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Big Data Baseball: Math, Miracles, and the End of a 20-Year Losing Streak
New York Times Bestseller
After twenty consecutive losing seasons for the Pittsburgh Pirates, team morale was low, the club’s payroll ranked near the bottom of the sport, game attendance was down, and the city was becoming increasingly disenchanted with its team. Big Data Baseball is the story of how the 2013 Pirates, mired in the longest losing streak in North American pro sports history, adopted drastic big-data strategies to end the drought, make the playoffs, and turn around the franchise’s fortunes.
Big Data Baseball is Moneyball for a new generation. Award-winning journalist Travis Sawchik takes you behind the scenes to expertly weave together the stories of the key figures who changed the way the Pirates played the game, revealing how a culture of collaboration and creativity flourished as whiz-kid analysts worked alongside graybeard coaches to revolutionize the sport and uncover groundbreaking insights for how to win more games without spending a dime. From pitch framing to on-field shifts, this entertaining and enlightening underdog story closely examines baseball’s burgeoning big data movement and demonstrates how the millions of data points which aren’t immediately visible to players and spectators, are the bit of magic that led the Pirates to finish the 2013 season in second place and brought an end to a twenty-year losing streak.
After twenty consecutive losing seasons for the Pittsburgh Pirates, team morale was low, the club’s payroll ranked near the bottom of the sport, game attendance was down, and the city was becoming increasingly disenchanted with its team. Big Data Baseball is the story of how the 2013 Pirates, mired in the longest losing streak in North American pro sports history, adopted drastic big-data strategies to end the drought, make the playoffs, and turn around the franchise’s fortunes.
Big Data Baseball is Moneyball for a new generation. Award-winning journalist Travis Sawchik takes you behind the scenes to expertly weave together the stories of the key figures who changed the way the Pirates played the game, revealing how a culture of collaboration and creativity flourished as whiz-kid analysts worked alongside graybeard coaches to revolutionize the sport and uncover groundbreaking insights for how to win more games without spending a dime. From pitch framing to on-field shifts, this entertaining and enlightening underdog story closely examines baseball’s burgeoning big data movement and demonstrates how the millions of data points which aren’t immediately visible to players and spectators, are the bit of magic that led the Pirates to finish the 2013 season in second place and brought an end to a twenty-year losing streak.
256 pages, Paperback
First published May 19, 2015
121 people are currently reading
2150 people want to read
About the author
Travis Sawchik
2 books41 followersTravis Sawchik is a sportswriter for FiveThirtyEight. He is a former staff writer for FanGraphs and previously covered the Pirates and Major League Baseball for the Pittsburgh Tribune-Review. Sawchik has won national Associated Press Sports Editor awards for enterprise writing and numerous state-level awards. Sawchik's work has also been featured or referenced on The Athletic, ESPN, Grantland, and MLB Network.
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February 2, 2024
I picked up Big Data Baseball because I thought it was a book about my lifetime team, the Pittsburgh Pirates. And it is, kinda-sorta, though really only peripherally. My Pirates were the object lesson of the book’s actual subject — big data and how it has changed the game of baseball. (Yeah, I know I should have figured that out from the title, but fans who spend a lifetime routing for a team that hasn’t been to a World Series in nearly half a century and has far more losing than winning season in that period don’t always think rationally.)
Big Data Baseball explains how and why the data revolution in baseball has been fundamentally changing the way the game is played. Modern computers have made the collection of vast amounts of information on every player in the game possible — the kind of valuable information that simply wasn’t available to clubs and managers throughout most of baseball’s history. The book Moneyball examined how that data, when effectively studied and used, changed baseball’s offense — emphasizing walks and on base percentage, to find player skills that were previously undervalued, flying beneath the radar. Big Data Baseball examines how the data now available changed the way clubs think about defense.
And that’s where my Pirates come in. They were a team mired in the worst slump in professional sports history — twenty years since they had last made the playoffs, and losing seasons for every one of those twenty years. They were a desperate club, looking for any edge to change things up. This is an important point, because even if a team has a data department, it can only make a difference if the field manager is willing to make the changes on the field that the data calls for. The Pirates and their last-chance manager, Clint Hurdle, were at that point of desperation. The data the Pirates took advantage of was mostly about making on field shifts against individual batters, emphasizing pitch release point and using a two seamed fastball rather than the four seamer, and having their catcher concentrate on pitch framing to trick umpires into more strike calls. These changes that the Pirates implemented on the field in 2013 gave them an edge which helped to break their twenty year losing streak, and sent them back to the playoffs in 2013 and the following two seasons.
You don’t need to be a math guy to appreciate this book. Sawchik explains his subject well, and using the once hapless Pirates as the object lesson drives home the point. Even baseball traditionalist like me who learned their baseball statistics from the back of baseball cards fifty years ago can comprehend it.
The bad news (for me) is that once the rest of the league made adjustments and started implementing the same changes as the Pirates, the Buccos lost their edge, and have largely returned to hapless futility.
Big Data Baseball explains how and why the data revolution in baseball has been fundamentally changing the way the game is played. Modern computers have made the collection of vast amounts of information on every player in the game possible — the kind of valuable information that simply wasn’t available to clubs and managers throughout most of baseball’s history. The book Moneyball examined how that data, when effectively studied and used, changed baseball’s offense — emphasizing walks and on base percentage, to find player skills that were previously undervalued, flying beneath the radar. Big Data Baseball examines how the data now available changed the way clubs think about defense.
And that’s where my Pirates come in. They were a team mired in the worst slump in professional sports history — twenty years since they had last made the playoffs, and losing seasons for every one of those twenty years. They were a desperate club, looking for any edge to change things up. This is an important point, because even if a team has a data department, it can only make a difference if the field manager is willing to make the changes on the field that the data calls for. The Pirates and their last-chance manager, Clint Hurdle, were at that point of desperation. The data the Pirates took advantage of was mostly about making on field shifts against individual batters, emphasizing pitch release point and using a two seamed fastball rather than the four seamer, and having their catcher concentrate on pitch framing to trick umpires into more strike calls. These changes that the Pirates implemented on the field in 2013 gave them an edge which helped to break their twenty year losing streak, and sent them back to the playoffs in 2013 and the following two seasons.
You don’t need to be a math guy to appreciate this book. Sawchik explains his subject well, and using the once hapless Pirates as the object lesson drives home the point. Even baseball traditionalist like me who learned their baseball statistics from the back of baseball cards fifty years ago can comprehend it.
The bad news (for me) is that once the rest of the league made adjustments and started implementing the same changes as the Pirates, the Buccos lost their edge, and have largely returned to hapless futility.
September 14, 2015
At first blush, this is a baseball book about the 2013 Pittsburgh Pirates. Small Market Team Overcomes the Odds, Enjoys First Winning Season in 21 Years and Advances Past the First Round of Playoffs.
And yet, that's not what it is. In actuality, it's a business book, with what I'd identify as two primary messages. The first is that 'big data' offers the potential to provide market data beyond what we've ever dreamed was possible. And the second is that it requires a lot of leadership and communication to transform analytic findings into practice.
For the baseball fan, there are a lot of good examples of analytical findings: the advantages of defensive shifts; pitching inside; pitching more 2-seam fastballs and fewer 4-seamers; pitch framing; metrics beyond simple pitch counts. I'm sure there could be a lot more, except that a lot of this type of information is proprietary now for baseball clubs trying to keep a leg up on the competition.
But the real interesting part of it is the fact that stat-heads often come up with findings that are entirely counter to what traditional strategies and approaches dictate on the field. And because baseball has been around for a century and a half, changing minds and implementing innovations is not an easy task. That's why Clint Hurdle (cast as the old-school manager that's desperate enough to try to buck tradition) is at the center of this story, the lynchpin between the number crunchers and the practitioners. It's one thing to run a bunch of computer simulations with defenses positioned in different ways. It's an entirely different matter to get a whole team on board to adjusting their strategies to implement something entirely new. That's where the storytelling really pays off.
The other fascinating part of it to me was the idea that baseball is such a team sport. It has always struck me that the game is more of an individual sport than, say, basketball or soccer or football, because a pitcher is alone on the mound and a hitter is alone in the batter's box. But nearly every piece of this book is contrary to that idea. Defensive positioning and pitching strategy and even hitting strategy are all completely interconnected. (Although this is not really the story that Sawchik tells.)
Another implication is that analytics - finding strategy and value where others don't - is an arms race. In that respect, this book only offers one snapshot of willing revolutionaries at one particular moment in time. It would be more interesting to see how these changes impact the game over a longer period of time, how management strategies change, how valued skill sets change, and how rules change. But that's perhaps another book at another time.
And yet, that's not what it is. In actuality, it's a business book, with what I'd identify as two primary messages. The first is that 'big data' offers the potential to provide market data beyond what we've ever dreamed was possible. And the second is that it requires a lot of leadership and communication to transform analytic findings into practice.
For the baseball fan, there are a lot of good examples of analytical findings: the advantages of defensive shifts; pitching inside; pitching more 2-seam fastballs and fewer 4-seamers; pitch framing; metrics beyond simple pitch counts. I'm sure there could be a lot more, except that a lot of this type of information is proprietary now for baseball clubs trying to keep a leg up on the competition.
But the real interesting part of it is the fact that stat-heads often come up with findings that are entirely counter to what traditional strategies and approaches dictate on the field. And because baseball has been around for a century and a half, changing minds and implementing innovations is not an easy task. That's why Clint Hurdle (cast as the old-school manager that's desperate enough to try to buck tradition) is at the center of this story, the lynchpin between the number crunchers and the practitioners. It's one thing to run a bunch of computer simulations with defenses positioned in different ways. It's an entirely different matter to get a whole team on board to adjusting their strategies to implement something entirely new. That's where the storytelling really pays off.
The other fascinating part of it to me was the idea that baseball is such a team sport. It has always struck me that the game is more of an individual sport than, say, basketball or soccer or football, because a pitcher is alone on the mound and a hitter is alone in the batter's box. But nearly every piece of this book is contrary to that idea. Defensive positioning and pitching strategy and even hitting strategy are all completely interconnected. (Although this is not really the story that Sawchik tells.)
Another implication is that analytics - finding strategy and value where others don't - is an arms race. In that respect, this book only offers one snapshot of willing revolutionaries at one particular moment in time. It would be more interesting to see how these changes impact the game over a longer period of time, how management strategies change, how valued skill sets change, and how rules change. But that's perhaps another book at another time.
August 14, 2019
The book is so amazing.
October 20, 2015
Another entry in the post-Moneyball "how'd these guys do it?" genre. The problem is Michael Lewis is a great writer, and a lot of the authors trying to emulate him are less talented at weaving a wonderful story out of metrics. The Pirates are an interesting team, but aside from maybe Clint Hurdle and Russell Martin, I don't leave this book for a real feel for personalities, and only so much plot can be driven by describing infield shifts. Overall, there is interesting material, but the tale would be told better in the hands of a more talented writer.
March 27, 2016
Low-rent Moneyball with less compelling characters, less interesting revelations (a whole chapter is about how PIT has a large LF so they found - wait for it - a fast guy to play left!!), and a far less talented author.
Also full of editing mistakes and padded with pages of pitch-by-pitch details.
Also full of editing mistakes and padded with pages of pitch-by-pitch details.
January 6, 2019
First and foremost, I want to state my appreciation for how often the expressions “get on board” and “preventing a mutiny” were used in this book about the 2013 Pirates organization. Why bother writing about the pirates if you don’t take that liberty.
Big Data Baseball follows in the same vein as Moneyball, as it follows a small-market low-budget team make its way to the post season. But where Moneyball’s Billy Beane took an underused statistic and made it important, the pirates opted for a different route. They created new stats to find new value, stats that are the core of baseball contracts and announcer wisdom in today’s game.
Sawchik did a remarkable job explaining these new age stats in a way that made them make sense, but in a way he had his work cut out for him. The stats do make sense (now all GM’s are looking at defensive effectiveness and WAR,) but also they are so used today that it seems silly they weren’t before. How could we not think aligning defenders to where the ball is most often hit, is the right idea?
My only complaint with this book would be Sawchik’s excessive repetition. If you are not aware of what the Ted Williams shift (an extreme shift for a dramatic pull hitter), Sawchik would remind you every two pages that the Ted Williams shift is an extreme shift for a dramatic pull hitter. And perhaps, he put too much drama in the will-they won’t-they make it storyline, where I wanted to know more about the how-they and why-they. Overall, it was very enjoyable and enthused me to try and discover my own statistics that might better explain why good batters and pitchers are good, and I look forward to seeing how the game evolves further with big data.
Big Data Baseball follows in the same vein as Moneyball, as it follows a small-market low-budget team make its way to the post season. But where Moneyball’s Billy Beane took an underused statistic and made it important, the pirates opted for a different route. They created new stats to find new value, stats that are the core of baseball contracts and announcer wisdom in today’s game.
Sawchik did a remarkable job explaining these new age stats in a way that made them make sense, but in a way he had his work cut out for him. The stats do make sense (now all GM’s are looking at defensive effectiveness and WAR,) but also they are so used today that it seems silly they weren’t before. How could we not think aligning defenders to where the ball is most often hit, is the right idea?
My only complaint with this book would be Sawchik’s excessive repetition. If you are not aware of what the Ted Williams shift (an extreme shift for a dramatic pull hitter), Sawchik would remind you every two pages that the Ted Williams shift is an extreme shift for a dramatic pull hitter. And perhaps, he put too much drama in the will-they won’t-they make it storyline, where I wanted to know more about the how-they and why-they. Overall, it was very enjoyable and enthused me to try and discover my own statistics that might better explain why good batters and pitchers are good, and I look forward to seeing how the game evolves further with big data.
April 6, 2018
This is the story of the Pittsburgh Pirates of 2013. With a number of years of poor performance, Pirates management decided to invest in analytics in a big way. You could say this is the sequel to “Moneyball”, amping up the analytics analysis to start playing different. Here, the Pirates go big on “the shift”, moving defensive players to where each hitter tends to hit in order to make more outs. Given the move to optimize the shift, they also determine that ground ball pitchers are more valuable – ground balls make the shift more effective. So they invest in ground ball pitchers. And they also invest in “pitch framers”, catchers who have perfected the art of making a ball look like a strike to an umpire through their hand and body movements. These three focuses, along with rehabbing pitchers, are the bulk of the storyline here. But what a story. The author categorizes and summarizes the Pirates strategy in a way that the reader thinks he’s being let in on a secret. This was very well done. The personal stories of the players, managers, and others (including a mention of a happy fan that jumped off the Roberto Clemente bridge in celebration) were also well done and memorable. This is the kind of baseball book I really enjoy – you get the game action, along with the perspectives of the players and managers/analysts. The book also follows through with a chapter on the 2014 season and the lasting impact of the changes made. Learning: no resting on your laurels when it comes to analytics. Very enjoyable.
August 16, 2015
How did the Pirates finally become competitive and make the post-season after several decades of futility? Why are we seeing more and more teams employing defensive shifts? (In fact, the rise in shifts has become dramatic.) It's all in the numbers and not the traditional numbers. While batting averages, ERAs, and a handful of other data points have been the traditional markers for assessing performance they don't necessarily translate into producing wins. The new numbers crunchers go well beyond the standard template and focus on multiple tendencies that don't draw as much notice, for example - how often a player hits ground balls to the left or right side and precisely where. Some of those patterns are common sense, but the base of evaluation has been expanded to which catchers are the best at stealing strikes (it confers a huge advantage over the course of a season), which outfielders cover the most ground efficiently, which pitchers are best at inducing ground balls by changing their pitching grip, ad infinitum.
More and more, teams are not hiring data coaches and analysts and the game is being fought on computers. Technology is taking the diamond by storm. Those who are slow to adapt are in peril of falling by the wayside. This is Moneyball with a keyboard. The baseball revolution that Bill James, SABR, et al initiated has become institutionalized. It is also driving a shift in momentum back in favor of defense after the offensive explosion of the late 90s and early 21st century.
You do not have to have a good command of math to appreciate Sawchik's commentary. He is judicious in his use of the numbers and makes a painstaking effort to break them down to show how they apply to the specifics of game situations. This is not a criticism, but it should be noted you do have to have a fundamental understanding of baseball in order to appreciate some of the analysis and finer points he is explicating. He is also astute enough to show that as teams embrace the new shift models, more digging is required to counter that tactic and the cat and mouse game continues. This book will surely inspire sequels as the cat and mouse game continues.
More and more, teams are not hiring data coaches and analysts and the game is being fought on computers. Technology is taking the diamond by storm. Those who are slow to adapt are in peril of falling by the wayside. This is Moneyball with a keyboard. The baseball revolution that Bill James, SABR, et al initiated has become institutionalized. It is also driving a shift in momentum back in favor of defense after the offensive explosion of the late 90s and early 21st century.
You do not have to have a good command of math to appreciate Sawchik's commentary. He is judicious in his use of the numbers and makes a painstaking effort to break them down to show how they apply to the specifics of game situations. This is not a criticism, but it should be noted you do have to have a fundamental understanding of baseball in order to appreciate some of the analysis and finer points he is explicating. He is also astute enough to show that as teams embrace the new shift models, more digging is required to counter that tactic and the cat and mouse game continues. This book will surely inspire sequels as the cat and mouse game continues.
June 30, 2015
I really wanted to love this book, given that it's about baseball analytics, but oh boy, did the author (or more likely, editor) nip that enthusiasm in the bud.
The author is paid to cover the Pirates, so I can't imagine the numerous baseball-related errors were his, but were probably the editor's. (For example, relief pitchers come out of the "bullpen", not the "bull pen", though that would make pitching changes a little more entertaining. More importantly, a 2-strike count is "0-2", not "2-0", and beginning a chapter about why pitchers want to get ahead of hitters by citing stats about how well pitchers do in a "2-0" count is pretty confusing.) The writing wasn't great either, but with sportswriting, one must keep one's expectations pretty low.
One final contention: The author (and this probably WAS the author and not the editor) really liked counting data points. As in, he would talk about a new data-harvesting method by describing how much it increased the number of data points collected from baseball games. That's not adding apples and oranges, it's adding apples and atoms... It's a measure that means absolutely nothing.
Having said that, the book was informative about the strategies the Pirates employed in 2013, which was interesting. But it'd have been nice if the final draft had displayed more knowledge of baseball or big data, or even better, both.
The author is paid to cover the Pirates, so I can't imagine the numerous baseball-related errors were his, but were probably the editor's. (For example, relief pitchers come out of the "bullpen", not the "bull pen", though that would make pitching changes a little more entertaining. More importantly, a 2-strike count is "0-2", not "2-0", and beginning a chapter about why pitchers want to get ahead of hitters by citing stats about how well pitchers do in a "2-0" count is pretty confusing.) The writing wasn't great either, but with sportswriting, one must keep one's expectations pretty low.
One final contention: The author (and this probably WAS the author and not the editor) really liked counting data points. As in, he would talk about a new data-harvesting method by describing how much it increased the number of data points collected from baseball games. That's not adding apples and oranges, it's adding apples and atoms... It's a measure that means absolutely nothing.
Having said that, the book was informative about the strategies the Pirates employed in 2013, which was interesting. But it'd have been nice if the final draft had displayed more knowledge of baseball or big data, or even better, both.
August 5, 2020
It's strange that a book from 2015 is now best read as a historical document, but here we are. Specifically, this is a document of one phase in the development of baseball analytics.
The biggest issues the book has are simply how it has held up over time. The 2013 Pirates had a few unique points of emphasis that come through in this text: 1) aggressive infield shifting, 2) prioritize catcher framing, and 3) pitching sinkers low in the zone.
Infield shifting is certainly still a thing, but it's seen as less game-changing than it was at the time. And while the Pirates were among the teams showing a newfound focus on it, the introduction of better batted ball tracking meant that they were far from the only ones. Each team had to get over the hurdle (no pun intended) of applying the shift, but the Pirates were not game-changers. In a similar way, catcher framing became less of a distinguishing feature in short order because negative framers were mostly moved out of the game or into other positions, so it became difficult for a team to have a truly standout framer.
The issue of the sinker has aged the worst. With the onset of the fly ball revolution (coincident with the changes in the MLB ball construction in 2015), slower balls low in the zone were converted to home runs at a much higher rate. Despite this, the Pirates continued to emphasize inducing ground balls, even as they became less common. The uniform approach toward their pitchers has resulted in many of their starters improving measurably upon their departure (Gerrit Cole, Charlie Morton, Tyler Glasnow).
It's not the author's fault that he couldn't see the future. I do think there are several sections where the text tends to take the subject's word for things that could be analyzed: Bob Nutting's spending is seen as a self-evident necessity, Hurdle's disinterest in his pitchers getting strikeouts, the writing-off of development in areas like framing or pitch selection. I think I would have enjoyed this and learned more from it if I'd read it upon release. But the industry has evolved in the interim and this one is largely out of date. Like Moneyball, it's a snapshot in time. Unlike Moneyball, it didn't change the game; the game was already moving beyond these tenets as it was published.
It's worth saying that Sawchik's second book The MVP Machine: How Baseball's New Nonconformists Are Using Data to Build Better Players had a much wider impact and is (for the moment) still quite relevant to the evolution of the game.
The biggest issues the book has are simply how it has held up over time. The 2013 Pirates had a few unique points of emphasis that come through in this text: 1) aggressive infield shifting, 2) prioritize catcher framing, and 3) pitching sinkers low in the zone.
Infield shifting is certainly still a thing, but it's seen as less game-changing than it was at the time. And while the Pirates were among the teams showing a newfound focus on it, the introduction of better batted ball tracking meant that they were far from the only ones. Each team had to get over the hurdle (no pun intended) of applying the shift, but the Pirates were not game-changers. In a similar way, catcher framing became less of a distinguishing feature in short order because negative framers were mostly moved out of the game or into other positions, so it became difficult for a team to have a truly standout framer.
The issue of the sinker has aged the worst. With the onset of the fly ball revolution (coincident with the changes in the MLB ball construction in 2015), slower balls low in the zone were converted to home runs at a much higher rate. Despite this, the Pirates continued to emphasize inducing ground balls, even as they became less common. The uniform approach toward their pitchers has resulted in many of their starters improving measurably upon their departure (Gerrit Cole, Charlie Morton, Tyler Glasnow).
It's not the author's fault that he couldn't see the future. I do think there are several sections where the text tends to take the subject's word for things that could be analyzed: Bob Nutting's spending is seen as a self-evident necessity, Hurdle's disinterest in his pitchers getting strikeouts, the writing-off of development in areas like framing or pitch selection. I think I would have enjoyed this and learned more from it if I'd read it upon release. But the industry has evolved in the interim and this one is largely out of date. Like Moneyball, it's a snapshot in time. Unlike Moneyball, it didn't change the game; the game was already moving beyond these tenets as it was published.
It's worth saying that Sawchik's second book The MVP Machine: How Baseball's New Nonconformists Are Using Data to Build Better Players had a much wider impact and is (for the moment) still quite relevant to the evolution of the game.
May 25, 2017
A more in depth Moneyball, in that the Pirates went full on with primarily defensive analysis. This one was very interesting in that the key character was the Manager, Clint Hurdle. It was a great depiction of the evolution of data driven baseball, which now is being embraced at the manager level.
December 14, 2023
The simplest way I can put it is “Moneyball but for the 2013 Pittsburgh Pirates” but that is downplaying the story as a whole. This focuses a lot of defensive metrics and the background to those stats which I found very interesting and probably my favorite aspect to the book as a whole. Being able to watch moments outlined in the story on YouTube is a super cool plus as well.
October 28, 2020
Great moneyball-type book
September 23, 2022
This book doesn’t reach the heights of “moneyball” but will appeal to fans of baseball , whereas moneyball was able to appeal to a much wider audience.
February 21, 2017
A good story trapped in below average writing and truly bad editing. So, the story, which deserves to be heard - small-market Pirates team, 20-year streak of no winning seasons, uses advanced metrics to sign two undervalued free agents, re-align their infield defense, and have their pitchers modify their tactics. All this leads to a 94-win season and a berth in the NLDS after an exciting win at home in front of a huge crowd. I have a lot of respect for Pittsburgh's data chiefs and decision makers here, because all three of their new strategies worked together to make an 80-win team on paper a 94-win team in reality.
1. Russell Martin, one of the 2 free agent signees, is an outstanding pitch-framer, and the Pirates got him at 2 years/$17M; he produced approximately 3 times that value by receiving in a manner so as to deceive the ump into believing a pitch that is just barely a ball is actually a strike.
2.The infield defense in 2012 had been below average, but in 2013 by stacking 3 infielders on one side of the field for a higher percentage of batters than all but the Brewers and Rays, the Pirates got a lot of ground balls hit right into that alignment. Hits became outs. This added roughly 9 wins to their record, the difference between an also-ran and a playoff team.
3. PNC Park (gorgeous by the way, great view of the skyline and bridges from the North Shore) is spacious, so flyball hitters have an advantage, one that can be largely negated with 2-seam sinking fastballs that more often turn into grounders, grounders that were being hit into the shifted infield defense. Of Pittsburgh's 6 most frequent starting pitchers, all 6 had an ERA below 3.60, because they bought in to pitching to the defense and to the park.
Much credit to the Pirates, and to the forward-thinking that brought their data guys into the clubhouse and that brought visual charts to the players rather than just data points. Business people reading this would be familiar, you can have the best ideas out there, but without buy in they don't get enacted. So deliver your message in a medium your stakeholders best understand.
So, yes, big ups to Pittsburgh, but, oh dear, the writing. We're told over and over about "stats that don't show up on the backs of baseball cards." Got it the first time, bro. We're told Aaron Sele is a "woeful pitcher." Real life - WAR of 20; FIP of 4.41 over 2153 innings in the heart of the steroid era. So, yeah, not woeful, more like a bit above average. And we're also told about Michael Jordan beating the Utah Jazz-Blazers. I'm impressed that even after Utah and Portland apparently merged he was still able to defeat them. And finally, there were dozens of instances in which a word was left out of a sentence. Distracting.
It would have taken an editor a single day to fix it, but, no, they just let it get published as is. Sloppy, and unnecessarily so. This should have been a 3, but the below average writing and D- editing dropped it to 2.
1. Russell Martin, one of the 2 free agent signees, is an outstanding pitch-framer, and the Pirates got him at 2 years/$17M; he produced approximately 3 times that value by receiving in a manner so as to deceive the ump into believing a pitch that is just barely a ball is actually a strike.
2.The infield defense in 2012 had been below average, but in 2013 by stacking 3 infielders on one side of the field for a higher percentage of batters than all but the Brewers and Rays, the Pirates got a lot of ground balls hit right into that alignment. Hits became outs. This added roughly 9 wins to their record, the difference between an also-ran and a playoff team.
3. PNC Park (gorgeous by the way, great view of the skyline and bridges from the North Shore) is spacious, so flyball hitters have an advantage, one that can be largely negated with 2-seam sinking fastballs that more often turn into grounders, grounders that were being hit into the shifted infield defense. Of Pittsburgh's 6 most frequent starting pitchers, all 6 had an ERA below 3.60, because they bought in to pitching to the defense and to the park.
Much credit to the Pirates, and to the forward-thinking that brought their data guys into the clubhouse and that brought visual charts to the players rather than just data points. Business people reading this would be familiar, you can have the best ideas out there, but without buy in they don't get enacted. So deliver your message in a medium your stakeholders best understand.
So, yes, big ups to Pittsburgh, but, oh dear, the writing. We're told over and over about "stats that don't show up on the backs of baseball cards." Got it the first time, bro. We're told Aaron Sele is a "woeful pitcher." Real life - WAR of 20; FIP of 4.41 over 2153 innings in the heart of the steroid era. So, yeah, not woeful, more like a bit above average. And we're also told about Michael Jordan beating the Utah Jazz-Blazers. I'm impressed that even after Utah and Portland apparently merged he was still able to defeat them. And finally, there were dozens of instances in which a word was left out of a sentence. Distracting.
It would have taken an editor a single day to fix it, but, no, they just let it get published as is. Sloppy, and unnecessarily so. This should have been a 3, but the below average writing and D- editing dropped it to 2.
August 7, 2025
my dream life is fox&fitzgeralds— what an excellent analytics/coach balance!!! good writing with a timeline that was only occasionally difficult to follow. this is exactly how baseball and data should work together. if you liked moneyball for the math you will like this; if you liked moneyball for the plot&billy beane you will find that this lacks character investigations but that is not what it’s trying to do. also russell martin you are my new favorite player re: pitch sequencing i’ll figure it out and send u an email don’t even worry about it
December 27, 2015
I haven't followed Major League Baseball closely over the last few seasons, but I've been fascinated by the statistical applications to the sport since I read Moneyball about ten years ago. I picked up Travis Sawchik's recent book Big Data Baseball hoping to better understand some more current avenues of baseball research and an idea of what today's MLB analytics departments look like. Thankfully, Big Data Baseball largely delivers, exploring the quantitative insights underpinning the Pittsburgh Pirates' team-building and strategic principles that helped the team snap a losing streak that was rapidly approaching legal drinking age.
Big Data Baseball is organized around the Pirates' aforementioned 2013 season, which saw the Pirates reach the playoffs for the first time in 20 years. This was a surprising result, as the Pirates kept most of their key players and coaches from their disappointing 2012 campaign. While Moneyball and Jonah Keri's The Extra 2% were largely concerned with the best way to draft players and assess talent, the Pirates and their meager budget were forced to put a greater emphasis on how to get the most out of their current roster. This predicament required them to comb through seas of game data through richly detailed sources such as PITCHf/x pitch tracking to identify areas they could exploit to their benefit. Chief among these were frequent and dramatic infield shifts and having pitchers adopt a low pitch count strategy with a heavy diet of sinking fastballs. The Pirates also paid a great deal of attention to pitch-framing, seeing that catchers could cause dramatic swings in wins through their mitt placement, which led them to doggedly pursue catcher Russell Martin despite his anemic hitting performance the previous year.
The book proceeds in a generally chronological fashion and covers the entire 2013 season. Each chapter covers one rough theme, whether it is the value of pitchers inducing ground balls or the impact of pitch-framing, along with a look at how the Pirates and their statistics-based approaches were faring on the diamond. Sawchik presents surface-level analysis of these quantitative conclusions, often using a graph or table and spitting out a few relevant metrics. The focus is always on the conclusions rather than specifics as to how the data was crunched. Everything is explained in a cogent fashion that doesn't require a math degree to understand, though I wish Sawchik went a little more in-depth when explaining some topics.
While Pirates data wonks Dan Fox and Mike Fitzgerald earn a hefty amount of (much-deserved) credit and ink from Sawchik for their efforts, Big Data Baseball cites a handful of other heroes. Manager Clint Hurdle was instrumental in getting his team and organization at large to buy into some seemingly counter-intuitive strategies such as the Brobdingnagian infield shifts, General Manager Neal Huntington kept his faith in Hurdle and also assembled the Pirates' Baseball Informatics department, and players such as the unexpected star pitcher Charlie Morton and pitch-framing extraordinary Russell Martin were vital to executing on the field. Sawchik gives in-depth profiles of these actors as well as a handful of others and they help flesh the book out and make the reader care more about the 2013 Pirates' fortunes. No one is as engaging as Billy Beane (that's admittedly a rather high standard) but there was some personality to the 2013 Pirates.
Sawchik covers the Pirates for the local paper The Pittsburgh Tribune-Review and Big Data Baseball features the usual benefits and drawbacks of a book by a reporter. The big positives are that Sawchik had considerable levels of access to the team throughout the entire season, allowing him to draw from in-depth interviews from all major actors and a huge base of knowledge around the team. Learning about how Clint Hurdle became fascinated by analytics from his stint as an MLB Network talking head and Russell Martin's catching techniques were some of the highlights of Big Data Baseball for me. The downsides concern the prose, which is littered with some cliched metaphors and sometimes gets a little too homer-y for me. Still, it's a light and entertaining read with a decent amount of insight. The book doesn't seem to be geared towards die-hard statheads (Sawchick even explains what "the count" is at one point) but if you're interested in baseball analytics (but not too interested to the extent that all of this might be old hat for you), Big Data Baseball is worth reading. Despite my few nits, I thoroughly enjoyed it and if you too fall into that intended reader sweet spot (I-liked-Moneyball-and-think-quantitative-analysis-applied-to-baseball-is-interesting-but-I-don't-currently-follow-it-to-an-incredible-extent) you'll probably like it too.
7.5 / 10
Big Data Baseball is organized around the Pirates' aforementioned 2013 season, which saw the Pirates reach the playoffs for the first time in 20 years. This was a surprising result, as the Pirates kept most of their key players and coaches from their disappointing 2012 campaign. While Moneyball and Jonah Keri's The Extra 2% were largely concerned with the best way to draft players and assess talent, the Pirates and their meager budget were forced to put a greater emphasis on how to get the most out of their current roster. This predicament required them to comb through seas of game data through richly detailed sources such as PITCHf/x pitch tracking to identify areas they could exploit to their benefit. Chief among these were frequent and dramatic infield shifts and having pitchers adopt a low pitch count strategy with a heavy diet of sinking fastballs. The Pirates also paid a great deal of attention to pitch-framing, seeing that catchers could cause dramatic swings in wins through their mitt placement, which led them to doggedly pursue catcher Russell Martin despite his anemic hitting performance the previous year.
The book proceeds in a generally chronological fashion and covers the entire 2013 season. Each chapter covers one rough theme, whether it is the value of pitchers inducing ground balls or the impact of pitch-framing, along with a look at how the Pirates and their statistics-based approaches were faring on the diamond. Sawchik presents surface-level analysis of these quantitative conclusions, often using a graph or table and spitting out a few relevant metrics. The focus is always on the conclusions rather than specifics as to how the data was crunched. Everything is explained in a cogent fashion that doesn't require a math degree to understand, though I wish Sawchik went a little more in-depth when explaining some topics.
While Pirates data wonks Dan Fox and Mike Fitzgerald earn a hefty amount of (much-deserved) credit and ink from Sawchik for their efforts, Big Data Baseball cites a handful of other heroes. Manager Clint Hurdle was instrumental in getting his team and organization at large to buy into some seemingly counter-intuitive strategies such as the Brobdingnagian infield shifts, General Manager Neal Huntington kept his faith in Hurdle and also assembled the Pirates' Baseball Informatics department, and players such as the unexpected star pitcher Charlie Morton and pitch-framing extraordinary Russell Martin were vital to executing on the field. Sawchik gives in-depth profiles of these actors as well as a handful of others and they help flesh the book out and make the reader care more about the 2013 Pirates' fortunes. No one is as engaging as Billy Beane (that's admittedly a rather high standard) but there was some personality to the 2013 Pirates.
Sawchik covers the Pirates for the local paper The Pittsburgh Tribune-Review and Big Data Baseball features the usual benefits and drawbacks of a book by a reporter. The big positives are that Sawchik had considerable levels of access to the team throughout the entire season, allowing him to draw from in-depth interviews from all major actors and a huge base of knowledge around the team. Learning about how Clint Hurdle became fascinated by analytics from his stint as an MLB Network talking head and Russell Martin's catching techniques were some of the highlights of Big Data Baseball for me. The downsides concern the prose, which is littered with some cliched metaphors and sometimes gets a little too homer-y for me. Still, it's a light and entertaining read with a decent amount of insight. The book doesn't seem to be geared towards die-hard statheads (Sawchick even explains what "the count" is at one point) but if you're interested in baseball analytics (but not too interested to the extent that all of this might be old hat for you), Big Data Baseball is worth reading. Despite my few nits, I thoroughly enjoyed it and if you too fall into that intended reader sweet spot (I-liked-Moneyball-and-think-quantitative-analysis-applied-to-baseball-is-interesting-but-I-don't-currently-follow-it-to-an-incredible-extent) you'll probably like it too.
7.5 / 10
June 26, 2022
Good book about the 2013 Pirates, who were one of the early adopters of using analytics to make in game decisions. This helped this baby boomer to better understand analytics and it’s role in the game I love.
July 21, 2019
A look at how the 2013 Pirates broke a 19-year losing streak and made the playoffs by using "big data" analytics to improve the on-field play.
January 5, 2019
Could have been a much better book, ends up being more about the personalities surrounding the 2012 - 2014 Pirates attempts at leveraging statistical analysis and basic machine vision to help improve team performance, vs the actual science and techniques itself.
Central in this story is 3 people, Russell Martin, the undervalued free agent the pirates procured during their storied 2013 run, Clint Hurdle, the team manager, and his (soon to be) team of statisticians, starting with, and led by, Dan Fox, who headed the small Pirates analytics/IT department.
The major tools that came out of these efforts were the infield shift strategy and the discovery of pitch framing. I was not very well convinced on the profundity of these two insights. That fact may be the result of my relative lack of interest in baseball as a sport (although I did take a short course on Sabremetrics a few years back). What I don't understand is why no one had experimented with changing defensive field configurations in 80 years? Like with Moneyball I'm led to believe this has a lot to do with the ossified culture of most front office clubs. As a result, Baseball seems like a fairly stagnant game up until the past few years, almost ritualistic.
Also, the author does a poor job of recounting much of anything of the actual techniques which went into the Pirates' analysis. The reader gets that it starts with doing counts of typical trajectories of hits, but the explanation doesn't go much beyond that level. From my understanding of Sabermetrics, the key insight was to understand which metrics, batting averages, OBP, slugging percentages, ISO, strikeout rate etc., were actually linearly correlated with a target of interest, say runs scored for a given team, then to construct metrics from these means and percentages, like Simple Runs Created, that could possibly capture the underlying functional relationship between your target and your metrics. Thus, the real insights of Sabremetrics can be recast as a matter of feature discovery and feature engineering from a modern machine learning or data science perspective.
The key value of Moneyball was that its practitioners were able to discover the subset of valuable metrics from those that were dross and wrap them up into something both nicely understandable, and predictive, like a multiple linear regression. Unfortunately, Big Data Baseball doesn't go much at all into this level, thus the reader doesn't ever feel the full weight of impact of the technique or the toils that went towards constructing them.
The book hints at a more interesting thing in the last segment of the book when it discusses ways data could be utilized to predict professional performance from high school performance, or forecast body morphology and development, but only in brief. Another thing, the title is a misnomer, as none of the analysis recounted in this book would constitute "big data" (something that could not practically be done on a single machine). Perhaps the data from the PitchFX system, but even this amounts to extrapolating flow fields from a video, something a software like Matlab could do on a fairly powerful laptop.
Wish the book went into much more detail, recommended if you are a Pirates fan.
Central in this story is 3 people, Russell Martin, the undervalued free agent the pirates procured during their storied 2013 run, Clint Hurdle, the team manager, and his (soon to be) team of statisticians, starting with, and led by, Dan Fox, who headed the small Pirates analytics/IT department.
The major tools that came out of these efforts were the infield shift strategy and the discovery of pitch framing. I was not very well convinced on the profundity of these two insights. That fact may be the result of my relative lack of interest in baseball as a sport (although I did take a short course on Sabremetrics a few years back). What I don't understand is why no one had experimented with changing defensive field configurations in 80 years? Like with Moneyball I'm led to believe this has a lot to do with the ossified culture of most front office clubs. As a result, Baseball seems like a fairly stagnant game up until the past few years, almost ritualistic.
Also, the author does a poor job of recounting much of anything of the actual techniques which went into the Pirates' analysis. The reader gets that it starts with doing counts of typical trajectories of hits, but the explanation doesn't go much beyond that level. From my understanding of Sabermetrics, the key insight was to understand which metrics, batting averages, OBP, slugging percentages, ISO, strikeout rate etc., were actually linearly correlated with a target of interest, say runs scored for a given team, then to construct metrics from these means and percentages, like Simple Runs Created, that could possibly capture the underlying functional relationship between your target and your metrics. Thus, the real insights of Sabremetrics can be recast as a matter of feature discovery and feature engineering from a modern machine learning or data science perspective.
The key value of Moneyball was that its practitioners were able to discover the subset of valuable metrics from those that were dross and wrap them up into something both nicely understandable, and predictive, like a multiple linear regression. Unfortunately, Big Data Baseball doesn't go much at all into this level, thus the reader doesn't ever feel the full weight of impact of the technique or the toils that went towards constructing them.
The book hints at a more interesting thing in the last segment of the book when it discusses ways data could be utilized to predict professional performance from high school performance, or forecast body morphology and development, but only in brief. Another thing, the title is a misnomer, as none of the analysis recounted in this book would constitute "big data" (something that could not practically be done on a single machine). Perhaps the data from the PitchFX system, but even this amounts to extrapolating flow fields from a video, something a software like Matlab could do on a fairly powerful laptop.
Wish the book went into much more detail, recommended if you are a Pirates fan.
June 10, 2015
Who would have thought a book about math and statistics would be so entertaining? Granted, there is the baseball element that's always good, but reading about math and probabilities and ratios is not a high selling point for some books.
But Travis Sawchik does an amazing job of incorporating the mathematical principals used by the 2013 Pittsburgh Pirates to turn their team around and end a 20-year losing season streak into a dramatic story. He writes of how manager Clint Hurdle got his players to accept the changes.
A lot of the book focuses on defensive shifts made popular when the Cleveland Indians are (wrongly) first credited for moving fielders to one side of second base to deal with Ted Williams. He also writes of pitch-framing by catchers and the changing pitcher's motions for different release points of the ball. Hurdle even debated about going with a four-man rotation rather than a five-man one.
Baseball fans all remember 2013 and how the Pirates began hot and held on. In the past, Pittsburgh teams, if they were decent in the spring, usually faltered by August and resumed their position well behind St. Louis in the Central.
There will be comparisons with Sawchik's book to Moneyball. I felt the writing in Big Data was far more engaging. While Lewis' book was good on stats and written, well, Sawchik is a fan from Pittsburgh, and his heart comes through in this. His writing about Pittsburgh clinching its first winning season and then its playoff birth are very good and entertaining.
This is a must read for baseball fans. While I prefer more biographies on older players, this book is one of the better baseball books I've read in a while.
But Travis Sawchik does an amazing job of incorporating the mathematical principals used by the 2013 Pittsburgh Pirates to turn their team around and end a 20-year losing season streak into a dramatic story. He writes of how manager Clint Hurdle got his players to accept the changes.
A lot of the book focuses on defensive shifts made popular when the Cleveland Indians are (wrongly) first credited for moving fielders to one side of second base to deal with Ted Williams. He also writes of pitch-framing by catchers and the changing pitcher's motions for different release points of the ball. Hurdle even debated about going with a four-man rotation rather than a five-man one.
Baseball fans all remember 2013 and how the Pirates began hot and held on. In the past, Pittsburgh teams, if they were decent in the spring, usually faltered by August and resumed their position well behind St. Louis in the Central.
There will be comparisons with Sawchik's book to Moneyball. I felt the writing in Big Data was far more engaging. While Lewis' book was good on stats and written, well, Sawchik is a fan from Pittsburgh, and his heart comes through in this. His writing about Pittsburgh clinching its first winning season and then its playoff birth are very good and entertaining.
This is a must read for baseball fans. While I prefer more biographies on older players, this book is one of the better baseball books I've read in a while.
August 8, 2015
With the advent of greater statistical and data analysis starting in the mid-1980s, the use of — and tools for collecting — new kinds of information to analyze the performance of baseball players has exploded. Some stats, like OPS, have become mainstream; others continue to be more valued by the data folks than by the announcers and/or fans. The technologies used to collect this information improve every year and more and more major league teams have created their own statistical analysis staffs in an effort to find a competitive edge.
Travis Sawchik's Big Data Baseball tells the story of one of the front-runners in that movement —the Pittsburgh Pirates — and how in 2013, after twenty consecutive losing seasons, that organization made the leap of faith to tangibly act on data about defensive positioning, pitch framing, and pitching patterns in the hope of changing its fortunes.
As most baseball fans now know, the Pirates did turn their fortunes around and, for any Pirates fan — as I am — it's a story worth knowing. In the end, however, while Mr. Sawchik does a good job of explaining the concepts, both in terms of the statistics themselves and the application of those statistics on the playing field, he tends to be repetitive (I found myself checking several times to make sure that I wasn't re-reading a chapter I had read earlier) and his prose is better suited to a newspaper article, rather than a book (which makes sense, because that is his profession).
So, if you're a fan of the new data applications in baseball or a diehard Pirates fan, this is worth your time. But be prepared to cover the same ground several times.
Travis Sawchik's Big Data Baseball tells the story of one of the front-runners in that movement —the Pittsburgh Pirates — and how in 2013, after twenty consecutive losing seasons, that organization made the leap of faith to tangibly act on data about defensive positioning, pitch framing, and pitching patterns in the hope of changing its fortunes.
As most baseball fans now know, the Pirates did turn their fortunes around and, for any Pirates fan — as I am — it's a story worth knowing. In the end, however, while Mr. Sawchik does a good job of explaining the concepts, both in terms of the statistics themselves and the application of those statistics on the playing field, he tends to be repetitive (I found myself checking several times to make sure that I wasn't re-reading a chapter I had read earlier) and his prose is better suited to a newspaper article, rather than a book (which makes sense, because that is his profession).
So, if you're a fan of the new data applications in baseball or a diehard Pirates fan, this is worth your time. But be prepared to cover the same ground several times.
May 8, 2020
Fantastic analytic book. Moneyball 2.0. Doesn't just tell you what the Pirates did--and there is no simple solution (defensive shifting, using more 2-seam fastballs, pitch framing and the acquisition of Russell Martin, integrating the data into a culture of players, finding players like Starling Marte in Latin America, Liriano and AJ Burnett on the scrap heap, Gerrit Cole through the draft)--but where Big Data is going with StatCast. One of the reviewers said it perfectly, does for defense what Moneyball did for offense (OBP and the other hidden advantages in data).
If I have two minor complaints, it is that a) I would have liked a bit more game action mixed in with the analytics about what the Pirates did. Those are often used to tee up discussion about certain players. Also b) would like a better understanding of how other organizations are using this data (something Sawchik neither set out to do nor is positioned to do). Did the Pirates really do things a lot differently then than Rays or Brewers or others?
That's the next challenge. Can someone look at this revolution holistically?
If I have two minor complaints, it is that a) I would have liked a bit more game action mixed in with the analytics about what the Pirates did. Those are often used to tee up discussion about certain players. Also b) would like a better understanding of how other organizations are using this data (something Sawchik neither set out to do nor is positioned to do). Did the Pirates really do things a lot differently then than Rays or Brewers or others?
That's the next challenge. Can someone look at this revolution holistically?
January 25, 2016
Ostensibly the story of the 2013 Pittsburgh Pirates, a team that broke a string of 20 consecutive losing seasons and won back the hearts of the city's football-crazed fans. How did they do it? With data and analytics. Think Moneyball 2.0, with defensive shifts, pitch framing and 2-seam fastballs instead of on-base percentage.
But this is more than a baseball book. It describes the challenge faced by the Pirates' analytics team to convince Clint Hurdle to buy into what they were selling, and Hurdle's challenge in getting the rest of the players and coaches on board. Baseball can be very old-fashioned and slow to change, and the Pirates used this to their advantage. One of the most interesting facets of this book is that the data was really out there for any team to use - the Pirates were just the quickest to buy into it.
If you're looking for a story about managing change in a resistant organization, one that just happens to be about baseball, this is a good addition to your shelf. At times, it felt a little redundant - there probably aren't 200-something pages worth of revelations. But it was a fun read about a fun team.
But this is more than a baseball book. It describes the challenge faced by the Pirates' analytics team to convince Clint Hurdle to buy into what they were selling, and Hurdle's challenge in getting the rest of the players and coaches on board. Baseball can be very old-fashioned and slow to change, and the Pirates used this to their advantage. One of the most interesting facets of this book is that the data was really out there for any team to use - the Pirates were just the quickest to buy into it.
If you're looking for a story about managing change in a resistant organization, one that just happens to be about baseball, this is a good addition to your shelf. At times, it felt a little redundant - there probably aren't 200-something pages worth of revelations. But it was a fun read about a fun team.
January 19, 2017
I got caught up in this one and finished the book with the Pirates' 2013 Wild Card win playing on the TV in the background.
Sawchick, a rookie beat writer in Pittsburgh in 2013, had fortuitous timing as he was able to cover the Bucs during the season that they snapped their 20 year playoff drought. The story is a sort of Moneyball 2.0 as the Pirates had to use analytics to level the playing field against big-spending competitors.
As a close follower of Sabermetrics, I was vaguely familiar with some of the trends that found footing with this organization like pitch framing and defensive shifting but it was great to get the inside story from someone who was there talking to the GM Neal Huntington, manager Clint Hurdle and the quantitative guys like Dan Fox and Mike Fitzgerald.
Now that Sawchick has joined Fangraphs, I'm excited to see what kind of output he'll produce across the entire spectrum of Major League baseball.
Sawchick, a rookie beat writer in Pittsburgh in 2013, had fortuitous timing as he was able to cover the Bucs during the season that they snapped their 20 year playoff drought. The story is a sort of Moneyball 2.0 as the Pirates had to use analytics to level the playing field against big-spending competitors.
As a close follower of Sabermetrics, I was vaguely familiar with some of the trends that found footing with this organization like pitch framing and defensive shifting but it was great to get the inside story from someone who was there talking to the GM Neal Huntington, manager Clint Hurdle and the quantitative guys like Dan Fox and Mike Fitzgerald.
Now that Sawchick has joined Fangraphs, I'm excited to see what kind of output he'll produce across the entire spectrum of Major League baseball.
October 29, 2015
the book that moneyball should have been: sawchik's BDB is a pretty fantastic point by point analysis of how the pirates rebuilt their dismal operation from the data up (and in ways that the moneyball crew were grasping but dimly years ago). this book is: a) blissfully free of game by game recaps, which i fucking hate in sports books and b) constructed out of considerable original research by a former beat reporter who covered the team. bully stuff, except for how clint hurdle (a pretty wretched in-game tactician who places way too much stock in "glue guys") is lionized as some kind of nu-baseball guru. he's more like a dude who didn't stir the soup too too much than some crusty veteran turned visionary (other examples: ned yost, terry collins, art howe).
November 11, 2014
This is like MONEYBALL 2.0. Baseball fans will love this book but people who know nothing about baseball will be able to easily follow the concepts and ideas and will be inspired to look at information in new ways. This is more than a fascinating look at how the Pittsburgh Pirates used big data to exploit a market inefficiency; it's also a call to action to question commonly held assumptions and to build teams that can marry big data concepts (the tech guys) with real-world results (the coaches and players on the field).
December 21, 2014
Got an advance copy of this one. It's a really good, easy read ... basically a sequel to Moneyball, but one with less conflict since scouts and stats act in a greater harmony. Everything is explained thoroughly-- things like the value of pitch framing, shifts, and even heat maps(!)-- and it's simple to grasp. Everything comes together, setting up a nice ending (always like it when the ending ties back to the beginning)
It's not coming out until May, so file it away on your wish lists.
It's not coming out until May, so file it away on your wish lists.
January 3, 2016
The story is as interesting as the one told in Moneyball, but the storytelling is not nearly as compelling. It's dry and lacks any emotion. The author also butchers some of the math and analyses. The book reads like a 235 page magazine article that would have been much better had it ben condensed to 24 pages.
If you don't like stats, Big Data Baseball isn't worth reading. If you do like stats, you'll likely find the inaccurate presentation of the math to be distracting.
If you don't like stats, Big Data Baseball isn't worth reading. If you do like stats, you'll likely find the inaccurate presentation of the math to be distracting.
August 30, 2015
Every beat writer is looking to write their own version of Moneyball. The difference being that Michael Lewis is a fantastic writer. This book was uneven - really felt like a long form SI article with some fluff in the middle to get to 200 pages. Interesting none the less.
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