Baseball Graphs is dedicated to the better use and communication of baseball statistics. Below, you'll find excerpts from, and links to, some of the best baseball writers on the Internet. Follow the links above to read my own intermittent attempts at wisdom (the Baseball Graphs blog), and the heart of this site, historical graphs of every season dating back to 1900.
There are also two special sections you might want to check out. One is the graphical review of the 2003 season, which informed our work at The Hardball Times. The other is the Batted Balls Library, which includes a unique look at batters and pitchers from 2002 through 2005.
I received an email from a high school baseball coach today:
I’m a new assistant high school coach and I am trying to relay the importance of not making errors to my infielders. Do you have or know where I can find a graph, chart, or statistic that shows the more errors you make the less likely you are to win?
You know, I tend to get so carried away with advanced baseball statistics that I sometimes forget the basics. And we should always remember the basics, right? So here’s a graph of every team in major league history, illustrating their fielding percentage (errors divided by total chances) and their winning percentage:
It seems to be pretty simple, right? If you make less errors, your fielding percentage goes up. And, as your fielding percentage goes up, you win more games. But take a closer look.
See, the triangles pretty much flatten out once fielding percentage reaches around .860, which implies that if you average less than four or five errors a game, errors don’t impact winning. But that has more to do with baseball history than anything. Those fielding percentages below .900 primarily occurred in the 1800’s, when some teams registered as many as ten errors a game. Those guys really played with a patch of leather on their hands instead of what you’d call a glove. Today, major league teams average less than one error per game. So let’s draw a different graph that corrects for this data problem.
I created an “error index” for each team, which basically compares each team’s errors per game to the average number of errors per game that year. This way, teams are compared to other teams in similar playing conditions. Here’s a bar graph of the index against the average winning percentage:
As the index goes up, the wins go down. Said differently, if you make more errors than your competition, you’re more likely to lose. Teams that made half as many errors as the competition averaged a winning percentage of .600. Teams that made 50% more errors than the competition averaged under .400.
It really is pretty simple after all.