How Managers Were Fooled by the Home-Run Spike

If you follow baseball at any level, pitch counts are a part of your life. Some people hate them, some people think they need to be more heavily enforced for amateurs. They impact our thinking about pitcher health, durability, and effectiveness. Every broadcast tracks them.

The interest in pitch counts isn’t simply a media/outsider-driven affair, either. Teams have significant financial and competitive incentives to keep pitchers healthy and effective, and it certainly seems like they’ve stopped pushing their pitchers as much within individual games over the last decade.

But even amid this general trend, something significant happened over the last two seasons. Instead of following the long-term trends, pitch counts fell sharply starting last year and have fallen again in 2016. While we can’t offer a definitive explanation without spending time inside the heads of MLB’s managers, the evidence seems to suggest that the culprit is something other than a newfound appreciation for protecting arms.

Let’s get right into the data (through Wednesday’s action for 2016). Here are the percentage of starts which reached a certain pitch count for every season dating back to 1995. There’s quite a bit to discuss in this table, but I’ll call your attention to a few of its most important points.

Percentage of Starts To Reach Certain Pitch Counts
Year 80+ 85+ 90+ 95+ 100+ 105+ 110+ 115+ 120+ 125+ 130+
1995 72.5% 66.3% 58.4% 49.2% 40.1% 31.6% 23.2% 15.8% 10.3% 5.80% 2.97%
1996 71.9% 66.3% 59.1% 50.3% 41.6% 32.7% 23.9% 16.1% 10.0% 5.14% 2.45%
1997 76.1% 69.8% 61.8% 53.2% 42.7% 32.0% 22.9% 14.7% 8.3% 3.91% 1.63%
1998 83.1% 76.7% 68.1% 57.8% 46.8% 34.6% 24.9% 16.7% 10.2% 5.59% 2.73%
1999 82.6% 76.6% 67.9% 58.4% 46.7% 35.1% 24.3% 16.1% 9.6% 4.47% 2.12%
2000 83.7% 77.5% 70.2% 61.0% 49.2% 37.1% 26.7% 16.9% 9.6% 4.12% 1.59%
2001 82.4% 75.7% 67.0% 55.8% 43.3% 31.3% 19.5% 10.9% 4.9% 1.96% 0.56%
2002 83.1% 76.6% 66.9% 55.8% 43.5% 30.5% 19.1% 9.9% 4.7% 1.83% 0.56%
2003 83.1% 76.6% 67.8% 57.0% 43.8% 29.9% 17.9% 9.6% 4.7% 1.83% 0.51%
2004 83.8% 78.2% 70.0% 59.0% 45.0% 30.4% 18.0% 9.4% 3.8% 1.34% 0.29%
2005 85.7% 79.2% 71.2% 59.9% 45.3% 29.6% 16.9% 7.9% 2.8% 0.82% 0.31%
2006 84.8% 79.0% 70.2% 58.0% 43.1% 27.7% 14.5% 6.3% 2.5% 0.68% 0.14%
2007 85.0% 78.5% 69.3% 57.2% 40.7% 26.0% 13.7% 5.8% 1.7% 0.43% 0.04%
2008 86.5% 80.1% 70.7% 58.2% 41.9% 26.0% 13.4% 5.3% 1.5% 0.41% 0.16%
2009 87.0% 81.2% 72.7% 60.3% 44.8% 28.6% 15.3% 6.5% 1.9% 0.56% 0.14%
2010 88.8% 83.7% 75.9% 64.6% 49.7% 33.3% 18.7% 8.5% 2.7% 0.51% 0.08%
2011 88.9% 83.3% 74.7% 63.4% 48.2% 33.0% 18.9% 8.2% 2.7% 0.93% 0.08%
2012 86.9% 80.0% 71.3% 58.4% 43.0% 26.7% 13.1% 5.7% 1.5% 0.39% 0.08%
2013 87.9% 82.8% 74.3% 60.9% 43.6% 26.4% 13.1% 4.6% 1.4% 0.29% 0.08%
2014 89.3% 83.6% 74.5% 61.0% 43.5% 26.1% 12.9% 4.4% 1.2% 0.35% 0.04%
2015 85.3% 78.2% 68.4% 54.4% 38.3% 20.9% 9.5% 3.0% 0.8% 0.12% 0.04%
2016 87.3% 80.7% 70.8% 56.0% 37.6% 20.7% 9.1% 3.0% 0.5% 0.03% 0.03%
SOURCE: Baseball-Reference

First, there’s a clear adjustment over the course of 1997 and 1998, likely as the league adapted to expansion from 28 to 30 teams. With a diluted talent pool, starting pitchers threw more pitches to cover the innings that would otherwise have gone to player who wouldn’t have been on MLB rosters in 1996.

Beyond that, high-pitch-count starts have been trending downward for quite some time. In 2002, 0.56% of all starts went 130 pitches or more. Over the last three seasons, that number has been more like 0.04% of all starts. The 130-pitch outing is virtually dead, basically only occurring when a pitcher is chasing a no-hitter.

But the trend exists at lesser thresholds as well, such as 115-plus-pitch starts. This graph does the trick:

% of Starts of 115+ Pitches

However, while it’s been easy to see the steady vanishing of high pitch counts over the years, there’s a less obvious trend occurring, as well. Over the last two seasons, teams have begun to back off even on the 90/95/100-pitch thresholds. Not only are teams staying away from those really long 125-pitch outings, they’re cutting back on how often they let their pitchers throw 95-plus pitches in a start.

It probably won’t shock you to learn that pitchers are not becoming any more or less efficient on a per-inning or per-batter basis in recent years. Over the last 15 years, pitches per batter faced have been trending up for starters, but there hasn’t been a dramatic change over the last two seasons. Pitches per inning have held relatively steady over the entire sample as well.

Other Starting Pitcher Trends
2002 16.0 94.8 3.71 5.93
2003 16.0 94.4 3.71 5.89
2004 16.2 94.9 3.73 5.86
2005 15.9 95.3 3.70 5.99
2006 16.2 94.4 3.73 5.82
2007 16.3 94.1 3.74 5.79
2008 16.3 94.6 3.77 5.81
2009 16.4 95.3 3.80 5.81
2010 16.2 97.0 3.80 5.98
2011 16.1 96.9 3.78 6.03
2012 16.1 94.9 3.79 5.89
2013 16.2 95.3 3.82 5.90
2014 16.0 95.6 3.81 5.97
2015 16.0 93.1 3.79 5.81
2016 16.4 93.7 3.85 5.72

What we do observe, however, is that, over the last two years, there has been a marked drop in pitches per start and innings per start. Given that pitchers are throwing fewer innings and fewer pitches in tandem, we know that manages are pulling starters earlier, but it’s not yet clear if this is inning driven or pitch-count driven just from looking at this initial data.

Let’s confirm this by looking at the percentage of starts to complete a certain number of innings. If you look at the table below, you see a clear drop over the last two seasons at the five-, six-, and seven-inning thresholds.

Percentage of Starts To Complete Certain Innings
Season 5+ 6+ 7+
1995 78.6% 60.1% 36.9%
1996 79.3% 60.3% 37.5%
1997 80.5% 62.5% 37.7%
1998 81.9% 63.9% 38.1%
1999 80.0% 60.4% 34.2%
2000 80.5% 61.6% 35.9%
2001 82.2% 62.1% 34.0%
2002 82.2% 61.9% 34.7%
2003 81.6% 62.0% 32.8%
2004 81.5% 60.5% 31.8%
2005 84.0% 63.9% 35.1%
2006 81.8% 59.3% 31.0%
2007 81.3% 59.4% 28.9%
2008 81.9% 59.4% 28.9%
2009 82.0% 59.1% 29.3%
2010 84.6% 64.3% 33.5%
2011 85.6% 65.7% 33.8%
2012 82.8% 61.3% 30.4%
2013 83.9% 61.8% 31.1%
2014 85.3% 63.5% 31.4%
2015 82.1% 58.8% 28.6%
2016 81.3% 56.4% 23.6%
SOURCE: Baseball-Reference

The question we want to answer is this: is the league getting more cautious with their arms or are teams responding to what’s happening on the field? You could imagine a scenario in which teams are collectively being more cautious with workloads, deciding to shave pitches or outs whenever possible. But it’s also possible that this is the product of something else and that the lower pitch counts are a symptom rather than a cause.

There isn’t a clean way to test the idea that teams are being more cautious. We could devise a few strategies, but given the multitude of confounding variables, getting a clear picture would be difficult. However, it does seem rather straightforward to study one “happening on the field” explanation.

Over the last 13 months, scoring has been up thanks to an uptick in home runs. It seems possible that, as offense has returned over the last two seasons, that teams have pulled their starters more quickly because they have allowed more runs. There’s a relatively easy test of this, considering that offense spiked right after the break last year. If we compare 2014 and 2015 by half, we can see if this trend started in the second half of last year, right alongside the home-run spike.

Innings Pitched Per Start
Year First Half IP/GS Second Half IP/GS
2014 5.97 5.96
2015 5.90 5.70

When home runs started flying out of ballparks in the second half of 2015, managers started to pull their pitchers earlier. At an individual level, this makes plenty of sense: if you have a pitcher who is allowing more runs, especially more home runs, you’re likely to think he’s having an off night and the other team has his number. No reason to keep him in the game any longer, even if he has a few more pitches left before hitting his usually threshold.

But at the macro level, pulling him is not particularly logical because the individual pitcher isn’t performing any worse relative to his peers, he’s just performing worse relative to what you would have expected before the 2015 All-Star Game.

What’s fascinating about this is that managers are responding to the home-run spike. If you look at earlier seasons, there’s not a clear relationship between scoring and pitches per start or innings per start. And you wouldn’t expect there to be in the aggregate. The decision to pull the starter should be based on his effectiveness relative to the other pitching options, not his un-adjusted in-game performance. But that’s not how it happened. Managers didn’t realize the league was scoring more runs; they all acted as if their starters in particular were losing effectiveness.

Put another way, the home-run increase disrupted managers’ ability to evaluate their own starting pitchers. Instead of recognizing that run-scoring was up and that expectations should be adjusted as a result, managers started pulling their starters in response to the runs they were allowing.

It makes sense from their perspective. How would they know last August that the game was fundamentally changing? It wasn’t an easy change to spot and, at first, it could easily have been brushed aside as a blip. Outside analysts are now confident that the home-run spike wasn’t random, but we still haven’t definitively established the cause. It seems likely that the ball is juiced, but you can’t blame managers for failing to see that at the time.

This gets right to the heart of an idea that fascinates me about human cognition. As a species, we’re extremely good at adapting to changing conditions. We can get used to almost anything. Even things that seem unimaginable now will seem normal after a little exposure. But when it comes to evaluating numeric performance, our minds get anchored to certain numbers we consider meaningful and we have a really difficult time replacing those benchmark statistical levels when the world changes around us. We can adapt extremely well, but we have a hard time adapting baselines that are numeric. Numbers are sticky, it seems.

The interesting question is what happens next. Will managers readjust once they find their footing and get a sense of the new normal? Or will this change be permanent and lead to earlier pulls for starters despite the fact that individual pitchers are no less effective relative to the league than they were two years ago?

This is one of the consequences of a meaningful change to the baseball, intentional or not. If it doesn’t happen out in the open, decision makers aren’t going to be able to respond to the changes in the right way, and by the time they figure out where they went wrong, it might be too late to go back.

Neil Weinberg is the Site Educator at FanGraphs and can be found writing enthusiastically about the Detroit Tigers at New English D. Follow and interact with him on Twitter @NeilWeinberg44.

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Pirates Hurdles
7 years ago

So you’re saying that SP are giving up more runs lately so managers are taking them out sooner? Seems obvious. I just wonder how much of this is general acceptance of the 3rd time through the order penalty and the reality that even MR are more effective than the SP at that point.

OddBall Herrera
7 years ago

Guess you could think of it like this – we’ve got a sense of a ‘quality start’ and how well a pitcher needs to pitch in order to give a team a statistically realistic chance of winning. If the offensive environment changed considerably, we would need to adjust our expectations for a quality start, otherwise we’d risk undervaluing starting pitchers.

Just the same, if managers are giving their pitchers a quick hook because their pitchers aren’t performing well per a now outdated set of expectations, they need to adjust.

7 years ago

Except that the league doesn’t really use long RP’s anymore. Most teams have their designated garbage time multi-inning guy but that’s it. Everyone else is a one-inning guy. They may try to stretch them for an extra couple of outs but they aren’t replacing that ‘3rd time thru order is a problem’ starter with a ‘get through the order once’ reliever. They are replacing him with a ’20-pitch’ guy.

I actually think the short-term (last two or three years) data showing more SP’s being pulled at even 80/90 pitches is just noise. The longer real trend looks to me more like ‘get all starters to 100 pitches regardless and then pull them all regardless’. That is also the interpretation that fits the new roster construction (no long relief and a lot of 1-IP bullpen mouths that need regular feeding).

Jackie T.
7 years ago

The point is basically this: managers are seeing starting pitching performances that give up more runs, because the offensive environment is more robust now, and incorrectly concluding that the starting pitcher doesn’t “have it” that day or has exhausted his quality pitching for the day and pulling them sooner. This is a result of their decision making being based on the previous run-scoring environment, not the current one, and so not the most optimal.

Pirates Hurdles
7 years ago
Reply to  Jackie T.

I guess, but I dont buy that explanation, I think its tied more to trends in data analysis in the game and seeing other teams get more out of optimal RP usage. The Bucs went into 2016 with a plan to manage their week SP by pulling them earlier and it had nothing to do with HR rates.

Jackie T.
7 years ago

I agree. I was just trying to sum up what I understood to be the gist of the article.

I think the author has confused some correlation with causality here. There’s a lot more going on in pitcher usage the last few years than just more home runs.

7 years ago
Reply to  Jackie T.

I don’t think the author confused correlation with causality, because the trend likely isn’t caused by just one factor, but rather many, including analysis of pitching data, injury data, etc. I do think this article is onto something, because even after years of analyzing injuries and trying to come up with a pitch count/innings pitched algorithm, there hadn’t been a significant effect on the workload of starting pitchers until the HR spike. Pirates Hurdles mention that the Pirates went into the 2016 with a plan that had nothing to do with HR rates, but maybe that plan was already influenced by them, given that the rates started to go up after the 2015 All-Star Break.

7 years ago
Reply to  rockbard

there hadn’t been a significant effect on the workload of starting pitchers until the HR spike.

I completely disagree. The most obvious thing I can see in that data is a convergence of starting pitcher workloads around 100 pitches. A dramatic narrowing of the distribution where previously durable pitchers are being shut down at 100 pitches and less durable/capable pitchers are staying in longer in order to get to 100 pitches. Or IOW – this is a statistical example where the mean (P/GS; IP/GS; P/TBF) is going to be meaningless because it is the standard deviation that shows trends in convergence.

The last two years are not the start of a new trend. They are just noise that is occurring on only one side of the distribution (the less durable pitchers being knocked out earlier than 100 and maybe that is HR spike driven).