The Verducci Effect
On Monday, Will Carroll noted that the Verducci Effect was being discussed on MLB Network. On Tuesday, Tom Verducci posted his ten young pitchers at risk of the Effect. Then to top it off, yesterday Josh Hermsmeyer unveiled a free player injury database. I've been meaning to research the Verducci Effect for some time, so this seemed like as good a time as any.
The Verducci Effect, also known as the Year-After Effect, is defined by BP as "a negative forward indicator for pitcher workload," Specifically, pitchers under the age of 25 who have 30-inning increases year over year are at risk. David Gassko's research pointed to the opposite. With pitch by pitch data from FanGraphs and disabled list data from Rotobase, I attempt to expand on Gassko's preliminary analysis, although purely numerical research on injury prediction and pitch limits will never come close to showing conclusive results.
I found 340 pitchers who pitched three consecutive years in MLB at ages 25 and under since 2002. 140 of them fit the Verducci Effect, while 200 did not. Here's the data.
The first point of interest is the decrease in innings pitched for those under the influence of the Verducci Effect. I should preface the rest of this analysis with a few popular credos: TINSTAAPP, regression to the mean, and small sample size. First, pitching is an inherently risky business. Dave Cameron recently wrote a great piece on how successful young pitchers often peak early. This problem is exacerbated by the nature of the Verducci Effect, which dictates that pitchers establish a career high in innings pitched. If you take any group of players who establish a career high in any category, chances are that they will regress to the mean the following year. Finally, my sample again only contains 140 Verducci pitchers. One can't draw important conclusions from a sample of that size. You've been given fair warning.
In general, 25-and-under pitchers improve their peripherals in their third year. Their strikeout rate trends up while their walk rate trends down. Gassko found similar results. I'm not so interested in whether or not young pitchers improve; I'm looking to see where Verducci Effected pitchers differ from other pitchers.
Therefore, the Difference row is the row of interest, as it represents the change from the innings-jump year to the Year After. There are four terms in the Difference row that report different positive/negative signs (besides innings pitched) between each group. BABIP, velocity, whiff rate, and days per DL trip. That Verducci Effected pitchers suffer worse luck based on BABIP and that their counterparts exhibit better fortune speaks to the infallibility of regressing to the mean. I'm not so interested in the contact rate of pitchers, but I decided to further explore the possible velocity and injury aspects of the Verducci Effect. So I turned to the statistical technique of regression analysis.
First, I tried predicting fastball velocity using several separate variables for age, past velocity, and past workload. I've looked at the topic of velocity curves before. Velocity generally peaks during a pitcher's mid twenties. Here are the regression results, which I've broken down by variable type.
Younger pitchers have a .5 MPH advantage over older pitchers in velocity.
Fastball velocity from the previous year has nearly five times as much predictive value as fastball velocity from two years ago.
The previous year's workload helps predict velocity. Throwing a thousand pitches in a year coincides with a drop in velocity of more than a tenth of a mile per hour. This could represent the difference between starters and relievers, in that starters throw more pitches at a lower velocity than relievers. Also, pitchers who have undergone the Verducci Effect have thrown softer than non-Effected pitchers to the tune of 0.3 MPH.
Next, I ran another linear regression to predict days spent on the disabled list in a pitcher's third consecutive year of pitching.
First off, predicting future health is hard. While I was able to predict nearly 90% of a pitcher's fastball velocity without developing a very sophisticated model. The disabled list model explains only 6% of a pitcher's health. Nevertheless, injuries from the previous year are significant, as each trip to the DL tends to yield another several days on the DL the following year.
Age isn't a very strong predictor of future injuries. Pitchers on either extreme of the age spectrum are most at risk, but the results aren't significant. Verducci might've chosen a wise cutoff at age 25, as this table shows that there could well be a point at which pitchers grow less vulnerable.
The Verducci Effect, like most everything else I tested, is not significant in predicting future injuries. Injuries are hard enough to predict as is, and there's certainly no straightforward rule of thumb. A high workload does coincide with a trip to the DL the following year, though the causative effect may be that pitchers who throw a lot of pitches have more opportunities to get injured, rather than the pitches placing more stress on their arms.
Verducci identifies the likes of Felix Hernandez and Josh Johnson as pitchers at risk. Verducci Effect or not, those guys aren't going to replicate their spectacular seasons. But Verducci also points to lesser pitchers such as Homer Bailey and Joba Chamberlain, who failed to live up to their prodigious potential last year. Bailey's fastball velocity leaped up three MPH last year while Joba's velocity dipped by a similar amount. I say if they stay healthy, they both improve on their performance from last year, but chances are at least one of them hits the DL. The data show that workload and age help predict production, velocity, and injuries, but the jury's still out as to whether the Verducci Effect helps explain the nexus between injury and risk beyond what one would expect from young pitchers with taxing workloads.