# Pitcher Aging Curves: Introduction

As on-field performance data has evolved, baseball enthusiasts have been spoiled with more precise measures of player performance. One area in particular is pitcher velocity. Whether through Baseball Info Solutions (BIS) or PITCHf/x, writers and researchers can now add a critical variable into their analysis that wasn’t readily available a decade ago.

Many readers of FanGraphs and Beyond the Box Score have seen Jeff Zimmerman’s position player aging curves. After reviewing them, I started to pester Jeff to see if he considered similar curves for pitchers — specifically in the area of fastball velocity. I was curious about the general pattern of decline for fastball speed and how it impacts overall pitcher performance. Luckily, Jeff already had been thinking about this.

Today, Jeff and I are launching a multi-part series on pitcher aging curves, which is centered on fastball velocity. This introductory article will lay out the methodology we used and — of course — the initial baseline curves for all pitchers, as well as starters versus relievers.

Here are the overall pitcher aging curves:

Let’s take a look at the methodology used to produce the curves:

For the aging curves, we compared pitcher seasons from 2002 to 2011 using fastball velocity (FBv) data from BIS. This means that we have more than just four-seam fastballs mixed into the sample for each pitcher. Ideally, we would have focused just on four-seam fastballs — but given that PITCHf/x only has been around since 2007 — this was necessary to get the volume of data required for the curves.

The same method was used as the one that Jeff previously used to calculate hitter aging curves (all the heavy math is in the linked article). To get the aging amount, Jeff took each of the pitcher’s rates in one year and compared them to how they did the next year. Each pitcher’s change was then weighted by the harmonic mean of the number of innings they pitched between the two seasons. Finally, all the weighted values are added together to get the total amount of change for each metric. Note that the curves are cumulative: For example, when it comes to fastball velocity, pitchers lose a total of 3.75 mph on average during their careers.

Here are the metrics we looked at for this study:

Metric | Description |
---|---|

Velocity | Fastball (FBv) speed in mph |

K/9 | Strikeouts per 9 innings |

BB/9 | Walks per 9 innings |

LD% | Line drives as a percentage of all batted balls |

GB% | Ground balls as a percentage of all batted balls |

FB% | Fly balls as a percentage of all batted balls |

HR/9 | Home runs allowed per 9 innings |

BABIP | Batting average on balls in play |

SWG_Strike | Swinging strike percentage |

FIP | Fielding Independent Pitching |

From time to time, you’ll notice that some of the curves are labeled with “x 10.” This means that — for the purposes of visualizing these metrics side-by-side — we multiplied the cumulative change by 10. Had we not, those curves would have basically looked like straight, horizontal lines.

Some initial thoughts:

— Velocity is a young man’s game. Rather than a parabolic curve of some sort, pitchers generally lose velocity from the beginning. Through age 28, they appear to stay within .5 mph of their peak velocity; but starting at age 29 they have lost about 1 mph with the loss accelerating every year thereafter.

— The loss of velocity is important because we see that pitchers’ abilities to record strikeouts follow a curve similar to the speed of their fastballs. However, the slope of the decline is not as dramatic as the velocity decline. This is perhaps do to a couple factors. First, pitchers are likely to further develop secondary and tertiary pitches as they mature. Many of the best arms in the minors can dominate using mostly their plus-fastball and little in the way of plus- or above-average off-speed stuff. But surviving in the majors requires more than just a plus-fastball — which many pitchers quickly realize. Either they develop additional weapons, or they move on. Second, and somewhat related, pitchers might develop a fastball with additional movement — like a sinker or a cutter — to compensate for the velocity decline. This also could lead to a less steep decline in K/9 and SWG_Strike rate than a pitcher’s velocity decline alone might predict.

And what about batted ball type? Behold:

Even with each batted-ball-type percentage multiplied by 10, it’s a bit tough to see. Essentially, what we observe is that as velocity decreases batting average on balls in play (BABIP) increases. This is because as velocity goes down, the percentage of line drives and fly balls increase at the expense of ground balls. Our guess is that while ground balls generally have a higher BABIP than fly balls, the fly balls being hit off lesser-velocity pitches are likely better hit.

We also observe an uptick in line drives during a pitcher’s age-34 season. This uptick seems to align well with a similar jump in BABIP during a pitcher’s later years.

These initial curves incorporate both starters and relievers. Obviously, this could (and does) introduce some bias into the data, since ineffective starters are unlikely to remain starters as they age. This could artificially lessen the slope of the velocity curve. Don’t worry, though, we have the data broken out by starters and relievers and will be addressing those issues in later articles.

For now, here are the overall aging curves for starters and for relievers:

Starters were coded as pitchers who threw more than 80% of their innings as starters in both year one and year two. For relievers, we used the cut-off of greater than or equal to 66% of innings in relief for both years.

The curves are obviously quite different for both classifications.

First, change in reliever velocity is more tightly aligned with change in strikeouts. Starters seem to be able to better maintain their strikeout rates even as their velocity declines; relievers appear more dependent on their fastball speed to be effective. Here’s that difference in chart form:

Second, walks trend up from the get-go for relievers. So, too, do home runs and batting average on balls in play. In fact, reliever performance in general worsens at a much faster rate than starters, except for velocity. Looking simply at the Fielding Independent Pitching (FIP) curves, between ages 23 and 29, starters on average experience a .54 increase, while relievers average a 1.20 increase.

Let’s take a look at two starting to pitchers to help illustrate the curves. Roy Oswalt and Barry Zito were both 24 years old in 2002, so we have a almost a full career’s worth of data to plot. When we compare their drop in velocity and strikeout rate against our average for all starters, we see that both players follow the pattern we established earlier:

While Oswalt and Zito aged at different rates, the relationship between their velocity and strikeout rates is similar to our average across all starters. During a starter’s age-33 season, the strikeout rate will decline roughly .55 for every 1 mph the pitcher lost in velocity. Oswalt lost about .4 strikeouts per nine innings for every 1 mph his velocity declined by 33; Zito’s strikeout rate fell by .59 for every 1 mph he lost.

During the next few weeks, Jeff and I will publish more articles based on the data and our findings. For example, we’ll look at starters and relievers in more detail — as well as the differences between the two. We’ll also look at what happens when pitchers maintain their velocity from year to year, and how other pitchers deal with velocity losses.

There are lots of things to discuss and untangle, so we would love feedback on the research that’s already done, as well as thoughts about additional analysis.

Bill leads Predictive Modeling and Data Science consulting at Gallup. In his free time, he writes for The Hardball Times, speaks about baseball research and analytics, has consulted for a Major League Baseball team, and has appeared on MLB Network's Clubhouse Confidential as well as several MLB-produced documentaries. He is also the creator of the baseballr package for the R programming language. Along with Jeff Zimmerman, he won the 2013 SABR Analytics Research Award for Contemporary Analysis. Follow him on Twitter @BillPetti.

Has anyone ever looked at curve ball velocities and ever found anything suggestive of anything? (slower curve balls break more and hence are more effective; faster curveballs break sharper and hence are more effective; curve ball velocity or perhaps use fluctuates over time and correlates in such-and-such a way with effectiveness; and so on)

I agree that research would be very interesting.

I did a very rudimentary calculation of sliders and cureball weight values, as compaired to average velocity. There is a positive relationship of weighted value to velocity with breaking pitches, but it doesn’t appear to be as closely related as fastballs are.

https://docs.google.com/spreadsheet/ccc?key=0AppIzP28qdp-dGRKT2s1ZUVna2xCUzFyQ2R0dDl0bnc#gid=0