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A Fan's Guide to Baseball Analytics: Why WAR, WHIP, wOBA, and Other Advanced Sabermetrics Are Essential to Understanding Modern Baseball
Broken up into sections (pitching, fielding, hitting), this authoritative yet fun and easy guide will help readers young and old fully understand and comprehend the statistics that are the present and future of our national pastime.
We all know what a .300 hitter looks like. The same with a 20-game winner. Those numbers are ingrained in our brains. But do they mean as much as we think? Do we feel the same way when we hear a batter has a .390 wOBA? How about a pitcher with a 1.2 WHIP? These statistics are the future of modern baseball, and no fan should be in the dark about how these metrics apply to the game.
In the last twenty years, an avalanche of analytics has taken over the way the game is played, managed, and assessed, but the statistics that drive the sport (metrics like wRC+, FIP, and WAR, just to name a few) read like alphabet soup to a large number of fans who still think batting average, RBIs, and wins are the best barometers for baseball players.
In A Fan’s Guide to Baseball Analytics, MLB.com reporter and columnist Anthony Castrovince has taken on the role as explainer to help such fans understand why the old stats don’t always add up. Readers will also learn where these modern stats came from, what they convey, and how to use them to evaluate players of the present, past, and future.
For instance, what if we told you that when Joe DiMaggio had his famous 56-game hitting streak in 1941, helping him win the AL MVP, that there was, perhaps, someone more deserving? In fact, the great Ted Williams actually had a higher fWAR, bWAR, wRC+, OPS, OPS+, ISO, RC . . . well, you get the picture. So, streak or no streak, Williams should have been league MVP.
An introductory course on sabermetrics, A Fan’s Guide to Baseball Analytics is an easily digestible resource that readers can keep turning back to when they see a modern metric referenced in today’s baseball coverage.
We all know what a .300 hitter looks like. The same with a 20-game winner. Those numbers are ingrained in our brains. But do they mean as much as we think? Do we feel the same way when we hear a batter has a .390 wOBA? How about a pitcher with a 1.2 WHIP? These statistics are the future of modern baseball, and no fan should be in the dark about how these metrics apply to the game.
In the last twenty years, an avalanche of analytics has taken over the way the game is played, managed, and assessed, but the statistics that drive the sport (metrics like wRC+, FIP, and WAR, just to name a few) read like alphabet soup to a large number of fans who still think batting average, RBIs, and wins are the best barometers for baseball players.
In A Fan’s Guide to Baseball Analytics, MLB.com reporter and columnist Anthony Castrovince has taken on the role as explainer to help such fans understand why the old stats don’t always add up. Readers will also learn where these modern stats came from, what they convey, and how to use them to evaluate players of the present, past, and future.
For instance, what if we told you that when Joe DiMaggio had his famous 56-game hitting streak in 1941, helping him win the AL MVP, that there was, perhaps, someone more deserving? In fact, the great Ted Williams actually had a higher fWAR, bWAR, wRC+, OPS, OPS+, ISO, RC . . . well, you get the picture. So, streak or no streak, Williams should have been league MVP.
An introductory course on sabermetrics, A Fan’s Guide to Baseball Analytics is an easily digestible resource that readers can keep turning back to when they see a modern metric referenced in today’s baseball coverage.
240 pages, Paperback
First published May 12, 2020
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Displaying 1 - 30 of 62 reviews
January 22, 2020
Stat heads of the world unite! For those of us who grew up in primitive times when the only batting stats that mattered were batting average, homers, and RBIs, all the modern alphabet soup of new and enhanced statistics is way more than confusing. This book digs into the old stars and shows with examples why batting average and runs batted in are not full measures of a batter’s accomplishments and why ERA and wins and saves are flawed as well.
It then proceeds to explain all of the stats and if you thought on base percentage (OBP) was the only new one, you haven’t been following baseball ⚾️ closely enough. There’s OBP and SLG and OPS and ISO and enough new stats to make your head spin faster than Linda Blair’s. This book in short concise chapters explains them all.
This book is a great resource for those of us trying to follow the conversations that grown mathematically complex. It’s easy to read and filled with great humor. It was hard to put down except that by the end we were inundated with some many new stat measures that our heads spun.
It then proceeds to explain all of the stats and if you thought on base percentage (OBP) was the only new one, you haven’t been following baseball ⚾️ closely enough. There’s OBP and SLG and OPS and ISO and enough new stats to make your head spin faster than Linda Blair’s. This book in short concise chapters explains them all.
This book is a great resource for those of us trying to follow the conversations that grown mathematically complex. It’s easy to read and filled with great humor. It was hard to put down except that by the end we were inundated with some many new stat measures that our heads spun.
October 3, 2020
The author really explains well, inserting humor and examples that make them look a lot more relatable.
February 12, 2020
Anthony Castrovince, whose work has been an absolute delight to read since back when he was MLB.com’s Cleveland Indians beat reporter, delivers again with this outstanding analytics primer that boasts something for fans of every level of knowledge of baseball.
I came to this book expecting something meant for analytics beginners, intending to evaluate it for that audience and not thinking that I, as someone who works in sports media, would get much out of it.
Wrong!
The beauty of this book is that it works to increase the knowledge of any baseball fan, whether newbie or expert, without boring those with more knowledge coming in or intimidating those with less.
Castrovince explains both basic and advanced stats, ideal for everyone from longtime fans seeking a more data-based education on the game to kids just getting into the complexities of baseball to the merely stat curious. And even if you are already familiar with the calculations for both basic and advanced stats, there is plenty of anecdotal content and statistical theory to keep your brain occupied.
Further credit to Castrovince for taking a topic that’s often dry when written about (particularly so for those who are not analytics enthusiasts) and making it fun through his writing. From his signature dropping in of song lyrics and puns to his so-bad-they’re-good dad jokes, Castrovince proves that stats are anything but boring when discussed by the right person.
Bonus: You might even learn something about a non-baseball topic! For example, I finally know what a Whip’s job is in Congress!
In all, a terrific effort that has something for every baseball fan, from self-professed stat nerds to those who are new to the game.
I came to this book expecting something meant for analytics beginners, intending to evaluate it for that audience and not thinking that I, as someone who works in sports media, would get much out of it.
Wrong!
The beauty of this book is that it works to increase the knowledge of any baseball fan, whether newbie or expert, without boring those with more knowledge coming in or intimidating those with less.
Castrovince explains both basic and advanced stats, ideal for everyone from longtime fans seeking a more data-based education on the game to kids just getting into the complexities of baseball to the merely stat curious. And even if you are already familiar with the calculations for both basic and advanced stats, there is plenty of anecdotal content and statistical theory to keep your brain occupied.
Further credit to Castrovince for taking a topic that’s often dry when written about (particularly so for those who are not analytics enthusiasts) and making it fun through his writing. From his signature dropping in of song lyrics and puns to his so-bad-they’re-good dad jokes, Castrovince proves that stats are anything but boring when discussed by the right person.
Bonus: You might even learn something about a non-baseball topic! For example, I finally know what a Whip’s job is in Congress!
In all, a terrific effort that has something for every baseball fan, from self-professed stat nerds to those who are new to the game.
January 28, 2024
I have officially become what old baseball heads hate. Statistics and analytics rule all. Out with the basic stats like batting average, RBI, wins, and errors. We are now on board with wOBA, wRC+, OPS+, ERA+, WHIP, DRS, and FIP. If you’re confused by the random letters I put up there, then I guess you’ll have to check out this book so you can become an intelligent and stat driven baseball fan like myself.
April 21, 2020
If you love baseball and want a richer understanding of sabermetrics, this is the book for you! The author clearly explains the many new methods for measuring the effectiveness of baseball players in all positions. The tone is chatty and genuinely excited, welcoming to all fans. If you're new to sabermetrics, you'll be able to follow Castrovince's explanations. If you're already hooked on sabermetrics, you'll learn details and nuances that will enrich your understanding of the game. Keep this title in mind when you need the perfect gift for the baseball fans in your life—young or old, newbies or sophisticates.
I received a free electronic review copy of this book from the publisher via EdelweissPlus. The opinions are my own.
I received a free electronic review copy of this book from the publisher via EdelweissPlus. The opinions are my own.
June 2, 2024
Castrovince convincingly argued that many of the traditional baseball statistics are misleading, causing us to overvalue some players and undervalue others. He makes the new statistics relatively accessible, though they are unavoidably math-y so I still don't understand as much as I'd like. I finally understand WAR, which is why I got the book, so mission accomplished I guess.
The most incredible part about this book was the continual vindication of Babe Ruth as the best baseball player of all time. Babe Ruth has a compelling case based on the old, outdated, misleading statistics. You might think that with our new, more thorough statistics we would realize that we had overestimated him. In fact, it's the opposite. The new statistics show that Babe Ruth was a better all around baseball player than we ever knew. Using statistics that Babe Ruth himself and all his fans never even heard of, baseball analysts have proven that Babe Ruth deserves his reputation as THE baseball player.
The most incredible part about this book was the continual vindication of Babe Ruth as the best baseball player of all time. Babe Ruth has a compelling case based on the old, outdated, misleading statistics. You might think that with our new, more thorough statistics we would realize that we had overestimated him. In fact, it's the opposite. The new statistics show that Babe Ruth was a better all around baseball player than we ever knew. Using statistics that Babe Ruth himself and all his fans never even heard of, baseball analysts have proven that Babe Ruth deserves his reputation as THE baseball player.
November 18, 2025
Even though I already knew most of these statistics, it was a really fun and good read! I hope more baseball fans who don’t know these new measurements pick up this book!
July 19, 2024
I absolutely loved this book. I love baseball, numbers, and stats and this is all of them rolled up into one. Overall, the book does a great of simplifying some very complicated statistics while also providing context in how they are used. I was familiar with some of the concepts, but this book gave a great appreciation of where we were in analyzing baseball players to where we are now. If you love the game, you owe it to yourself to give this one a read.
November 17, 2025
I'm not sure of the target audience for this book. I'm eager to read books about the analytical revolution in baseball, but this book really didn't teach me very much. In fact, in the 5 years since it was published there have been analytical developments that are not really covered here. Other books (and columns and podcasts) have done a better job.
The greatest problem I have is that the book is too static. It writes about stats as if they are fixed indicators rather than evolving measures for success in a game that is more-and-more embracing analytical methods.
The opening 40 pages are definitely very old news as the author bashes batting average, RBI, defensive errors, pitcher wins and saves. I recall huge arguments about these stats on internet boards in the 1990s and anyone who read Bill James in the 1980s (like me) was easily bored by them even then.
The next section reviews some improved stats, but most of those are not truly "new," even if they are improved. On base average was a key part of Moneyball, published in 2002, and Bill James had emphasized this skill in his Abstracts. Slugging percentage has been an official statistic in both major leagues since the end of WW 2. Runs Created was crafted by Bill James decades ago. The wRC+ and OPS+ stats are slightly improved versions of stats that have long been well-known. Former ESPN columnist (and Bill James protege) Rob Neyer did a great job explaining many of these stats to the masses in the 1990s and early 2000s.
The third section focuses on pitching versions of improved stats with emphasis on ERA+, WHIP (a rotisserie baseball stat from the 1980s), and FIP (tied to DIPS, as the author notes, presented in 1999). DRS/UZR are defensive stats tied to the Fielding Bible, which first started appearing in 2006.
Some of the team stats are not commonly used (SRS?) or are well known (DIFF is a variant of Bill James's Pythagorean formula), and magic number is something I could calculate as a kid. Baseball Prospectus has long included DER.
The final section, which starts with BABIP and builds to WAR emphasizes that virtually all the stats discussed in the book are descriptive rather than predictive. However, the interesting aspect of competing books like The MVP Machine (also 2020) is that baseball has been really interested in predictive measures. The fancy cameras that the author notes in this section can measure pitch movement and spin, which has been used by players and team to design pitches.
Analysts and teams have likewise developed Stuff (and Stuff+) models to predict that the ability to throw pitches with certain characteristics is highly correlated with mound success. I've listened to Eno Sarris on various podcasts for half a decade now and read much of his work on The Athletic.
Some of the newest interesting (and predictive) analytical work focuses on hitters -- bat (swing) speed, swing path, and angle of attack. The author discusses Barrel rates and it seems like batters with certain swing characteristics are much more likely to achieve those highly valued barrels. I can't blame the author for not knowing and writing about this in 2020, but we have known for many years that hard hit rate correlates with slugging percentage. Low strikeout rates for hitters can promote contact -- and if it is hard contact we are probably talking about elite talent.
I wanted more than this book delivered.
The greatest problem I have is that the book is too static. It writes about stats as if they are fixed indicators rather than evolving measures for success in a game that is more-and-more embracing analytical methods.
The opening 40 pages are definitely very old news as the author bashes batting average, RBI, defensive errors, pitcher wins and saves. I recall huge arguments about these stats on internet boards in the 1990s and anyone who read Bill James in the 1980s (like me) was easily bored by them even then.
The next section reviews some improved stats, but most of those are not truly "new," even if they are improved. On base average was a key part of Moneyball, published in 2002, and Bill James had emphasized this skill in his Abstracts. Slugging percentage has been an official statistic in both major leagues since the end of WW 2. Runs Created was crafted by Bill James decades ago. The wRC+ and OPS+ stats are slightly improved versions of stats that have long been well-known. Former ESPN columnist (and Bill James protege) Rob Neyer did a great job explaining many of these stats to the masses in the 1990s and early 2000s.
The third section focuses on pitching versions of improved stats with emphasis on ERA+, WHIP (a rotisserie baseball stat from the 1980s), and FIP (tied to DIPS, as the author notes, presented in 1999). DRS/UZR are defensive stats tied to the Fielding Bible, which first started appearing in 2006.
Some of the team stats are not commonly used (SRS?) or are well known (DIFF is a variant of Bill James's Pythagorean formula), and magic number is something I could calculate as a kid. Baseball Prospectus has long included DER.
The final section, which starts with BABIP and builds to WAR emphasizes that virtually all the stats discussed in the book are descriptive rather than predictive. However, the interesting aspect of competing books like The MVP Machine (also 2020) is that baseball has been really interested in predictive measures. The fancy cameras that the author notes in this section can measure pitch movement and spin, which has been used by players and team to design pitches.
Analysts and teams have likewise developed Stuff (and Stuff+) models to predict that the ability to throw pitches with certain characteristics is highly correlated with mound success. I've listened to Eno Sarris on various podcasts for half a decade now and read much of his work on The Athletic.
Some of the newest interesting (and predictive) analytical work focuses on hitters -- bat (swing) speed, swing path, and angle of attack. The author discusses Barrel rates and it seems like batters with certain swing characteristics are much more likely to achieve those highly valued barrels. I can't blame the author for not knowing and writing about this in 2020, but we have known for many years that hard hit rate correlates with slugging percentage. Low strikeout rates for hitters can promote contact -- and if it is hard contact we are probably talking about elite talent.
I wanted more than this book delivered.
August 22, 2022
This book was a fantastic introduction to all of the statistics that go along with America’s pastime, and a bit of a revelation for me that sometimes bad math makes for good statistics (at least for baseball). While the super clean easy to understand metrics like batting average and wins seem nice and simple, that very simplicity means they can be misleading at best, and downright wrong at worst. Advanced metrics seem like a huge arbitrary mess, and some of the math doesn’t make sense from a mathematical point of view, but the end numbers they spit out do a much better job of evaluating players. Castrovince has found yet another way for me to confuse, bore, and anger my friends and family members with numbers, and for that he has my unending gratitude.
TL;DR there is a reason front office folks in baseball make a lot of money, and I do not.
TL;DR there is a reason front office folks in baseball make a lot of money, and I do not.
November 13, 2025
I for one can admit that before I read this book I was a casual baseball fan when watching games. I didn’t know much and just enjoyed being an average fan. Now that I have read this book, it has definitely gave me a better understanding on how to look at a player based on their stats. I had to pause myself while reading to go on to baseball reference to look up players that I like and compare them to other players for reference to give me a better idea, but I really enjoyed it. The author did a great job of explaining every stat and going into detail, and the humor was decent, which kept me engaged at times.
March 14, 2023
Because of this book there are a few more metrics I will look at when evaluating a player. Many of these metrics are accurate but unnecessary. Sure they can help you evaluate how good (or bad) a player is. But when you are at the ballpark and the stats show up for your favorite player on the scoreboard, 2-3 data points are enough. I would rather read about Mike Trout's HRs, OBP and AVG than his wOBA, wRC+, and xSLG. Just watch the damn game
February 20, 2022
Baseball by the book 051820: Baseball's advanced statistics can be confusing. WAR. FIP. wOBA. wRC. What does it all mean? Anthony Castrovince joins us to discuss how to make sabermetrics accessible to all fans, even those who are most comfortable with traditional metrics like wins, batting average and RBIs.
June 20, 2025
Good intro
April 13, 2024
Just in time for baseball season, I am on a journey to discover the alphabet soup beyond AVG and RBI and HRs that I grew up knowing. Ever since Moneyball, I’ve been interested in learning more about, at the very least, OBP and OPS — I don’t even know what good is for these stats. Fortunately, this is about as good as it gets when it comes to explaining these types of stats (and what qualifies good!) and more.
The problem with our old way of keeping stats is that baseball is such a random game based on luck and a million other variable factors that things like batting average and pitching wins and even saves don’t cut it anymore. These are stats based more on circumstance in many cases than on individual skill level.
“Funny thing about RBIs,” writes Castrovince, “It’s a lot easier to compile them when the guys hitting in front of you get on base.”
So Castrovince sets out to give us new stats that better quantifies the talent of the ballplayers, rather than the situations they inherit based on the random logic of the game. And that starts with, to go back to Moneyball, “Does he get on base?”
“The act — the art, really — of getting on base by any means necessary matters more than ever, and that’s why we turn to OBP. The hitter’s job, basically, is to avoid making outs. OBP tells us how often the hitter is successful at doing so.”
Then add slugging percentage, which gives more context to the bare-bones batting average, because it accounts for doubles, triples, and home runs — all of which are more valuable than singles.
“Slugging percentage takes on the important task of telling us more about what type of hits a player is producing… SLG is one of the best evaluators of power in baseball, because it accounts for more than just home runs.”
A rule of thumb for both OBP and SLG: Avg OBP is .320, average SLG is .420. Therefore, the average OPS (OBP + SLG) hovers around .700 (a really good one is any one that gets close to 1.000).
Throw in a couple more offensive metrics — RC (runs created, or an estimate of a player’s total offensive contribution to his team in terms of total runs); ISO (Isolated Power, or a measure of the raw power of a hitter — not as complicated as RC, it’s simply SLG - BA, where average is .140); and wOBA (weighted on-base average, or a version of OBP that accounts for not just whether a player reached base, but how he reached base — and you round out what I would consider the basics of advanced metrics (even though that’s a bit of an oxymoron).
But Castrovince doesn't stop there. He throws at us a bunch more metrics and stats that account for the defensive and pitching side of the ballgame. And yes, it goes beyond even the most popular metric for pitching, ERA.
First we get ERA+, which accounts for external factors such as ballparks aand opponents (100 is average) to more popular stats like WHIP, something even I can comprehend without a calculator (not really, but still).
WHIP is walks plus hits per inning pitched, which evaluates how well a pitcher has kept the runners off the basepaths, especially important because a pitcher’s fundamental role in not letting the traffic pile up — an important element in run prevention. Average here is 1.30, where the closer you get to 1.00 the better.
FIP also comes up a lot even though it’s harder to calculate. It stands for Fielding Independent Pitching and is “similar to ERA but focused solely on events a pitcher has the most control over — strikeouts, unintentional walks, hit by pitches, and home ruins.”
By limiting the inputs strictly to the events a pitcher has the most control over, FIP is a better tool than ERA, which is influenced on a pitcher's defense or the ruling of a scorer (in the case of errors). Because of this, FIP is good to use for determining a pitcher’s future ERA, or how much he’s likely to improve or regress season to season.
Because it’s so easy to comprehend, I’ll also throw out K/BB or strikeout to walk ratio: 4.00 is excellent, 2.50 is average, and 1.50 is awful.
Castrovince then goes to the metrics to evaluate a ball club, like the Diff (run differential), SRS (simple rating system, which is like the diff but incorporates strength of schedule), and WP, or win probability.
Finally, he ends with giving context stats that add clarity, including those that take into consideration “luck, skill and the total package.” Those include BABIP (batting average on balls in play), xBA, xSLG, and xwOBA (essentially stats that are expected given balls in play), and park factors.
I have to highlight two statistics that are pretty popular these days, starting with WPA (or win probaboity added), again because it’s pretty easy to comprehend (not the case for a lot of these really intricate calculations that back some of these metrics). It’s simply how much aplayer impacted his team’s chances of winning from one event to the next.
And then, of course, WAR, or wins above replacement, which measures a player’s value in all facets of the game by determining how many more “wins” he is worth than a readily available replacement at the same position.
The WaR calculation is a littler tricky, but because it’s easier to understand what WAR accounts for, the numbers we’re really concerned with is the final result: And MVP material is 8 or higher, whereas all stars are anything above 4 (regulars are 2+).
These stats can be conceived as alphabet soup, causing the reader or watcher of the game to throw up their hands and lose interest, but they’re really the foundation of the modern game. And people that understand these stats will really appreciate the game more.
You can stop simplifying the game and start understanding the context and true value of players within a seemingly random, wacky game where a ball in play can bounce an unlimited number of ways. As Castrovince writes, “this modern math strengthens, not severs, that connective tissue. It can help us relitigate past arguments, confirm what we already expected, or uncover something we might have missed.”
In summary, it can give us a greater appreciation and comprehension of the game, putting everything in its proper context. As someone who’s watched baseball for years, grew up playing it, and am still waiting for the Guards to win the coveted Series, this book surely did that for me.
The problem with our old way of keeping stats is that baseball is such a random game based on luck and a million other variable factors that things like batting average and pitching wins and even saves don’t cut it anymore. These are stats based more on circumstance in many cases than on individual skill level.
“Funny thing about RBIs,” writes Castrovince, “It’s a lot easier to compile them when the guys hitting in front of you get on base.”
So Castrovince sets out to give us new stats that better quantifies the talent of the ballplayers, rather than the situations they inherit based on the random logic of the game. And that starts with, to go back to Moneyball, “Does he get on base?”
“The act — the art, really — of getting on base by any means necessary matters more than ever, and that’s why we turn to OBP. The hitter’s job, basically, is to avoid making outs. OBP tells us how often the hitter is successful at doing so.”
Then add slugging percentage, which gives more context to the bare-bones batting average, because it accounts for doubles, triples, and home runs — all of which are more valuable than singles.
“Slugging percentage takes on the important task of telling us more about what type of hits a player is producing… SLG is one of the best evaluators of power in baseball, because it accounts for more than just home runs.”
A rule of thumb for both OBP and SLG: Avg OBP is .320, average SLG is .420. Therefore, the average OPS (OBP + SLG) hovers around .700 (a really good one is any one that gets close to 1.000).
Throw in a couple more offensive metrics — RC (runs created, or an estimate of a player’s total offensive contribution to his team in terms of total runs); ISO (Isolated Power, or a measure of the raw power of a hitter — not as complicated as RC, it’s simply SLG - BA, where average is .140); and wOBA (weighted on-base average, or a version of OBP that accounts for not just whether a player reached base, but how he reached base — and you round out what I would consider the basics of advanced metrics (even though that’s a bit of an oxymoron).
But Castrovince doesn't stop there. He throws at us a bunch more metrics and stats that account for the defensive and pitching side of the ballgame. And yes, it goes beyond even the most popular metric for pitching, ERA.
First we get ERA+, which accounts for external factors such as ballparks aand opponents (100 is average) to more popular stats like WHIP, something even I can comprehend without a calculator (not really, but still).
WHIP is walks plus hits per inning pitched, which evaluates how well a pitcher has kept the runners off the basepaths, especially important because a pitcher’s fundamental role in not letting the traffic pile up — an important element in run prevention. Average here is 1.30, where the closer you get to 1.00 the better.
FIP also comes up a lot even though it’s harder to calculate. It stands for Fielding Independent Pitching and is “similar to ERA but focused solely on events a pitcher has the most control over — strikeouts, unintentional walks, hit by pitches, and home ruins.”
By limiting the inputs strictly to the events a pitcher has the most control over, FIP is a better tool than ERA, which is influenced on a pitcher's defense or the ruling of a scorer (in the case of errors). Because of this, FIP is good to use for determining a pitcher’s future ERA, or how much he’s likely to improve or regress season to season.
Because it’s so easy to comprehend, I’ll also throw out K/BB or strikeout to walk ratio: 4.00 is excellent, 2.50 is average, and 1.50 is awful.
Castrovince then goes to the metrics to evaluate a ball club, like the Diff (run differential), SRS (simple rating system, which is like the diff but incorporates strength of schedule), and WP, or win probability.
Finally, he ends with giving context stats that add clarity, including those that take into consideration “luck, skill and the total package.” Those include BABIP (batting average on balls in play), xBA, xSLG, and xwOBA (essentially stats that are expected given balls in play), and park factors.
I have to highlight two statistics that are pretty popular these days, starting with WPA (or win probaboity added), again because it’s pretty easy to comprehend (not the case for a lot of these really intricate calculations that back some of these metrics). It’s simply how much aplayer impacted his team’s chances of winning from one event to the next.
And then, of course, WAR, or wins above replacement, which measures a player’s value in all facets of the game by determining how many more “wins” he is worth than a readily available replacement at the same position.
The WaR calculation is a littler tricky, but because it’s easier to understand what WAR accounts for, the numbers we’re really concerned with is the final result: And MVP material is 8 or higher, whereas all stars are anything above 4 (regulars are 2+).
These stats can be conceived as alphabet soup, causing the reader or watcher of the game to throw up their hands and lose interest, but they’re really the foundation of the modern game. And people that understand these stats will really appreciate the game more.
You can stop simplifying the game and start understanding the context and true value of players within a seemingly random, wacky game where a ball in play can bounce an unlimited number of ways. As Castrovince writes, “this modern math strengthens, not severs, that connective tissue. It can help us relitigate past arguments, confirm what we already expected, or uncover something we might have missed.”
In summary, it can give us a greater appreciation and comprehension of the game, putting everything in its proper context. As someone who’s watched baseball for years, grew up playing it, and am still waiting for the Guards to win the coveted Series, this book surely did that for me.
September 23, 2024
Definite value as a reference book, but the writing is bad. The jokes are cringe-inducing and it would be more served with story vignettes to make the stats land better. Probably was more valuable in 2020 when it came out, but most of the material at this point is common knowledge for a baseball fan.
June 23, 2025
Easy to read and follow along. Something I will go back to in the future.
October 3, 2020
4.5. This is an good introductory text to the current generation of baseball statistics that seek to provide a much higher level of performance measurement that traditional numbers like batting average, RBI, and ERA. As a novice to most of these, in my opinion this book does a really good job of covering the basics without getting too far into the weeds on the underlying math.
This isn't a textbook-level document, nor is it one that would be greatly beneficial to people who have spent hours of their lives debating which player is better based on comparative WAR or WHIP values. But it was exactly what I was looking for. I went through it slowly and highlighted useful info and quotes so that I can refer back in the future.
A couple of stats I'd not heard of before reading this that I found particularly interesting were Game Score (GSc) and Win Probability Added (WPA), and I also thought the section on Win Probability - which is the basis for WPA and something I've seen on online scoreboards and TV broadcasts - was very interesting. Meanwhile, having heard of BABIP (Batting Average on Balls in Play) before but not really having context, I now consider that stat to be kind of obscure and not terribly useful.
This isn't a textbook-level document, nor is it one that would be greatly beneficial to people who have spent hours of their lives debating which player is better based on comparative WAR or WHIP values. But it was exactly what I was looking for. I went through it slowly and highlighted useful info and quotes so that I can refer back in the future.
A couple of stats I'd not heard of before reading this that I found particularly interesting were Game Score (GSc) and Win Probability Added (WPA), and I also thought the section on Win Probability - which is the basis for WPA and something I've seen on online scoreboards and TV broadcasts - was very interesting. Meanwhile, having heard of BABIP (Batting Average on Balls in Play) before but not really having context, I now consider that stat to be kind of obscure and not terribly useful.
November 1, 2021
A couple years ago I got Keith Law’s book Smart Baseball for Christmas and I began a transformation in the way I think about the game. Over time, I’ve become increasingly convinced that most traditional ways of evaluating players have been seriously limited, or even fundamentally flawed, and that many of the “new” metrics are vastly superior.
Recently, I picked up this book to expand on my understanding of player and team metrics. While much of the material in this book wasn’t new, I did pick up a number of new arguments in favor of the new over the old. I thought the writing was fluid and easy to read for anyone who wants to “big picture”, instead of a more technical approach. Of course, the authors endless attempts at humor and the barrage of puns thrown at you got really tiresome after the 10th or 20th time, but I’m willing to write this off as his particular schtick. But the bottom line is, this is a good book for a casual reader who would like a primer on the new analytics.
I would give this 4.5 stars, if Goodreads allowed for fractional ratings.
Recently, I picked up this book to expand on my understanding of player and team metrics. While much of the material in this book wasn’t new, I did pick up a number of new arguments in favor of the new over the old. I thought the writing was fluid and easy to read for anyone who wants to “big picture”, instead of a more technical approach. Of course, the authors endless attempts at humor and the barrage of puns thrown at you got really tiresome after the 10th or 20th time, but I’m willing to write this off as his particular schtick. But the bottom line is, this is a good book for a casual reader who would like a primer on the new analytics.
I would give this 4.5 stars, if Goodreads allowed for fractional ratings.
February 4, 2025
Baseball analytics, as Anthony Castrovince recognizes, are a complex and controversial subject to many baseball fans. Some fans live by the numbers as the best and only way to watch the game and judge the worthiness of players, reveling in statistics to predict outcomes and dispense advice. Other fans see the stat revolution as a travesty to the game, stripping it of it’s humanity and soul and turning it into a heartless machine. Castrovince’s effort to explain statistics to the layman is an admirable and mostly successful effort at creating an understanding about where these statistics can improve understanding of the game and players and why they are important in a way that accepts most people will not be able to fully grasp the mathematical concepts.
Statistics are often seen by opponents as a way to discredit great players by proving mathematically they weren’t very good, and focus heavily on statistical numbers above the ultimate goal: winning. But Castrovince does an excellent job of taking the time to explain how many of these statistics, examined retroactively, actually enhance and improve the legacy of most of baseball’s legends. There are still a few moments in which the numbers tamp down a certain legendary player, but usually for only a season, rather than a career. Castrovince doesn’t play “gotcha” in an effort to prove one player superior to another like some statisticians can, but rather pleads the case as to why numbers matter in simple, straightforward prose. Some of the statistics still get overly-complicated for a layman and Castrovince is still clearly a numbers guy, but all in all this is a useful reference book for those seeking a simple explanation to what the numbers now dominating the game mean.
Statistics are often seen by opponents as a way to discredit great players by proving mathematically they weren’t very good, and focus heavily on statistical numbers above the ultimate goal: winning. But Castrovince does an excellent job of taking the time to explain how many of these statistics, examined retroactively, actually enhance and improve the legacy of most of baseball’s legends. There are still a few moments in which the numbers tamp down a certain legendary player, but usually for only a season, rather than a career. Castrovince doesn’t play “gotcha” in an effort to prove one player superior to another like some statisticians can, but rather pleads the case as to why numbers matter in simple, straightforward prose. Some of the statistics still get overly-complicated for a layman and Castrovince is still clearly a numbers guy, but all in all this is a useful reference book for those seeking a simple explanation to what the numbers now dominating the game mean.
January 31, 2023
Tremendous read that explains the origin, rationale, and high-level math for the most commonly occurring advanced metrics that are being used to understand baseball these days. I would strongly recommend this to anyone who only has a vague sense of all this WAR, wOBA, and ERA+ business but wants to understand why they are useful and how they can help a fan more deeply appreciate what's happening on the field.
I was one of the people who thought that advanced analytics would take all the fun out of arguing about who's better, but after reading this book, I can see how what they really do is allow for better, more substantive arguments. And I'm all for that.
I was one of the people who thought that advanced analytics would take all the fun out of arguing about who's better, but after reading this book, I can see how what they really do is allow for better, more substantive arguments. And I'm all for that.
September 30, 2024
This book was really cool. It was the only really modern book I could find on the ever-evolving and quickly advancing world of baseball statistics. I liked how everything was explained, broke into sections, and explained with examples from throughout MLB history. As a baseball lover, it’s a great way to really dig deep into understanding the game for more than a full count or a line-drive. I put this on my partially read list because this book reads as a user manual in the way of being about to skip around chapters to answer questions. So, as I have not completely read straight through the book. I say I venture to say I’ll finish it, but I don’t believe my opinion will change! Great book.
October 13, 2024
Nice, great read. Wish more people (baseball fan folk) would give it a try.
I knew most of the formulas and agreed to many of the comments, but I've been a saber-minded person for the last, what? 20 years?
But I loved it, not only because it confirmed most of my understanding of...measuring...baseball performances, but also because it explains everything in very simple ways, with examples, clear language and zero arrogance (you are not dumb if you don't know this statistics, as you are not smart just because you know them).
A very useful tool to, er, convert some old fashioned fans. Could make for a great gift...
I knew most of the formulas and agreed to many of the comments, but I've been a saber-minded person for the last, what? 20 years?
But I loved it, not only because it confirmed most of my understanding of...measuring...baseball performances, but also because it explains everything in very simple ways, with examples, clear language and zero arrogance (you are not dumb if you don't know this statistics, as you are not smart just because you know them).
A very useful tool to, er, convert some old fashioned fans. Could make for a great gift...
December 18, 2025
Start follow MLB in late 2020 and this year I chose to understand more advanced stats so I can become more into baseball geek.
I think I'll reread some chapters to understand it more but thanks to Anthony now I know why Aaron Judged deserve more 2025 MVP title than Cal Raleigh. I still want Cal to win it, but it just show how much I become Judge's hater haha (I mean he's great player, but I'm Dodgers fans so it make sense to dislike Yankees player).
Overall, this book is good if you want to understand more about baseball stats. I'll recommending this book if you understand first the play of baseball (you can watch anime or read manga to understand this one).
I think I'll reread some chapters to understand it more but thanks to Anthony now I know why Aaron Judged deserve more 2025 MVP title than Cal Raleigh. I still want Cal to win it, but it just show how much I become Judge's hater haha (I mean he's great player, but I'm Dodgers fans so it make sense to dislike Yankees player).
Overall, this book is good if you want to understand more about baseball stats. I'll recommending this book if you understand first the play of baseball (you can watch anime or read manga to understand this one).
January 11, 2021
I received this as a gift from a good friend who also loves baseball and, like me, is a baseball traditionalist. Understanding that analytics have been a huge piece of front office strategy since "Billy Ball" became the norm we needed a guide and this is the best. Written with some history and humor it us filled with more formulas than a high school calculus class. I found it educational and somewhat entertaining.
June 23, 2022
This is a really good introduction into some of the newer stats that baseball fans are seeing these days. What I appreciated about the tone of the book was that it was written in plain English. I suppose a stats purist could take issue with the approach, but I really enjoyed it. No, it did not make me an expert on FIP, but it did give me a good sense of why it's important in looking at a pitcher's performance.
November 14, 2020
A great reference book where the appendix alone is worth the price
I’m an avid baseball fan that’s ignored the stats for decades. With a lot more time on my hands I decided I should at least understand what they are and why they are important. This book does that and just a little more without making your head hurt.
I’m an avid baseball fan that’s ignored the stats for decades. With a lot more time on my hands I decided I should at least understand what they are and why they are important. This book does that and just a little more without making your head hurt.
December 24, 2020
Really good book to get old time baseball fans up to speed on the new stats. I liked the rating of when a particular player's stat is great, good or poor. I wouldn't think someone really into stats would like this book as it would be very basic. For those of us who are trying to learn, it's great. Very readable with good humor to boot.
September 13, 2022
Baseball has always been a statistics-rich sport. This is a good book for understanding the newest ones and how they are calculated, without requiring the reader to be a math wizard. The author points you to online resources where you can find the data already calculated. Interesting read for the baseball fan.
May 30, 2023
What a wonderful book for baseball fans. It’s does exactly what it set out to do, educate the reader on baseball’s advanced metrics but not overwhelming the read with mat while also serving as a great reference guide. A must read for any true baseball fan that wants to understand modern statistics. It’s also a quick and easy read. I thank the author for writing this book
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