Testing the Verducci Effect

For some reason, the Verducci Effect seems to be getting a lot of attention right now. I recall it being mentioned in the past, but I haven’t paid much attention to it. The effect is named for Sports Illustrated writer Tom Verducci, who came up with the concept but didn’t pick the name. Verducci uses a set of criteria to identify pitchers who are at risk for injury due to a significant increase in workload. He describes the criteria for selection and rationale in an article published this week.

More than a decade ago, with the help of then-Oakland pitching coach Rick Peterson, I began tracking one element of overuse which seemed entirely avoidable: working young pitchers too much too soon. Pitchers not yet fully conditioned and physically matured were at risk if clubs asked them to pitch far more innings than they did the previous season — like asking a 10K runner to crank out a marathon. The task wasn’t impossible, but the after-effects were debilitating. I defined an at-risk pitcher as any 25-and-under pitcher who increased his innings log by more than 30 in a year in which he pitched in the big leagues. Each year the breakdown rate of such red-flagged pitchers — either by injury or drop in performance — was staggering.

I figured now would be as good a time as any to put off the other important things I should be doing in order to find out if the Verducci Effect is real. I used a sample of major-league pitchers from 1998–2007 to estimate the impact of ratcheting up pitching loads on performance on innings pitched and era, using both their recent major-league and minor-league workloads to predict performance. In some specifications I included the average between the present and past seasons’ performances (Mean IP or mean ERA) to peg a typical performance level for each pitcher. The Verducci Effect was considered to be in force if a pitcher was under 26 had increased his workload by more than 30 innings in the previous year. I also measured the Verducci Effect continuously using the actual number of innings pitched increased before the preceding season. I only looked at performance in the majors, but minor-league workload totals counted toward the Verducci Effect. I estimated the impact using a random-effects estimation technique that controlled for detected serial correlation. The regression estimates are below, but if you’re not familiar with reading such tables you can skip over them and read my write-up that follows.

	 		IP Change	 IP Change	IP Change	 IP Change
Verducci		19.07		22.17		
			[3.18]**	[3.73]**		
IP Change * Under 26					0.23		0.21
							[3.37]**	[3.15]**
IP Change						-0.25		-0.17
							[10.41]**	[7.22]**
Under 26						14.89	17.04
							[4.46]**	[5.29]**
Mean IP			0.06				0.13	
			[3.96]**			[6.98]**	
Constant		-12.23		-4.83		-21.97		-6.61
			[5.78]**	[4.90]**	[8.83]**	[5.98]**
Observations		2383		2383		2316		2316
Overall R2		0.0122		0.0058		0.0379		0.0257
Absolute value of z statistics in brackets				
* significant at 5%; ** significant at 1%				
			ERA Change	ERA Change	ERA Change	ERA Change
Verducci		-0.09600	-0.10295		
			[0.21]		[0.22]		
IP Change * Under 26					-0.00391	-0.00386
							[0.78]		[0.77]
IP Change						0.00611		0.00609
							[3.71]**	[3.74]**
Under 26						-0.24738	-0.25085
							[0.93]		[0.95]
Mean IP			0.47554				0.00684	
			[13.67]**			[0.17]	
Constant		-1.90261	0.49064		0.36013		0.39538
			[8.05]**	[2.98]**	[1.50]		[2.86]**
Observations		2380		2380		2313		2313
Overall R2	0.0707	0.0000	0.0034	0.0038
Absolute value of z statistics in brackets				
* significant at 5%; ** significant at 1%		

The first row of each table measures the straight-up Verducci effect. If you increased your workload by more than 30 innings in the preceding season and are under the age of 26, then we should expect to see a decline in innings pitched and ERA. However, it turns out that this is not the case. In terms of workload, Verducci Effect pitchers actually increased their innings pitched between 19 to 22 innings. In terms of performance quality, pitcher ERAs declined by an average of 0.1 runs; however, the effect was not statistically significant, which means it’s probably best to say there is no effect.

The last two columns of the tables represent attempts to quantify the Verducci effect as a continuous phenomenon; that is, the more your workload increases the stronger the effect ought to be. To do this I used three variables: the change in workload (measured by innings pitched), an indicator of whether or not the player was under 26, and an interaction term that multiplies the change in workload times the under 26 indicator. The interaction term (listed on the second row of each table) captures any difference in performance from workload by Verducci Effect pitchers. For innings pitched, Verducci Effect pitchers increased the number of innings pitched by about 7 innings for every 30 innings pitched. In addition, being under 26 increased expected innings by 15 innings, while the change in workload tended to lower innings pitched for all pitchers by about 8 innings. Thus, the net result for an under 26 pitcher increasing his workload by 30 innings is an increase of about 7 innings pitched. Note these results are all statistically significant, but this was not the case for ERA.

So, where are we? The results do not bode well for the Verducci Effect. Pitchers who were predicted to decline actually improved. One potential problem with this study is that pitchers who pitched no innings at all in a season were not included; however, I think this bias is slight since this number is small, as even injured pitchers normally get in a few innings every season. Frankly, this is about as quick and dirty as you can get with a test; but, it’s a starting point, and I’d like to see others examine the effect further. While appreciate the intuition behind the Verducci Effect, I don’t see much evidence for it.

Update: More Testing of the Verducci Effect

2 Responses “Testing the Verducci Effect”

  1. A few questions:
    1) Is this improvement more or less than what would be expected from the normal aging progression?
    2) Was Verducci saying “expect a decline in performance” or was he saying “these pitchers are more at risk for an injury”? Could your research be masking an increased rate of attrition?
    3) As in finance, the act of observing a trend can in fact cause the trend to disappear since people take actions which allow them to profit from that trend. Since your study covers the period where the observation was made, do your results hold for prior periods?

  2. JC says:

    1) It’s very difficult to estimate this and hard to separate from noise within this group. Furthermore, the VE effect isn’t about differing by age, it’s about falling off, and under-26 players should be improving.
    2) Both. As I mentioned, players who didn’t play at all may be biasing the study, but I expect the effect is small. I think the burden of proof is now on showing the VE exists.
    3) Not really relevant here. If the VE is observed to be real, then we should see teams stop surpassing the 30IP step-up (they haven’t), and those that do should still exhibit the VE. Data availability precludes going back any further. And the sample extends to well before Verducci’s analysis was made.