doesn’t appear that the pitches they choose to swing at in and of themselves are causing the disparity.

One thing that is noteworthy, though, is that this clearly indicates the Mets’ shift in offensive philosophy toward being very selective even within the zone. It appears that Eric Chávez and then Jeremy Barnes preached choosing your pitches over just swinging at strikes. Although that philosophy appeared to help in 2022 compared to 2021, the shift even further down this season seems to be hurting the Mets. Perhaps 2022 was a sweet spot in the philosophy: try to be selective, but swing at more pitches in the zone.

Mark Hoffman/Milwaukee Journal Sentinel-USA TODAY Sports

Making Contact

More important than the pitches the Mets choose to swing at is the quality of contact. What’s interesting is that in the last three seasons, the Mets have ranked similarly in hard-hit rate (20th, 21st, and 17th), barrel rate (22nd, 19th, 22nd), fly ball rate (21st, 20th, and 24th), groundball rate (10th-highest, 11th-highest, and 7th-highest) and weak contact rate (eighth-highest, 11th-highest, and 13th-highest). So why were they so successful in 2022 while struggling so much in 2021 and 2023?

This question is particularly perplexing when you consider that the Mets have a far higher average exit velocity (89.4, T-11th, vs. 18th/19th in 2021-22) and solid hit rate (6.5%, T-11th, vs. T-17th and 20th) than they did in the previous two seasons. They’re also making contact in the zone at the fifth-highest rate in baseball.

One number that sticks out, though, is the difference in the Mets’ line drive rates in 2021-23.

• 2021: 23.1% (19th)
• 2022 – 24.2% (9th)
• 2023 – 23.6% (17th)

It does appear that the Mets hit more line drives in 2022, which brought them better results. This season, those hits appear to be turning into popups, as the Mets pop up 6.8% in 2023, which ranks the 14th-highest, while they popped up 6.3% of the time in 2022, which was the sixth-lowest.

Still, could a 0.6% shift from line drives to popups really explain such a drastic shift in offensive output?

Lack of the Shift

It was a known fact that several Mets’ hitters, led by Jeff McNeil, murdered the shift (.423 BABIP against it). The Mets’ team BABIP has decreased from .300 in 2022, which ranked seventh in the league, to .275 in 2023, which ranks 26th. Could the shift limitations have something to do with it? (In 2021, they ranked 16th with a .291 BABIP.)

Consider that the Mets went to the opposite field 25.7% of the time in 2022, the sixth-highest rate in baseball. That kind of hitting is designed to beat the shift. In 2023, though, they haven’t adjusted their hitting style; they are going the opposite way 25.9% of the time, which ranks ninth. Combined with the fact that the Mets have a high groundball rate, perhaps a change in approach toward pulling the ball is warranted now that the shift is not there. This is particularly since the pull side is usually the power side for most hitters.

These are just some of the factors influencing the malaise of the Mets’ bats. Stay tuned for Part 2 tomorrow, where we take a look at specific pitches, other aspects of the problem, and whether there are any answers.

The post The Mets’ Hitting Struggles Ring a Familiar Bell, Pt. 1 appeared first on Metsmerized.

Brad Penner-USA TODAY Sports

What truly is an MVP?

• Is it the player who provided the most overall value to an individual team?
• The player who displayed the most prowess on a playoff-bound club?
• The player who rewrote the history books?
• Or is it the player who simply added the most value to his team’s win total, with respect to the club’s payroll?

For small-market teams, it might be the latter.

“Most Valuable Player” is a convoluted term.

Michael Lewis’ infamous novel Moneyball turned the term on its head. The novel, rather infamously, chronicled the story of the 2002 Oakland Athletics, the team that disrupted the model of how baseball teams are built.

In the book, the duo of Billy Beane and Paul DePodesta, the latter of whom eventually worked for the Mets under Sandy Alderson, applied economic theory to optimize player valuation. Their objective to value a win by analyzing previously-unapplied metrics, resonated with small-market teams with limited funds.

During the 2001-2002 offseason, Jason Giambi and Johnny Damon, two of the Athletics’ most prolific hitters the past season, left the club, pivoting to greener pastures (and more greenbacks) elsewhere.

Despite running a payroll of 85 million dollars less than the Yankees, the 2002 Athletics rolled off a 103-win season, including a historic 20-game win streak, resulting in an AL West division title. Their top-salaried player, Jermaine Dye, earned $7.16 million, the 71st-highest mark among Major League players that season.

Despite a team-wide salary exceeding just over $40 million, the 2022 Athletics paid $388,389 per win, the most efficient mark in the Majors that season.

The experiment of the 2002 Athletics proved to be the foundation of the “sabermetric revolution,” a period of cataclysmic change that has shaped the sport. Instead of purely judging a player on past performance, analysts took into consideration a variety of factors to predict not only what a player is, but what a player could evolve into. This shift in philosophy drove teams to invest in more analytical-friendly infrastructure to support their baseball operations staff, a movement that Mets owner Steve Cohen has supported.

In his introductory press conference 18 years later, Steve Cohen talked about modeling his team after the Los Angeles Dodgers, implying that a continuation of the standard that the Athletics set would continue in Queens.

I’m curious about applying one of the Athletics’ approaches to the 2022 Mets: How much did they pay for a win?

In 2021, the Mets’ payroll sat at $203 million. The club won 77 games, missing the playoffs for the fifth consecutive season. After a frenzied offseason, which was highlighted by the record-breaking signing of Max Scherzer (and notably not Steven Matz), the club’s payroll shot up to $282 million, the highest mark in baseball.

Overall, the team spent $2.8 million per regular-season win. That Million-Per-Win metric ranked third in the Majors, only behind the Philadelphia Phillies and the Boston Red Sox.

It is interesting to note that five of the top 10 teams failed to reach the playoffs in 2022. The Angels, White Sox, Red Sox, and Rangers all had hopes of a playoff berth entering the season. The Rangers, in particular, invested heavily in the 2021-2022 free-agent market. The club spent upwards of half a billion dollars on Corey Seager, Marcus Semien, and Jon Gray, but to no avail. Meanwhile, the Washington Nationals’ post-title euphoria influenced signings during that winter that are tracking to be among the decade’s worst.

Breaking it down to a more granular level, we’re able to see which player contributed the most value, with respect to his salary. To calculate this figure, I compared the player’s WAR with his salary. For players who were acquired midseason or rotated through the Majors and Minors, I adjusted their salary and contributions to account for the time they were on the Major-League roster. Players with a 0.0 WAR or lower were unable to be calculated.

Please note that I used the Adjusted Salary metric on Spotrac to calculate the following:

Fan-favorite Daniel Vogelbach took the crown in this regard, with David Peterson, Jeff McNeil, Colin Holderman, and Luis Guillorme rounding out the top five. Notice that Vogelbach, Peterson, Holdermann, and Guillorme all fall in the pre-arbitration salary category; this jives with the theory that pre-arbitration players are some of the most undervalued assets in sports. We’ll delve more into that later.

Trevor May and his  $77 million per WAR bring up the rear, followed by James McCann, Seth Lugo, Max Scherzer, and Jacob deGrom. May, inked to a two-year, $15.5 million deal during the 2020-2021 offseason, fought through an injury-ravaged campaign, accruing a total of 0.1 fWAR. That’s certainly a detriment to this study; it includes only counting stats, not taking into account any time spent on the IL.

The below visualizes a player’s WAR, compared to his salary.

Although Scherzer and deGrom both rank poorly in this metric, that’s simply the cost of signing free agents. Rarely do free-agent signings reflect an exact quantifiable of the player’s value. For some teams, some players who finished in the red are a luxury that they are unable to afford (Looking at you, Rays).

It should be stated that this analysis is less of a conundrum for the current Mets ownership. As seen with the May 2022 release of Robinson Canó, this ownership group is willing to eat sunk costs in order to produce a better on-the-field product. Max Scherzer isn’t likely to be worth  $43 million a year in a pure million-per-WAR metric, but his on-field temperament, HOF resume, and grandeur showed the Mets were ready to win.

The chart below visualizes each player’s Million-per-WAR, categorizing each player by their contract type. The Mets’ arbitration and pre-arbitration players (color) are concentrated on the left side of the chart.

Jeff McNeil, Brandon Nimmo, and Pete Alonso represent the red data points in the upper left quadrant. At a combined $17.4 million, the trio produced 15.3 WAR, a fantastic mark considering their price point.

Some teams, like the Rays, have eschewed the free-agent market in order to build their roster with players who are still in arbitration and pre-arbitration. And arguably, if payroll is limited, that’s an intelligent move.

While the Rays have received much flak for trading away club leaders as their arbitration number increased, their payroll situation doesn’t give the club much choice, assuming they have a suitable replacement lined up.

The May 2021 trade of Willy Adames fit that billing. The Rays’ clubhouse was reportedly crushed by the move, viewing Adames as a leader among the players. From a financial perspective, Adames was about to enter arbitration. Instead, the Rays opted for similar production at a more efficient price point, from the über-talented Wander Franco.

While the Mets shouldn’t go scorched Earth like the Rays, it stands to reason that growing the team through improving the farm system is the prudent approach. A few of the Mets’ top farmhands, like Francisco Alvarez, Brett Baty, and Mark Vientos, spent time on the Major League roster last season. The team’s future success will hinge on if they can live up to their high expectations. After a strong 2022 draft, the club’s farm system is certainly on the upswing.

Spending through free agency can help complement a strong farm system, but free agency shouldn’t be used as a fill-in to support an underwhelming farm system. All in all, small-market teams are forced to be efficient with their cost-controlled players, but having an owner with deep pockets allows for more flexibility in creating a 26-man roster.

Data via Fangraphs and Spotrac

Visuals Provided By Matt Mancuso: Github link

The post How Much Did the 2022 Mets Pay For A Win? appeared first on Metsmerized.

1. Smart Baseball: The Story Behind the Old Stats That Are Ruining the Game, the New Ones That Are Running It, and the Right Way to Think About Baseball by Keith Law

A fan favorite among the Metsmerized staff is Smart Baseball, written by Keith Law from The Athletic (formerly with ESPN). The book delves deeper into the evolution of sabermetrics in the industry, what it means for the future of baseball and long-standing traditionalists who oppose the methods. The book is a great primer for the average fan who wants to expand his or her knowledge beyond batting average and RBI. Whether you’re a skeptic or a believer in the value of sabermetrics, this could be a great way to learn more about the science behind sabermetrics or refine the knowledge you already have.

2. Infinite Baseball: Notes from a Philosopher at the Ballpark by Alva Noë

I picked this book up while I was in Washington, D.C. for the 2019 World Series and finally started it, now that I have some downtime. If you’re a vehement believer that baseball is an exciting sport, despite what some of your friends or family may think, Infinite Baseball captures the fan experience at its core and the narrative behind every game. As a philosopher, Alva Noë examines the fascination behind baseball and its greater clear-cut lesson of every action needing credit and blame, and even why it’s okay for adults to want nothing more than to catch a ball in the stands. He has some interesting takes, notably his stance on comparing PEDs to Tommy John Surgery, but it is thought-provoking to say the least. The author is also a Mets fan, which explains why he has an entire chapter dedicated to Matt Harvey.

3. Moneyball: The Art of Winning an Unfair Game by Michael Lewis

Moneyball, which was made into many fans’ favorite baseball film (starring Brad Pitt and Jonah Hill), centers around the early-2000s Oakland A’s and their manager, Billy Beane. The book narrates the A’s utilization of sabermetrics to scout low-buy players, all to compete with the likes of richer teams like the New York Yankees. The book calls into question traditionalists in the industry, who are often considered subjective, and the value of sabermetric enthusiasts who are considered to be “playing Moneyball.” Lewis highlights the success of the 2002 A’s in cracking the code and ultimately changing many of the business practices of MLB front offices. The film is also a fan-favorite among the Metsmerized crew, but consider reading the book if you haven’t yet to see how it measures up.

4. The New York Mets All-Time All-Stars by Brian Wright

It’s a no-brainer that a book centered around the Mets deserved a spot on this list. This book notes some of the Mets franchise greats and how they measure up, including Tom Seaver, Jacob deGrom, Gary Carter, and Mike Piazza, to name a few. Wright compiles an all-time, All-Star lineup of the greats, and even assesses management and ownership. With some downtime since the 2020 regular season is delayed, consider reading this to study up on some of the Mets greatest to grace the field. Whether you’re a new fan or you’ve been on the journey since ‘62, there is never a bad time to reminisce and imagine every Mets great in one lineup.

5. Ahead of the Curve: Inside the Baseball Revolution by Brian Kenny

MLB Network’s Brian Kenny offers his own account of the value of sabermetrics and analytical thinking with Ahead of the Curve. While he notes that traditionalists believe in “preserving the game,” Kenny offers insight as to why fans and industry experts alike should embrace the sabermetric era of the sport. Sabermetrics are more widely accepted nowadays, with many thanks to its creator Bill James, but this book offers a greater understanding of the evolution of advanced metrics and the value in going against the grain to gain a better understanding of the game and its players. While it is considered similar to the aforementioned Smart Baseball, this book may be better suited for someone who has a baseline understanding of sabermetrics.

No matter how you’re spending your socially-distanced days, picking up a good book can certainly pass the time as we patiently await the season’s start. What are your favorite baseball books? Tweet us @Metsmerized and let us know!

The post Good Reads: 5 Baseball Books To Get Through Social Distancing appeared first on Metsmerized.

Back in 2004, the New York Mets hired their first full-time statistical analyst after the release of the New York Times bestselling book, Moneyball. 

The Mets were looking to invest in someone who could break down the numbers, analyze data for potential free agent signings and trades and offer evaluations on minor league players.

What the club received from Dr. Ben Baumer was even more than they could’ve expected.

Baumer, 41, realized that the best way to store stats and better visualize the data that the club needed was to devise an internal server. He then went about setting up the team’s internal infrastructure, or their statistical portal. Baumer admits that he self-imposed that undertaking but understood that it would ultimately make his job easier in the long run.

Hired by former Mets General Manager Jim Duquette, Baumer worked mainly under Omar Minaya and Sandy Alderson during his tenure with the club. Baumer counseled the GM in various aspects of player development and potential transactions. He worked with Mets coaches on providing data and analysis for specific research, including in-depth work with former Mets pitching coach Rick Peterson.

While working for the Mets, Baumer went to graduate school and earned his Ph.D. in mathematics at the Graduate Center of the City University of New York. In the summer of 2012, Baumer decided to leave the Mets in pursuit of a career in academia.

He started teaching at Smith College, located in Northampton, Massachusetts, and is now an assistant professor of Statistical & Data Sciences. The SDS major was created in 2016, and according to The Sophian – Smith’s independent newspaper – it’s the first women’s college to offer a data science major.

Along with teaching courses at Smith, Baumer has kept busy, co-authoring a book with economist Andrew Zimbalist released in 2014 called The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball. In it, the pair debunks several misconceptions about Michael Lewis’ Moneyball, explores how the game has evolved with the use of sabermetrics and highlights the effectiveness of the metrics.

Along with the book, Baumer and two colleagues – Shane T. Jensen and Gregory J. Matthews – won the Contemporary Baseball Analysis Award from the Society for American Baseball Research (SABR) in 2016 for their paper on openWAR, which is based on public data that offers greater transparency than other WAR metrics from sites like FanGraphs and Baseball Prospectus.

I had the pleasure of speaking with Dr. Baumer about his work in the Mets front office, his relationship with scouts and coaches and what advice he’d give to young people looking to break into the data science industry.

MMO: You started working for the Mets in 2004 as a statistical analyst. Can you talk a bit about how you came to secure that position?

Baumer: Things were very different back then. I was very lucky to get that job; it was very much being a person in the right place at the right time. This was right after Moneyball had come out, and I got word through a family friend that Jim Duquette was looking to hire someone to do statistical analysis. I sent in a resume and a couple of months later I had an interview. I had a good meeting with Jim and a couple of other people in the front office.

I ended up doing a couple of projects for the Mets over the last few months of 2003, and then I got the call that they were going to hire me in December of 2003. I started [in] January of 2004.

MMO: Were you always interested in data science and analyzing numbers, particularly in baseball? Were you a reader of Bill James’ work growing up?

Baumer: Yeah, I played baseball, and I paid attention to the numbers from my collective baseball cards, and I read some of Bill James’ stuff.

At that time, data science wasn’t a term that anybody used, and even sabermetrics was not a term that I was familiar with. I’d say it was more than my brain was always thinking like that, but there wasn’t a field to latch onto, so it wasn’t a conscious kind of position in that sense.

I was interested in math, and I ended up going to grad school in math before I even found out about this opportunity or even read Moneyball and realized that this was a career path.

MMO: Upon being hired, did the Mets give you a lot of freedom in terms of what data to analyze, or, did they lay out a specific course of action for your analysis?

Baumer: It was very free in the sense that there wasn’t really anyone who had that position before. I kind of got the sense that they knew they wanted to be doing more with what we would call baseball analytics, but they didn’t really have a firm sense of what that was.

When I got there, there were no computers – infrastructure – for baseball analytics. There were no databases and no server. I started building out some of that infrastructure as I got more familiar with the kinds of things that Jim wanted from me. Jim would come to me with questions like, Here’s this list of free-agent relief pitchers that are available. What do you think of these guys? Can you analyze them?

In order to do that work, I needed data to analyze, and so I set about building that infrastructure.

Photo Credit: SNY

MMO:  I spoke with your former colleague, Adam Fisher, who said that you built version one of the Mets’ statistical portal. He mentioned that you built it without any formal training and taught yourself to code. Is that right?

Baumer: Yeah, that’s mostly right. I had taken two computer science classes in college and had worked -before I went to UCSD (University of California San Diego) for a master’s before working for the Mets – for a company and that was my first experience building dynamically generated web pages.

It was a different technology than the one that we used with the Mets, and I only had a little bit of experience with it. But it was a taste and enough that I knew it could be done, and what Adam said was basically true. I had never used PHP or MySQL before, but I knew that they could work together, and I just kind of sat down and got to work on it.

MMO: Can you describe what your day-to-day work life was with the Mets?

Baumer: That changed throughout the years, but the way I like to think about this job was there were kind of two parts to it. The sexier part is advising the general manager on player evaluations: free agent signings, trades, minor league players, etc. There was that aspect of it. There were a lot of other people doing that like Adam [Fisher], and all the scouts were involved in that process.

The other part of the job – which was sort of more mundane – was the building up of the database and having the web front-end; there was a lot of work that went into that. That probably took up more than fifty percent of my time, and that was, I would say, largely self-imposed in the sense that nobody told me to do that. I just decided to do that because I knew that it was the best way to do my job more efficiently and help other people do their jobs more efficiently.

MMO: How often would you interact with the players and coaches in disseminating the information you analyzed?

Baumer: I didn’t interact much with the players, but I did interact with the coaches on a regular basis. The philosophy at the time was very much that it’s the manager’s and coaches’ jobs to translate the information from the front office to the players. They really didn’t want front office people talking to the players directly because there is such a perceived gap in experience, knowledge and context.

A little of that has changed; you see more and more teams – including the Mets – having this sort of embedded data analyst in the clubhouse, looking at video and talking to players. It was not that way for me when I was there.

MMO: I know you worked closely with then Mets pitching coach Rick Peterson, who was a big proponent of analytics. Were there specific numbers he asked for on a consistent basis that you could share?

Baumer: There was. I probably shouldn’t go into too many specifics about that, but Rick was someone who came in and knew exactly what he wanted to know. He told me what data points he wanted to see, and I helped produce reports that would give him that information. Then we would work together on those reports.

He was interested in things like what were players’ batting averages in different locations in the strike zone, and stuff like that.

MMO: During your tenure with the Mets, how would you describe the club’s reliance on analytics and how it evolved over the years?

Baumer: I think by the end of 2004 – which was my first year there and Adam’s first year as a full-time person – he and I got more and more into the conversations that Jim [Duquette] was having. By the end of that year, we started to feel like we were really integrated into the decision-making process.

When Omar [Minaya] came on, I think we were both worried that it was going to end because there was stuff out there in the public sphere about Omar sort of being dismissive of analytics. And that absolutely did not happen.

I remember having conversations with Omar on his first day in the office. From day one, he just made it clear to everyone involved that analytics was going to be a part of his decision-making process and that Adam and I and other people that were providing that kind of perspective were important and were going to be listened to.

I would say our influence only increased through the Omar years, and then with Sandy [Alderson], it was more obvious that he was interested in that kind of stuff. So we continued to be involved.

MMO: That’s something I’ve heard from several front office people over the years: that Minaya wasn’t just devoted to the player development and scouting aspects; he wanted the analytic information as well to make his final judgment.

Baumer: Yeah, I think it’s really a difference in the backgrounds, right? Omar’s background is in scouting and player development, and at the end of the day, whatever decisions he makes are going to be heavily predicated on his own perception of the player. Omar wasn’t doing analytics, but he recognized the value of analytics.

The other thing about Omar’s decision-making process is that he would listen to anyone, and he always wanted to gather everyone’s opinion and then make his decision. The analytical point of view was always part of that decision-making process. He was very good, I thought, at soliciting that point of view and all that.

With Sandy, it was very different because Sandy does not have a player development background or scouting background. For Sandy, his decision-making process is much more inherently analytic in itself. I think for him he sort of understood the analytics’ arguments in a much deeper way, but that doesn’t necessarily mean that it had a heavier influence on his ultimate decision-making.

MMO: Early on in your tenure, were the specific stats or metrics that you would lean more heavily on than others?

Baumer: I don’t want to get too specific but we did not focus on a single statistic. Actually, part of what I would say I was trying to achieve was a greater knowledge of sabermetrics in general for everyone in the front office.

To me, it seemed much more important that people sort of understood the basic arguments that sabermetrics was making as opposed to having this proprietary statistic and wanting to show how great it was so we can all believe in it and worship it.

The insight about batting average on balls in play (BABIP) and pitchers not being able to control that as much as people thought, that was an idea that at that time we talked about a lot. And again, part of what Adam and I were trying to get through is just to help everybody understand that idea and let it permeate our thinking about players in a much more general way as opposed to a very specific way.

MMO: After eight years with the Mets, you moved into academia at Smith College in Northampton, Massachusetts. Was that a move you had thought about making for quite some time?

Baumer: Not really. Teaching is something I’ve always thought about, but it wasn’t the plan. Obviously, I was in graduate school for a long time while I was working for the Mets, so it was a potential outcome for sure. But it wasn’t something that I decided that I needed to do.

By that time I got close to finishing my Ph.D. in 2012, my circumstances were different. I had been doing things with the Mets for a long time, and I needed to explore what was out there and this job at Smith College turned out to be a great fit.

MMO: Would you ever consider returning to a major league front office down the line?

Baumer: Yes. I’m coming up for tenure next year, so I’ve got to take care of that first, but it’s something that I would consider. From where I am personally in my family life, I think it would be very hard for me to leave this area, but in the right situation, I would certainly consider it. Certainly on a short-term basis, if not a permanent position.

MMO: You and two of your colleagues developed the concept of openWAR. Can you talk a bit about what made you interested in further investigating the current WAR models out there from Baseball Prospectus, FanGraphs and Baseball-Reference, and how openWAR differs in that regard?

Baumer: WAR had been gaining currency as an idea to go back to our previous conversation about a single statistic. When I sat down to think about it, I just got increasingly frustrated at how unclear it all was.

You had all these different organizations with their own implementation of it, and yes, there are descriptions of those implementations online, but nobody has released the source code or anything like that. It really wasn’t something you could verify that everything was working as it was supposed to. I think for most people, if you’re just consuming the numbers, that’s fine; you might not care about that. For me, it was like, I’m not going to trust this until I’ve really understood how exactly these numbers are being computed.

That was sort of the impetus for that project of just being frustrated at the current state of affairs. I worked on that with Greg Matthews and Shane Jensen, and what we were trying to do was not so much create a better version of WAR as much as creating one that was fully transparent and that everyone could agree. You don’t have to like it, but we could all agree on what it is and how the numbers are computed.

That was really the goal, and as we went through that process, there were a couple of things that we thought made more sense to do in a different way.

The way that we handled replacement players is different and the way that we do some of the fielding stuff is different, but at the end of the day, you should get more or less similar numbers. It wasn’t really about saying that the numbers that were out there were really far off and we need to make them correct. Again, it was just more about making it all transparent.

MMO: You and your colleagues released the paper on openWAR, and users can go online and play around with the numbers via an R software program. Where do you envision openWAR going in the public sphere? Are you and your colleagues working on any other open-source metrics? Did you hope that it would’ve taken off in the same way that Baseball Reference’s bWAR and FanGraphs’ fWAR are utilized?

Baumer: I sort of did hope for that. [Laughs.] But that hasn’t really happened. And that’s okay. Greg and I have talked about revisiting that and coming out with a 2.0 version and trying to iron out some of the things that some people were critical of in the first version.

We’re teachers, and I don’t have the time or the platform to set it up the way that they have it on FanGraphs or Baseball Prospectus. The website that I built for the Mets is not unlike FanGraphs or Baseball Prospectus, but that’s not where I’m at right now in my career.

I do think though that we did achieve some success in getting the public to talk about the specific version of WAR that they’re referencing more often. It used to be that when you read ESPN or the New York Times and they talked about WAR, it was just WAR. I do think that more people are referencing bWAR or fWAR but they’re referencing the specific source, and I think that’s a consolation prize.

MMO: You co-authored a book with Andrew Zimbalist called The Sabermetric Revolution back in 2014. For those who haven’t had a chance to read it yet, can you talk a little about what readers should expect to learn from this book?

Baumer: The original title of the book was Moneyball Revisited, and the concept was ten years after Moneyball, and we wanted to do a retrospective on not just Moneyball itself but how the game has changed and how these ideas have changed. We weren’t able to use that title due to the publisher having some legal concerns, but that’s what the book is about; it’s sort of this retrospective on how the sabermetric industry has changed both inside and outside Major League Baseball.

One of the things that I liked that we were able to do was actually try to measure whether we think analytics has worked or not. That is, through the teams who I perceived as being more analytically driven and to win more. I won’t say that we answered that question, but we addressed that question, and the short answer was yes; it did seem that teams were more successful.

MMO: During your time in the front office with the Mets, was there a certain aspect of the game – defense, pitching, hitting, running – that was harder for you to asses? Clearly public defensive metrics have been improving with the advent of Statcast. Would you assert that defensive metrics were the most challenging for you?

Baumer: Certainly defense would be one of those things. Catcher framing is another one that, in 2004, we had no way to measure. And it wasn’t that we didn’t think of the idea, scouts and player development people were talking about that idea. We just didn’t have the data to measure it, but that changed.

I think the biggest thing there was in 2004, we had play-by-play data that we purchased from Stats Inc. that had locations of pitches in the strike zone, but they were eyeballed by people watching the game on TV. We also had locations of where the balls landed in the field, but they were also eyeballed by people watching on TV.

To go from that to what they have now is just a big change in terms of the quality in the data.

MMO: One of the myths you debunk in the book you co-wrote with Zimbalist was the idea that the scouts and data analysts clashed. How would you describe your relationship and dealings with scouts during your time with the Mets?

Baumer: Overwhelmingly positive. In the very early days, there were a couple of people and a couple of moments where scouts weren’t that interested in what I had to say. But again, I think Omar made it very clear when he came on that that wasn’t going to be a winning philosophy.

I had a really good relationship with scouts. Al Goldis and Bill Lindsey, when they were there, would talk to us for hours, and I learned a lot of baseball from them. I went on the road with Brian Lamb a bunch of times and learned a ton of things from him on scouting and other things. Overwhelmingly positive were my relationships with scouts.

MMO: For those interested in pursuing a career in data science – specifically for baseball- what would you recommend they do in order to put themselves in the best position to succeed in that field?

Baumer: Publish. To that end, I have a book out with Jim Albert and Max Marchi, it’s the second edition of Analyzing Baseball Data with R. I wasn’t a part of the first edition, but was with the second and the book is a sort of tutorial on how to use R to access baseball data and to analyze it. It covers many of the key concepts of sabermetrics.

To go back to where we started this conversation, things are very different now than they were back in 2004. The good news is there are a lot more teams that have a lot more jobs. The bad news is there are a lot more people paying attention to that, and the competition is probably even tighter to get those jobs. The expectations are higher in terms of what type of degrees and skill set they expect you to have.

I think for younger people who are trying to get into this, if you want to be an analyst, you have to be able to code, and that can mean R, Python, SQL, or some combination of all three. But you have to be able to do that in order to do the work, and I think the best way to get that work noticed is to try to publish it somewhere online. Any one of these sites, whether you have a class project or something that you’re working on independently, if you can get that out there to get people’s eyes on it, then it gives you something to talk about in an interview that shows people what you can do.

MMO: I can’t thank you enough for taking the time to speak with me today, Dr. Baumer. Thank you for sharing your insights.

Baumer: I appreciate that. Thank you.

Follow Dr. Baumer on Twitter, @BaumerBen

The post MMO Exclusive: Former Statistical Analyst for the Mets, Dr. Ben Baumer appeared first on Metsmerized.

The analytic movement in Major League Baseball continues to gain further acceptance, as all thirty clubs employ statisticians and analysts to crunch the numbers in order to make better baseball decisions.

We’ve witnessed teams alter their methods with the increased amount of data available at their fingertips, which includes defensive alignments, bunting, platoons, and utilizing their best relievers in the highest leverage situations, just to name a few.

Services such as Statcast, Sports Info Solutions, Baseball Prospectus, and FanGraphs offer various insights into sabermetrics, helping to further the conversation and bring awareness into understanding the game in a new light.

Over the last few years, fans of baseball literature have been given various perspectives on the shift in baseball analytics, from Brian Kenny’s book Ahead of the Curve to Keith Law’s Smart Baseball. Each of these titles offers its own insight and analytical approach to baseball-related data; however, a simple yet often afterthought when trying to understand the logic behind sabermetrics is the human element aspect.

Author Russell A. Carleton looks to bridge that gap.

Carleton, a veteran baseball writer whose work has appeared in Baseball Prospectus for over eight years, penned a new book called The Shift: The Next Evolution in Baseball Thinking. In it, Carleton brings his background in clinical psychology paired with his keen knowledge of analytics to pose questions about various in-game scenarios along with a long-term outlook.

Why didn’t David Ortiz bunt down the third baseline with a major pull shift in the infield? Why don’t third-base coaches send more runners home on a sacrifice fly? How can we better evaluate the role managers play? What’s the expected value of a particular situation to make better in-game decisions? How do organizations go about ensuring their minor league players are given resources in case they need assistance in personal matters?

These are just a few of the questions Carleton poses in his book, along with detailed analysis and data to back up his claims and findings.

Carleton writes in an engaging and exuberant way, with many of the chapters filled with personal anecdotes from Carleton’s own life, which helps tie into his studies. This approach helps make Carleton’s arguments clearer for the uninitiated or fans that are looking to gain a better understanding of sabermetrics.

With the amount of information available to the masses, it’s important that we’re able to discern how to ask the right questions and make the best decisions for maximizing the most wins for a team. Carleton offers insightful analysis into making better baseball decisions but with the human element attached.

I had the privilege of speaking with Carleton in mid-March, where we discussed the development of his book, how he incorporated his clinical psychology background into his work and several of his chapters.

MMO: What made you write the book, and what was the process like for you?

Carleton: After the 2016 World Series, it was November, and I was depressed because my beloved Indians had lost the Series. November is National Novel Writing Month, and I said, ‘I’ll never write a novel, but I’ve always had the idea of writing a book.’ I even tried about ten years ago, and I found some of the old drafts, and they were awful. I decided that I’ve always had this dream, and if not now, then when?

I’ve been doing baseball writing for about ten years at this point, so I had a lot of stuff that I had been writing about and my rough drafts. The process was kind of tying a lot of that stuff together that had been kind of scattershot over the years.

MMO: One of the big stigmas you address right away in your book is about the ongoing acceptance, or lack thereof, by some of the sabermetrics. One of the common objections, and one that you expand upon, is the “you never played” statement. Can you talk a little about how you counter with those who are skeptical about advanced metrics, including this particular objection?

Carleton: My secret superpower is that I’m trained as a clinical psychologist. I have been in the therapy room, and I have talked to people, trying to help them out. Occasionally, you get people who say, “You haven’t lived my life; you don’t understand.” Well, I don’t in the sense that I’ve never done it myself, but we all have to do things where we’ve kind of learned to take somebody else’s perspective in life. So I had some training in that.

Somebody might say, “You’ve never played.” What they’re really saying is that they can’t even put it into words what they understand about baseball that you’re just not picking up on. I’ve gotten that before, and I’ll say, ‘Well, help me understand.’ You give them a couple of minutes to really formulate the words, and they bring up something, and they say, “Here’s what I’m trying to say.”

Usually, they have a pretty good point, and I’ve had those situations, and I think it calls for a little bit of humility around that.

It’s not that I have all of the answers or that someone else has all of the answers. It’s something in the therapy room you kind of learn to do, and when you do it, you’re a much better person for trying to help people out and piece out what’s really going on.

MMO: You come from a background in clinical psychology, as you mentioned, and one of the things I loved about your book was how you interspersed personal anecdotes and stories. Did you plan from the beginning to write the book that way?

Carleton: Self-disclosure in the therapy room is something that I always tried to integrate into my work. I don’t see patients anymore, but when I did, it is a way of being able to say, ‘Look, I may not have gone through something exactly like this, but I’ve been through something myself.’

I talk about that in the book when I was in high school and I was on a quiz show. There was a team that kind of bent the rules and found an advantage. Basically, we lost because they figured it out before we did. I talk about that, and part of it was just, well, I’m never going to write a memoir, so it’s a chance for me to do that.

I tried to pick stories from my life that demonstrated, okay, this is what I’m talking about and kind of serve as a way to get people ready for the baseball stuff that was going to happen. Plus, hopefully, it was kind of fun to read.

The idea of self-disclosure was that I’m a real person and not some monster out to destroy the game of baseball. I’m just a guy that’s been through my [own] life, and all I can say is these are my observations. Maybe it’s a lot easier to take when it’s just another person on the other side of the keyboard.

MMO: I think a big part of the appeal to your book is going to be how you intertwined both the analytics and the human element side of it. By doing so, the numbers don’t appear as “scary” to those who are either on the fence with sabermetrics or are wanting to get a better understanding of it.

Carleton: And this is part of why I wanted to write the book. The stereotype is you’re just interested in numbers; you’re not interested in the human element of it. Well, as a psychologist, that’s what I do. When I was pitching the book to my publisher, I said, ‘I don’t know if this is even a baseball book as much as it’s a book about people that just happen to be wearing baseball hats.’

This is a book where I think if you dig deeper into the numbers, you’ll find that baseball has a lot of places where very, very human things happen. Some of them are kind of silly human things, and some of them are places where yeah, we can play the game of baseball better.

I think that marriage of the human element and the numbers, especially within the game of baseball, has gotten a bad rap. It was one of the things I was consciously shooting for as I was writing it.

MMO: You write extensively about expected value and probabilistic thinking. Can you talk a bit about expected value and what fans should understand about that concept when it comes to making sound baseball decisions?

Carleton: One of the things about expected value is that we’re not trained to think about that in sports. If you think about the example that I use [in the book], if I know that there’s nobody on and two outs or the bases are loaded and nobody out, before I know what happens in those two innings, I know which one I would pick if I had a choice. I think it’s pretty obvious which one you’d pick. But sometimes the bases are loaded and nobody’s out, and you don’t score. Sometimes there’s nobody on and two outs, and you put together a four-run rally. It doesn’t make my initial preference for bases loaded and no one out false; it’s just that sometimes you just kind of get a bad outcome.

What you want to think about is what is the most likely thing to happen. What is the value of that? In baseball, value is denominated in runs. What is going to produce the most runs or wins or whatever it happens to be for me? The thing about expected value is that it describes a lot of the decisions people have to make in baseball. We have to make a decision before we know what the outcome will be. Whether that’s in an in-game decision of should I bunt here? Should I not? A general manager’s decision of should I trade for that starting pitcher and give up my top two prospects?

Some of those decisions that happen a lot in baseball, I think expected value is the key to really understanding how you should approach those, because you’re going to get burned on some of them. Some of them end up being wrong, but if you make the decisions that have a higher expected value time after time after time, you’re eventually going to end up ahead.

Even something as simple, and this one I can’t take credit for, but the sacrifice bunts that we’ve grown up and been taught about. Look at how many runs a team scores on average with a runner on first and nobody out versus the number of runs a team scores with a runner on second and one out. When you think about it, you can either swing away with that guy on first or bunt him to second and accept the out.

Well, it turns out that you actually score more runs with a runner on first and nobody out, so you might as well swing away. That was one of the first applications in expected value to baseball, and this was done thirty years ago by two guys named Pete Palmer and John Thorn. They found that, and ever since it’s been, why do we still see bunting if what you’re effectively doing is taking runs off the board for yourself in the long run?

MMO: In one of your chapters, you discuss the value that a guy like Ben Zobrist brings to a team the ability to play multiple positions. Can you talk a little about the value a guy like that brings to a team, and why versatility should be something that’s more readily looked for? For the Mets, a guy like Phil Evans comes to mind, who they are also teaching to catch. Should clubs be looking more towards this model of developing multi-positional players?

Carleton: Whether they like it or not, they’re starting to. You’re seeing more of those guys. I called Ben Zobrist the patron saint of those sorts of guys who just kind of zoom all over the field. It’s happening for a couple of reasons. One is that pitching staffs are getting bigger, so you need guys who are position players either on your bench or, if they’re starting, they can switch to another position. Then you have someone from the bench fill in, based on needing to cover eight different positions, possibly with three bench players. There’s that aspect of it.

The Zobrist effect is something that I look at, and I’m like, what is the effect of that versatility? It’s not something that Zobrist himself is doing, but if you think about it, he mostly played second base and right field. Now, whatever team, the Cubs, that employs him can go out and say, maybe we have a second baseman who hits right-handed who would be a good platoon guy, and we have a right fielder who is left-handed who has got good platoon splits. Now we can have a position platoon, and Zobrist can sort of bridge that gap. He can do that.

We don’t have a word for that sort of value in baseball. The way that his versatility unlocks their ability to make other decisions on their roster to make the team maybe a little bit better or maybe a lot better. That ability to have that versatility and how it impacts a team is one of those things when I look at baseball I’m looking for what’s something interesting to look for or write about. I look for places where we don’t have a word to describe what’s being said. I talk about that a little bit in the book, too. What are the missing words?

It’s frustrating when you don’t have a word for something. There’s this thing that I know is true or I know is out there, but I don’t know what to call it. I think some of that frustration bleeds through, and sometimes in sabermetrics you’re dealing with stuff that in baseball we haven’t really named yet because we haven’t really thought all the way through with it. Being in that sort of space, where you don’t have the word to say, can feel kind of strange. Some people kind of shut down at that point.

MMO: You dig into the role of closers and how managers should use their best relievers in the situations where the game is most important to winning the game. We’ve started to see managers utilize relievers earlier, such as with the Cubs, Indians, Yankees, and Astros in recent years.

You write about leverage in the book, and the numbers back up your ideas. Do you think we’ll start to see a more widespread approach where managers are using their best relievers in the highest leverage situations moving forward?

Carleton: I want to be kind of careful in how I say this. Yes, but not quite. In 2016, there was the Andrew Miller year, where it seemed like Miller was coming in the sixth inning of all of the games in the Indians’ playoff run. He was pitching high-leverage places and Terry Francona had no concerns about using him wherever he had to use him. I don’t think that is going to happen in the form that the Indians did in the postseason; that was a post-seasonal thing. And it’s because you’re going to burn the guy out if you do that over 162 games.

Teams are starting to get a little bit smarter in that we’re kind of trained to see the ninth inning as the thing that we should look at. What is the thing that tells us how winnable, losable, or basically how much in the balance this game hangs? When you look, it’s not how late the game is; it’s what the score is. We think about the ninth inning with a three-run lead, teams bring in their closer, usually their best reliever, to hold that down. Well, if you look at the math at that it is actually more important to see who is pitching in the sixth inning if your team has a one-run lead.

The two most important runs in baseball are the run that ties the game and the run that unties the game. If you’re up by a run, you’re in grave danger of giving up both. You want your best relievers in when the game is close. The save rule was kind of written in a way that has come to be, oh well, the ninth inning with less than three runs, the guy gets a save. It really isn’t describing the best way to win a ball game. It’s describing a late game that may feel a little more emotionally valiant, but it’s not the best way to win a game if you look at the numbers.

MMO: There’s been a debate for years now on how much value a manager has in terms of wins and losses each season for his club. You surmise that the behind-the-scenes work might be just as important, if not more, than the on-field tactics for the role of a manager. When fans assess the job a manager does, what should they be looking for, based on your research from the book?

Carleton: What we normally think about managers is in terms of the buttons they push. That’s who they pinch hit for when they call for the hit and run, when they call for a bunt, and how they arrange the lineup. We can look and see how much difference that makes. And you can go, well, okay, this is worth a couple of runs here and a couple of runs there. Over the course of a season, I think if you look at the perfect manager and you took all the types of strategic things that he can do and you say he did them all perfectly, he’d be worth about a win and a half, or something like that.

I found if you take a look over the course of a season when we see plate discipline, we see that it kind of deteriorates a little bit. It’s not something you pick up with the naked eye; it’s something that you need a big data set to look at. If you look at how plate discipline deteriorates over the course of a season, you see that some managers are actually better than others, and it’s a pretty consistent effect in either stopping that decline or even holding the line or making their players a little bit better as the season goes on. With some managers, their guys just seem to drop out.

I was able to isolate that, and a guy like Buck Showalter of the Orioles, who sometimes gets a bad rap from sabermetricians for his tactical stuff, is actually the best in baseball at preventing his players from kind of losing stuff to the grind of the season. He came out high-flying on my calculations.

It turns out if you look at the spread between the best and the worst, again, it’s probably worth a win and a half, and it’s pretty consistent. I would say that some of that off-field stuff where you’re keeping guys motivated, even though it’s August and it’s hot and you’ve been living out of a suitcase for four months, if you’re able to do that and keep the guys engaged and not kind of wandering off mentally, that’s most of what a manager does.

I think it’s something that we need to think about more and that sabermetricians have given a lot of short tiff to. When you look deep at the numbers, yeah, it’s in there.

MMO: One of the personal stories I loved was when you talked about the admissions worker you met with at Kenyon College (Carleton’s alma mater) named Mr. Jed. Can you talk a bit about your meeting with him, and who he ended up becoming?

Carleton: Mr. Jed was a really nice guy. I was a senior in high school, and I was living in Cleveland. My dad and I would go every weekend, and we would go out to a college and take a look around. I went to Kenyon, which eventually became my alma mater. It was on a Saturday, and my dad called ahead. They had an admissions worker, and it was Mr. Jed. He showed us around campus, and he showed us the bookstore, academic buildings, library, all of the things you do on a college tour.

We went back to the admissions office, and we had a little interview with me. During the interview, we started talking about baseball. In a moment of self-disclosure on his part, he said that he was really hoping that at some point he could work for a baseball team. I thought that’s really cool; I kind of have that same dream. I was just thinking this is the nicest guy named Jed that I had ever met.

About 10-12 years later I was reading a story, and it was that the Cubs had hired Jed Hoyer to be their new general manager. I was flipping through and reading it, and it mentioned Hoyer’s first job out of college was as an admissions worker at a school in the middle of nowhere Ohio. I kind of went, huh? Huh? Oh! I started doing the math and going, well, let’s see, he graduated there; yeah, that would’ve been about right and then I went to Google. And sure enough, it was Jed Hoyer.

I met Jed Hoyer when I was seventeen; he was twenty-three and he was an admissions worker, and we bonded over the fact that we both wanted to work for baseball teams.

Jed, if you’re out there, I’m happy you got your wish.

MMO: Did you ever get to meet Hoyer and talk to him about your initial encounter?

Carleton: You know, I haven’t talked to him directly. We have a couple of contacts in common, and a few of them have said that they knew the story and have told him that, and he sort of kind of remembers me [Laughs.] I don’t know if that’s true or not, but I hope it’s true.

Jed, if you’re out there, I’d love to sit down and talk about the good ‘ole days way back when, 20 years ago.

MMO: You write about player development, but more specifically, the human development aspect of it. You write that you reached out to several clubs in 2014 on how they approach human development issues. Can you talk about some of your findings?

Carleton: It was a totally different way of doing research. I specifically wanted to not go do a big data dive into ten million lines of data, [where] I’m going to punch through and code and try to find some numbers that explain everything. I decided to pull from my clinical psych background and instead call some of these people and talk to them and see what comes up. I had a couple of questions that I would ask everybody, and I would just let the conversation wander.

You’ve got these minor league players, and they are incredibly valuable because of the way the salary structure is set up that you don’t have to pay them a whole lot. If they’re any good, that’s a great source of value. Everybody knows that. You’re always looking for young talent, so how do you deal with him, not only as a baseball player, but as a human being? Realistically, once these guys take off their hats, they’re just kids that are nineteen, twenty, twenty-one, wondering, Why am I in Iowa?

Some of them are in a new country, some of them are on their own for the first time, some of them are probably homesick. Then you think about issues like, what if he doesn’t know how to do his laundry? What if he doesn’t know how to put together a good meal plan? What if he doesn’t know how to cook?

These types of things you have to do when you’re an adult, and things that all of us we’re learning around those times between 18-25, there’s professional literature around those times. It’s being recognized as a distinct period of time in one’s life called emerging adulthood. How do you help somebody through that time period?

I called a bunch of teams and I said, ‘Well, what do you guys do?’ They were all familiar with the problem, and they had different ways of doing it, some of it the same. The idea was that you really want to build connective tissue so that if someone has a problem, it can be identified and diagnosed. If the person’s reaching out for help he can be connected for that help if they need to stage some sort of intervention or something. You have a way of having a person that the player trusts be the one to reach out and say, “Hey, we’ve been noticing this about you; can we get you some help?”

They all have slightly different ways of doing it, and that’s okay. The point isn’t exactly how you structure it, and it’s not if you do it this way it’s worth 20 extra runs. It’s that you want it to build strong connective tissue, strong relationships between the players and the people who could help them.

I went to them and I said, ‘You know, you guys are sounding like a social service organization.’ If you think about community health and community health work – which is my job now – you want people who are outreach workers, you want people who are diagnosticians, you want people who can give good technical advice, but you have to build that relationship with the community in order to connect people to help.

We’re talking about twenty-something-year-old men; they’re not the kinds who are going to put up their hand and go, I need help, I need help. Our culture doesn’t validate that. In fact, our culture tells us the exact opposite.

That’s one of those things that I found was just an amazing line of inquiry, so I wanted to write that up and put that in the book for everybody to see.

MMO: Overall, what are you hoping readers take away from this book?

Carleton: I had this argument with my publisher that I didn’t want the subtitle to include, “And How to Think About Baseball”. I didn’t want that. I wanted it to be, here are my thoughts: some of them are brilliant; okay, maybe only one or two of them are brilliant, and some of them are going to be lame and some of them you may already think like that. I just want people to have a different way of looking at a baseball game. [It] might not be the same way you look at a baseball game; it might not be the same way you’ve always thought of a baseball game, but here’s just a new thing to try.

I’m a numbers guy; I look at things through the numbers. I think there is a lot of wisdom to be gathered from them, but I know not everyone’s there at that point where they’re fully comfortable with that. Okay, cool. I really just hope people are willing to have a different way of looking at this. Maybe one or two of those things you can take into your own self. As you’re watching the game, you can think of, now that we’re getting into this situation, I kind of wonder if this applies here. That’s really all I’m hoping for in this book. Hopefully it’s a fun read on the beach or wherever you happen to be.

That’s my goal: to give somebody a different way that you may not have had to ever think about the game.

MMO: Thanks so much for your time tonight, Mr. Carleton. Congratulations on the book.

Carleton: Thank you so much.

Follow Russell A. Carleton on Twitter, @pizzacutter4

To order a copy of, “The Shift”, Click Here.

The post MMO Exclusive: Russell A. Carleton, Author of “The Shift” appeared first on Metsmerized.

Mark DeRosa was reportedly the sixth candidate for the Mets managerial position, but ultimately lost out on the job to Mickey Callaway.

DeRosa, 42, played 16 years in the majors and currently works as a personality on MLB Network’s MLB Central. He is also a regular on Mike Francesa’s show on WFAN. While he has no prior coaching experience, DeRosa still felt disappointed he missed out on the job, though he understood.

“I was humbled by the opportunity,” DeRosa said. “They reached out about two weeks ago and flew down to Atlanta. I met with (general manager) Sandy (Alderson) and his staff and really enjoyed the time we spent together and talked it through.”

One of his regrets was not laying out a bigger plan for what he would do as Mets manager, DeRosa told Francesa, but said that Alderson offered him so quality feedback.

“Sandy was amazing,” DeRosa said. “I spoke to him at length when he called and told me that Mickey Callaway was going to get the job. And I was honest with him, I said, ‘Hey, anything I could’ve done differently?’ and we went into it. He said as such, ‘You’re not going to win these jobs on just charisma and class.’”

DeRosa pondered what he thought they were looking for, which he believes he will have to practice if he ultimately manages in the future.

“I thought they were looking for someone who was a leader,” he said. “I think they were looking for a collaborative-type effort. I think the sabermetrics is something that — I don’t think it’s an overriding thing, but I think they definitely want to integrate it on a much bigger level. So I felt they wanted to create a team with the manager and try and figure out a way to make a run.

“And I think Mickey gives them that with the pitching experience, what he’s been able to do in Cleveland for the last five years learning under the tutelage of Terry Francona. So I’m not shocked he’s the guy.”

The post DeRosa “Disappointed” He Missed Out On Mets Manager appeared first on Metsmerized.

We continue our widely-beloved “Know Your Stats” series with a “gateway stat:” Weighted Runs Above Average. wRAA is essentially wOBA converted into runs, but understanding wRAA is crucial to understanding Wins Above Replacement and Weighted Runs Created Plus, which we’ll be talking about this afternoon.

One of the biggest issues with rate statistics like wOBA and On-Base Percentage is that they don’t put the production into baseball terms. What does a .340 wOBA really mean? wRAA makes it easy by putting it into the “currency” of baseball: runs. Before we get started, here is the wOBA formula this year

wOBA = (0.688×uBB + 0.719×HBP + 0.878×1B + 1.245×2B + 1.576×3B + 2.030×HR) / (AB + BB – IBB + SF + HBP)

The formula for wRAA is pretty simple. To convert a .350 wOBA to wRAA, you simply subtract the league wOBA (.313 in 2015), and divide that by the wOBA scale for that year, which slightly changes based on the particular weights for that season. Then, to adjust for playing time, you multiply by plate appearances. Here is the formula:

wRAA = ((wOBA – league wOBA) / wOBA scale) × PA

Now here is the formula for someone from last with 600 plate appearances and a .350 wOBA:

wRAA =  ((.350 – .313) / 1.251) × 600

wRAA = (.037/1.251) x 600

wRAA= 17.7 runs above average

Just as it sounds, this player would be worth 17.7 runs above average at the plate. How good is that exactly? Here is a “rule of thumb” chart for the stat, courtesy of Fangraphs:

Of course, it’s important to remember that like wOBA and any traditional rate stat, wRAA is context-neutral. Also, wRAA is critical for understanding Wins Above Replacement, since it is the offensive component. You won’t see it used much in articles because it doesn’t have all that much practical use on its own, but as you’ll see, knowing it makes it easier to wrap your head around a few more widely-used stats. That’s why it’s in some ways a “gateway stat.”

In Context

Further Reading

• wRAA – Fangraphs
• wRAA For Position Player WAR Explained- Baseball-Reference
• How wRAA fits into fWAR

Other installments

• OPS/OPS+
• wOBA

The post Know Your Stats: Weighted Runs Above Average (wRAA) appeared first on Metsmerized.

For the next few days, I will be bringing back my “Know Your Stats” series that I began a few years back to highlight some important sabermetric stats and concepts. We begin this afternoon with OPS and OPS+.

OPS, or On Base Plus Slugging was one of the first sabermetric stats to go mainstream. It is, as the name implies, On-Base Percentage plus Slugging Percentage. It’s crude and simple, but it’s a good quick and dirty reference tool

OPS is expanded on even further when made into an index, OPS+. OPS+ does something very important: puts the OPS into context. The stat makes it possible to compare players from different eras, different teams, and different ballparks.

OPS+ is set on a percentage point scale. Essentially it is the percentage of league OPS. 100 (or 100% of the league average) is the league average, while a 110 mark is ten percent better than league average, and 90 is ten percent worse.

There are many issues with the crude OPS and OPS+. Is one point of OBP worth the same as one point of SLG? The math says no. In fact, the math says a point of OBP is worth 1.7 times what a point of Slugging is. Neither OPS nor OPS+ tell you the composition of OBP or Slugging and thus overvalues extra base hits.

OPS as I mentioned, is crude and the most basic sabermetric stat out there. It has its flaws, but it is a great way to get people to start thinking about sabermetrics. OPS and OPS+ are solid stats and certainly better than batting average, although not as good as wOBA or wRC+.

More thoughts

• Anytime there is a stat with a “+” at the end, that means it is an index and adjusted for park factors. I get a lot of questions and concerns about the fact that these park factors sometimes change from year to year. However, these changes are so miniscule from year to year that they don’t really effect the stat. Here are Yankee Stadium’s park factors going back to 2009:

Formula

OPS= ((H +BB+HBP)/PA) + (TB/AB)

OPS+=100 x (OBP/lgOBP*+SLG/lgSLG*- 1) then park adjusted

In Context

Further Reading

• Shortcomings of OPS as an Advanced Metric- Sports Data Research

• Career OPS+ Leaderboard

Up Next: wOBA

The post Know Your Stats: OPS/OPS+ appeared first on Metsmerized.

Judging from comments on my twitter feed, I’d venture to guess that the prevailing emotion among Mets fans out there for this season is abject frustration. While there are times when we are offered glimmers of hope and slivers of consistency, they tend to be quickly snuffed out. The team itself is probably more likeable than it has been in a while, but the constant RISP and LOB trends tend to put a proverbial damper on these good feelings like a wet wool blanket tossed on uncle Jimmy after his little bbq accident at the 4th Of July get together. We’re left with that nasty smell of burnt forearm hair and losses that should have been wins.

I can’t remember a season so full of “could have beens.” I honestly believe we might have easily been 8 or 10 wins up in the win column if only we’d had a few things break our way, if only we had a few more clutch hits. The Mets, post 2006, have been plagued with the worst label you can have as a team, they are perceived as unclutch. More recently they seem capable of pitching well enough, they’ve repaired a chronically leaky pen, but the team as a whole continues to struggle with scoring runs in high leverage situations. Is this apparent perception borne out statistically?

The world of sabermetrics seems to put out a new stat every week, and each one more complex than the last. I actually read an article a couple of days ago that asked you to refer back to first year calculus, that’s like asking me to refer back to my time in the birth canal. There are some things I’d rather not remember. There is actually a stat now called “clutch.” Clutch = (WPA / pLI) – WPA/LI. It is a measure loosely based on something called sequencing and “performance bunching.” In a nutshell, clutch measures a team’s ability to group fortuitous events together with productive results.

Take the Nats series for instance, the Mets left a bunch of runners on base, and inning after inning seemed to string hits together only after getting two outs, which resulted in being repeatedly turned away without scoring — they had a clutch score of -0.07 (0 = average). The Mets, ostensibly, appear to be extremely unclutch, however, when you take a closer look, the numbers don’t exactly bear this out. They have a whopping -44 rdif and according to fangraphs, offensively are the tenth most “clutch” team in baseball with a rating of -0.20. Are you kidding me?

Mets hitters are actually more “clutch” than the Nationals (the Nats have a clutch rating of -0.44) … Why?? I don’t know … THIRD BASE! But before I add another “and I don’t give a damn,” Lou Costelloism, I should mention that given what the Mets have accomplished statistically, they have won more games than they should have. Wonderful, so the Mets are actually pretty clutch given how bad they are. That makes about as much sense as saying a pig can fly fairly well considering he’s a pig … but I get it.

This my friends is why we may be stuck with Terry Collins. The numbers gurus are quick to dump out buckets of stats showing that given what the Mets have produced, they’ve actually won more games than they should have. Right now the most clutch teams in baseball are the Royals, the Orioles, the Red Sox (really?), the Yankees, and the Braves, the least clutch teams are the Twins, the Rockies, the Angels, the Rays, and the Cubs. The Mets currently have a .471 winning percentage, however, BaseRuns a statistic that strips away variation from performance and tells you in a sense what a team should have done were it not for sequencing and “clutch events,” says the Mets should have a .458 winning percentage.

Statisticians are also quick to point out that clutch is meaningless, primarily due to the inordinately high probability of regression. According to their theory, the Giants were never really better than the Dodgers, they have simply been extremely clutch, similarly they cite the Orioles and the Royals as examples of teams that are currently running ahead of their competition contrary to actual on field performance … again mostly because they’ve been lucky enough to group or sequence productive events (they’ve been clutch). Regression, however, is unavoidable. Jeff Sullivan of fangraphs recently showed in convincing detail that there is no such thing as clutch … clutch is simply a grouping of productive events that happens to coincide with high leverage situations, it is random and thus highly vulnerable to regression. Which means the Mets, given their production, may end up losing at an even higher rate than they have thus far.

Now I am not familiar enough with what goes into these statistics to comment on whether clutch performances are anomalies on a team level and whether regression is inevitable. If it is then the Royals will not win their division and the Rays will make a run at some point, but some teams, the Orioles and Giants come to mind, appear to be consistently “clutch” which runs contrary to league regression trends. There are outlying examples of teams that do not regress. In these instances there may be opportunities for determining whether there are in fact occasional examples of teams that have been for whatever reason capable of bunching improbable productive events together consistently. We saw this first hand with the Giants. An error here, a passed ball there, and poof, they snatch a win from the jaws of defeat. Maybe clutch has something to do with not being bright enough to be nervous in a situation where you should be nervous … Hunter Pence, that poor man’s wanna-be Brandon Nimmo, comes to mind. Who knows. I sure don’t.

If I had to guess I would say that clutch hitting does not correspond accordingly with clutch pitching, if it did I’m sure the Nats with their 17 WAR for pitching would have a higher overall clutch rating. While the Mets have failed to produce on par with a .471 winning percentage, their pitching has been stifling at times and I think clutch pitching performances are more difficult to qualify than clutch hitting performances because of all the myriad situational nuances that go into a pitcher’s mound presence and execution. Tom Seaver by most accounts would be considered a clutch pitcher, but he was also really really good. I would wager the 1969 Mets were an extremely clutch team if you go off of their production, but I’d also wager the teams who faced the Mets down the stretch and in the playoffs in 1969 didn’t think clutchness had anything to do with it … they simply overwhelmed you with pitching.

What worries me in light of all this is that our current front office’s adherence to sabermetrics dictates that the team is performing above it’s capabilities, which would imply their coaching staff and manager are doing a great job. The problem with this approach is it is an after the fact analysis. The Mets have not produced but have somehow won more than they should have given their production … unfortunately one of the reasons they’ve failed to produce is a problematic roster and an even more problematic allocation of playing time from said roster. It is akin to a baker using the wrong ingredients in a cake that ends up tasting terrible and giving him a pass because, well it isn’t fair to expect a cake with the wrong ingredients to taste good.

The post Mets Are More ‘Clutch’ Than The Nats, Statistically Speaking appeared first on Metsmerized.

Some of the answers are rather obvious. David Wright looks to be regressing, but for now at least, he’s still a very useful player. Obviously, the pitching depth is incredible both at the major league level and coming up through the farm system. Guys like Noah Syndergaard, Rafael Montero, and Steven Matz are well-known to Mets fans, but even in smaller prospects like Jack Leathersich and Cory Mazzoni, the Mets have the ability to put together a historically great rotation and bullpen filled with homegrown talent.

It’s clear that the team needs some more bats in the lineup. The question is, how many? Shortstop is an issue because while Ruben Tejada has improved upon an absymal 2013, he still hasn’t been anything to write home about. He’s been getting on base at a very nice pace, flashing a pretty .355 OBP, but his complete disregard for extra base hits, as evidenced by his .285 SLG, is an issue.

Outfield help is a necessity. The Young boys in left field have been nothing short of a disappointment, which is why Chris Young found himself Designated for Assignment last week. The really interesting debate begins when looking at who stands next to either of them in the outfield grass.

Juan Lagares has been an elite defensive player since his career began last April. The problem is that his offensive success has come and gone periodically. Is it worth locking him in as the center fielder when there’s no guarantee that he’ll ever develop into anything more than an inconsistent hitter?

Throughout his minor league career, Lagares was a largely uninspiring hitter. Since signing with the Mets as an amateur free agent at 17 years old in 2006, Lagares hit .281/.322/.403/.725 over the past nine seasons in all levels of the minors.

His best offensive season came in 2011, when he played 82 games in high-A and 38 games in Double-A, posting an overall .349/.383/.500/.883 line. And prior to making his major league debut in April 2013, he looked like he was on his way to a nice season in Las Vegas, hitting .346/.378/.551/.929 in a very tiny sample of 17 games.

There are some trends when looking at his professional career as a whole. First of all, Lagares has been a relatively free swinging hitter, as seen by his low walk totals. Over his first 203 games in the MLB, he’s drawn only 33 walks, which translates to one base-on-balls about every 6 games. His walk rate is 4.6%, which is well below the major league average of 7.9% over the past two years.

He also tends to swing at bad pitches more than most. According to Baseball Info Solutions, Lagares has swung at 35.8% of pitches outside of the strike zone during his major league career, compared to the major league average of 31%.

What makes his impatience somewhat bearable is the fact that he’s been a pretty decent contact hitter, which levels out his OBP to an acceptable level. While his .281 minor league batting average may not look like the greatest, considering the competition that he’s facing, it looks better when compared to the fact that he’s hitting .283 in 83 major league games this year.

While he may be over-aggressive in swinging at pitches outside of the strike zone, he also successfully makes contact at an above average clip. He makes contact with outside pitches 72% of the time, well above the MLB average of 66.2%. While the quality of that contact isn’t necessarily great, it’s good to know that if he’s going to keep chasing balls out of the zone, he’s not simply whiffing all of the time.

At times, he is able to flash a good amount of power. Some of that is due to his speed allowing him to stretch singles in the doubles, and doubles into triples, but as always, what happens off the bat is the most important thing to evaluate. A .403 slugging percentage in the minor leagues doesn’t suggest that he’ll hit for much power in the majors, but overall, his power has been coming in inconsistent bursts.

The problem is, I’m not sure how much of his moderate offensive success will continue. That .283 average in 2014 is heavily augmented by a ridiculous .363 BABIP, well above his major league mark from last year, which stood at .310 by year’s end. As a result, his .242 average from his rookie season has spiked significantly.

BABIP, or batting average on balls in play, can be a very telling stat when looking at a player’s future. A very high BABIP usually suggests that the batter is set for some regression, as they have been getting very good luck with bad defense, cheap infield singles, and bloopers in no-man’s land. A very famous case disproving this theory is Mariners’ center fielder Austin Jackson, who is able to use his speed to maintain a high BABIP mark. It’s entirely possible that Lagares’s speed is aiding him this season much more than last, but any wild fluctuation in BABIP is a red flag. It’s definitely something to keep in mind when evaluating Lagares’s career to this point and projecting forward.

Overall, Juan Lagares’s MLB averages of .260/.299/.367/.667 look to be right in the ballpark of what his slash line will look like for the forseeable future. He’s only 25 years old, meaning that he has yet to enter his offensive prime, but it’s unlikely that any player will get drastically better in their prime if their past doesn’t show anything that should get people excited. Juan Lagares will almost certainly be at best a mediocre hitter with some stretches of greatness, and many more slumps.

So, of course that means that he shouldn’t be part of the Mets’ long term plans. If there’s almost no hope for him offensively, then he needs to go, right?

I won’t blame anyone for thinking that way about Lagares. But their mind will change when they watch what the man can do with a glove. Watching Lagares field is one of the best things about the Mets. He’s easily the best center fielder to come to Flushing since Carlos Beltran‘s prime ended and he was shipped out of town.

Die-hard baseball fans already know about the other-worldy defense of Braves shortstop Andrelton Simmons, but Lagares deserves that type of mainstream attention for the way he plays in center.

So what if his defense is great? If he can’t hit, then does it really matter?

That’s true depending on what position the player in question fields. If we’re talking about a first or second baseman, or a corner outfielder, defense is much less important, but still needs to be factored into the player’s overall value. But when the amazing defensive player is a center fielder, the value of that defense is much higher. Center field is probably the most important defensive position in baseball (except maybe catcher), so having a guy out there with the talent of Lagares of extremely valuable and can save many runs.

Speaking of saving runs, Lagares has been near the top of the leaderboard in Defensive Runs Saved since making his debut. Among all fielders, Lagares is second to the aforementioned Andrelton Simmons with 49 DRS. In fact, had Lagares not missed significant time due to injuries over the past couple of seasons, he would be far and away the league leader in this category. Lagares has only played 1485.2 innings since the beginning of the 2013 season, while Simmons has played 2302 innings.

Because DRS is a cumulative stat, playing less innings hurts Lagares overall, but on a per inning basis Lagares has been much better than Simmons. He has saved .033 runs per inning compared to .024 runs per inning for the Braves’ defensive whiz. When I extrapolated those per inning averages over a full season of 1455 innings, Lagares turns out to be much better than Simmons, racking up an incredible 48 DRS while Simmons only saves 34 runs above the average fielder.

Another well-known defensive metric, UZR speaks very well of Lagares’s abilities in the field. His Ultimate Zone Rating has been tops in the major leagues at 37.6 during his career.

Defensive statistics aren’t perfect by any stretch of the imagination, but they do aid people in forming a broad opinion on a player’s defensive skills.

There are quite a few reasons why Lagares is such an incredible defensive player. These spray charts gathered by FanGraphs illustrate his talent:

The percentages seen in the second chart are measured by Inside Edge, in an attempt to put into perspective how easy each defensive play is to make. Do you see that huge gap in the middle of the second chart, which is almost all green in the first chart? That’s because Lagares does not mess up easy fly balls. His only missed play right in the heart of center field was considered by Inside Edge to be impossible to field. According to this chart, he’s missed quite a few fly outs with a 40% or higher chance of being fielded. But, when the time parameters are shortened to include only his 2014 results, you get this:

All but one of the yellow and green dots completely vanish, suggesting that Lagares has been even more reliable in the field this season than last. It’s a scary thought that someone already so good can keep improving. He’s a very sure-handed fielder, and tends not to mess up routine plays. He also won’t make the spectacular, diving grabs that get replayed all over the internet and ESPN, but I’d trade that for his inability to make mistakes on easy fly balls.

FanGraphs’ Jeff Sullivan wrote an amazing piece of Lagares’s defensive prowess last September, and I encourage everyone to read it. In a quick analysis of Lagares in center, one would be able to see that his starting position is routinely more shallow than most center fielders in the MLB. He’s able to do that because his speed allows him to catch up to the balls that go over his head, as seen here:

Photo courtesy of CBS Sports

And here:

GIF courtesy of Amazin Avenue

As Sullivan wrote, playing so shallow, along with a quick first step and very sound fundamentals enables Lagares to catch up to balls quicker than usual, and closer to the infield. Once there, he’s able to fire the ball in and, despite not having the strongest arm in the world, overall has one of the best. Starting his throws so much closer to the infield lessens the need for a cannon arm like what Yasiel Puig and Yoenis Cespedes are known for. Shorter, more accurate throws makes Lagares nearly impossible to run on.

Teams have started to take notice of this, as Lagares’s 15 assists in 2013 have been nearly eliminated from his stat line this season, as the number now only stands at 4 in the middle of August. That’s because everyone is so deathly afraid of Lagares’s arm that they won’t run on him unless the circumstances are perfect. They know that he isn’t any average center fielder out there.

There are few center fielders in the league that can even come close to matching Lagares’s value. The only players that come anywhere near him in center are Milwaukee’s Carlos Gomez, Texas’s Leonys Martin, Arizona’s A.J. Pollock, and Kansas City’s Jarrod Dyson and Lorenzo Cain. No, not even my boy Mike Trout, who has admittedly fallen off defensively over the last couple of years, can sniff Lagares’s defensive value. And while these other guys may come close, Lagares is clearly better than all of them.

What does all of this mean for the Mets? 

A remarkable defensive center fielder is rare, and any team with a player like that should make sure that they do their best to retain that player, even if their offense is subpar. Watching Lagares at the plate might be a tough pill to swallow at times, but as long as the other seven position players are quality hitters, Lagares’s issues will be much less of a factor. Hitting him eighth and surrounding him with good bats will lessen the blow. In the case of Lagares, his exceptional defense more than makes up for his lackluster offense.

All statistics courtesy of Baseball-Reference and FanGraphs. All graphs courtesy of FanGraphs. 

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