Book Review: 2007 Graphical Player

Anyone who has ever drafted in a live fantasy baseball league knows what it feels like to have to make a tough decision within 15 seconds.

This decision—between the injury-prone veteran stud and the elite prospect, let’s say—can help propel you on your way to the league championship or make you draft a fantasy time bomb, waiting patiently for the season to start so he can happily destroy your chances to make the playoffs.

With all the pressure on, and the clock ticking, it’s quite easy to blunder this type of pick.

Down the stretch of a draft—in decisions between four similar pitchers, let’s say this time—there’s no time to look up everybody’s projections, read the fun, snarky but time-consuming comments to the side, and then make an accurate decision. Time is money, and you’re broke. So what do you do?

The 2007 Graphical Player by John Burnson may be your answer.

At first glance, this book doesn’t make sense at all. I am not joking when I say that 90-95% of the book’s pages are just filled with charts and graphs, without much explanation beside them.

It looks much like a Bill James wannabe found a graphing application and some data, and just went nuts, without regard to understandability or practicality.

But then you read the explanations for the charts and graphs, and the book’s usefulness begins to dawn on you. Upon flipping through it, I realized it contained such usefulness that I wondered why nobody had done this before.

I’ve enjoyed playing around with this book so much that I’ll admit to being incredibly biased with it. You should probably keep that in mind going forward. I really like this book and the idea behind its creation.

I should probably mention Graphical Player’s goal – it doesn’t want to be a standalone baseball source. In its own words:

“You will not find a lot of statistics in this book. This book is meant to complement lists of numbers. Moreover, the graphs and charts in this book affirm trends that can be lost in a too-great focus on details.”

There are two sections in Graphical Player – one for the pitchers, one for the batters, both listing players in alphabetical order (teams aren’t even mentioned). At the end of this review, you can read a summary of each graph in the entries.

In addition to the numerous intuitive graphs for each player, there are some other goodies: star maps that graphically compares the biggest names in the league, run contribution graphs organized by position and league, and a fascinating look graphically at team’s overall farm system.

It’s like FanGraphs.com on crack, but it’s always at your fingertips for direct player comparisons and considerations. And it’s there that I find the most use in this tool.

If I’ve got a question in comparisons to Emil Brown and Mike Sweeney, I can quickly flip to their graphs, get a brief but deep look into their past and future, and draw my conclusions from there. If, in a fantasy draft, I know I’ve got three steady arms, I can look at potential draft picks and check to see who has been unlucky, who is a little more risky but underrated, and work from there. Likewise, if I see nothing but risks in my rotation, I can use the graphs to immediately know which pitcher is the most steady from start to start.

One of my complaints is that there is a learning curve in the use of Graphical Player, which goes against the idea of getting a one-look glimpse at a player, but that learning curve is only in the way at the beginning of a reader’s use. Additionally, I would have liked a few more projection-based graphs (perhaps a collaborative work with the Forecaster?), but with a rudimentary knowledge of the way players age, eyeballing projections manually becomes a breeze.

And while I understand the goal of the book is to supplement the Forecasters of the world, I would have really enjoyed some additional sections that helped endear me to the graphs—things like studies or essays that use the same type of stats and graphs found in the book. But, that’s just a personal taste that might not be shared by others. There are, after all, the Forecasters of the world.

Also, I question the use of a full 2/5ths of the batter’s graph on power and speed indexes (more info later on). They seem like simple enough charts that can be shrunk and pushed aside without losing meaning, to make room for another handy graph.

But my few complaints look small when you compare them to what the Graphical Player has achieved. At any one time, with the book open, you can simultaneously witness 8 MLB players in-depth—their weaknesses, their strengths, their past, their future. The book is an impressive feat that truly builds upon projection systems and fantasy guides.

When compared to projection systems for general baseball information, the Graphical Player looks pretty good. When compared to fantasy guides and similar resources, the Graphical Player sets itself apart from the rest of the crowd.

Simply put, it takes baseball stats to a new dimension.

If you’re interested in purchasing Graphical Player, find it in a bookstore or click this.

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IN-DEPTH LOOK AT THE PROFILES

While the book doesn’t necessarily have to be used for fantasy baseball (I intend on having it next to my BPro annual at all times for blogging, arguments with friends, and for fantasy baseball, among other things), I think that fantasy baseball is where the general masses will find usefulness in it. So that’s where my focus will be.

PITCHING SECTION—Sample: Gil Meche

---Age Among Peers (Top row, far left): This is a small bar graph that charts the age of every MLB player at that position (in the pitching section, there are two positions – starters and relievers), where age is the X-axis and the number of players at that age is the Y-axis. The age of the player being profiled is shaded. You can see where a player is in his likely development. This is incredibly useful in eyeballing rising/falling production, and holds a special and obvious usefulness in keeper leagues.

---Career Fortunes (Bottom row, far left): This graph is a line graph, where the X-axis is the pitcher’s hit rate (“the rate at which catchable balls turn into hits”) and the Y-axis is the pitcher’s strand rate (“the fraction of runners who get stuck on base”). The graph documents, more or less, the fortune of a pitcher over his career. If a pitcher has been hanging up around the top-left of the graph, there’s a good chance he’s overrated. Vice versa for the bottom-right.

---Game Log (Top row, middle): Every bar is a start from 2006. The actual gray bars represent the number of pitches for that start. Also, there are three symbols for every game – a strikeout rate, a walk rate and a groundball rate. They aren’t single-game rates, but rolling rates (three games for starters, five for relievers).

---+/- Wins (Bottom row, second from left): Using Bill James’ Pythagorean Theorem, expected wins is calculated, and then it is compared to the pitcher’s actual wins. +/- Wins = Expected wins – Actual wins. So a bar above 0 means the pitcher was expected to have won that many more, and a bar below 0 means the pitcher was expected to have won that many games less. Also, below the bars is the total number of expected wins for that year.

---Encounters (Bottom row, second from right): All MLB teams are organized in order of runs per plate appearance (for this year, the Yankees are at the top and the Pirates are at the bottom). The graph shows how often a pitcher has played each team. (Black bars are AL teams, white bars are NL teams.) The further out the bar, the more often the pitcher has faced a team at this run-scoring caliber.

---Sim ERA (Top row, far right): Using 5,000 seasons of simulations and Retrosheet’s play-by-play data (looking at actual K rates, BB rates, and groundball rates), a pitcher’s likelihood to get a given ERA for the last year is calculated. The further left the bar reaches, the higher the percentage that the pitcher got that ERA. The pitcher’s actual ERA is shaded. Like +/- Wins, this is designed to detect lucky pitchers or underrated hurlers.

---Career Trends (Bottom row, far right): This is a multifaceted graph. Along the X-axis is our pitcher’s age (20 years old to 45). Each year is a new bar, which goes up to the number of pitches in that year he had thrown. The two trend lines are as follows: Something called GOG is calculated (a metric of pitching value). Strikeout, walk, groundball, flyball and line drive percentages are used to calculate a general value for ERA, WHIP and Wins. Think of the final GOG3 number that’s charted like dollar values in an auction league – it combines those three stats into a general overall value. GOG4, the next line, includes strikeouts into the “dollar value”.

---Prognosis (Very bottom line): A few words on the player in question. On that far right side, they give predictions for better, worse, or similar stats (up, down arrows, or horizontal line, respectively). Also, the right box is their opinion on that player’s value – a dollar sign means he is under-valued or reasonably-valued; a horizontal line means he is over-valued to some degree.

BATTING SECTION (what’s different from the pitching side of things, anyway)—Sample: Mark Teahen

---Games Played by Position (Bottom row, far left): Those gray boxes represent a baseball field. The number in each spot of the diamond is the number of games played there for our batter. In the example, Royals third baseman Mark Teahen played 109 games at third, and didn’t play games anywhere else. Also included is a PA Snapshot, which is simply a graph of the number of PAs for each year of the last 3 years.

---Game Log (Top row, middle): Similar to the game log for pitchers, every bar across the graph is a game played. The height of each bar was the number of plate appearances for each game. There are three lines that are charted across the log, too: batting average, strikeout rate, and power index (more on PX later). Batting average is a season up-to-date number, while K% and PX are 20-game rolling rates.

---Career Trends (Bottom row, middle and far right): One graph is the power index, the other is the speed index. The X-axis for both is the age of our batter, the Y-axis is the appropriate index number and a counting stat (you’ll see). Power index (PX) is derived from “our recipe for long hits per ball”. For PX, the gray bars are the number of home runs for that year and the line with circles is the level of the hitter’s PX for that year. Speed index (SX) “is simply an estimate at which a hitter tries to steal (successfully or not).” The gray bar is stolen bases for the year, the line with circles is the level of the hitter’s SX for the year. The dashed lines for both is the average index number for other players at the hitter’s position.

---Production (Top row, far right): Each bar is OPS. The far left is the player’s OPS for the last year, to its right is the OPS for other players at his position, to its right is the player’s OPS vs. right-handed pitchers, and finally is the player’s OPS vs. LHP. The black bars below each OPS is the amount of plate appearances for the year.

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[I think that an in-depth look at those graphs is essential for understanding a book like this. I hope I didn’t get too wordy or in-depth.]