First-Batter Walks: The Sequel
Last week, I investigated something that I’ve long wondered about: are relievers particularly prone to wildness on their first batter of the game? I didn’t find much of an effect, and I also got tons of valuable feedback about further avenues for investigation. Do base/out states matter? Does handedness matter? Do intentional walks skew the data?
Aside from the last one (a definite yes), I haven’t explored all of these avenues yet. I did, however, answer another question I was curious about, one that ties into the general theme of reliever walk rates. I’ll tell you upfront that I found a confusing result, and that I’d love to hear anything I’ve missed or avenues for further investigation.
Here’s the question I’m answering: when a reliever walks the first batter he faces, what does that tell us about the rest of his appearance? All of us have seen this in practice, and we probably all know the existential dread it engenders. Great, he doesn’t have his command today. How many walks are coming up? Is the lead safe? Will the team even stay in the game long enough for a new pitcher comes in?
To explore this possibility, I examined every game thrown by a reliever since the beginning of the 2015 season. I split each reliever’s appearances into two subsets: every appearance where they unintentionally walked the first batter they faced on one hand, and every other appearance (except intentional walks, I threw those out) on the other. This gave me a sample of 1,085 relievers across more than 80,000 appearances.
From there, I looked at how each reliever did after the first batter they faced in each subset. That’s a confusing sentence, so let’s look at an example. We’ll use Fernando Rodney, because it’s always fun to use Fernando Rodney. Beginning in the 2015 season, Rodney made 44 appearances where he walked the first batter he faced. He also made 275 appearances where he didn’t walk the first batter he faced.
In the 44 walk-first appearances, he faced 145 subsequent batters and unintentionally walked 10.3% of them. A quick note: from here on out, every walk rate I quote will exclude intentional walks, both from the numerator and denominator. I simply threw them out of the sample. Of course, his overall walk rate for the appearance was still atrocious, what with the walk starting every single one and all. In fact, he walked 31.2% of batters in those appearances — 100% against the first batter, then 10.3% on subsequent ones.
When Rodney didn’t walk the first batter, he faced 811 further batters and walked 10.1% of them. Combine that with those 275 non-walks to start the appearances, and that comes out to a 7.6% walk rate. Here, then, we have two clearly different samples: one where Rodney walked 31.2% of his opponents and one where he walked 7.6% of them.
Here’s the thing: after that first batter, his walk rates were nearly identical! It’s relatively clear that his overall walk rate was worse when he walked the first batter, but that’s entirely down to the fact that we specifically selected a sample with walks in it. After that first batter faced, he walked just over 10% in each sample, roughly what you’d expect from a pitcher with a career 11.4% walk rate.
I can flat-out guarantee you that it didn’t feel that way at the time. An opening walk feels like a disaster. And hey, though Rodney was remarkably even between his two samples, he’s just one example. Just because this one pitcher in particular performed roughly equally in the two subsets doesn’t mean all pitchers do.
I weighted each pitcher by the lesser of their two appearance totals. That was almost always the times they walked the first batter, naturally, but just to be safe, I took the lesser of the two. For each pitcher, I created a walk rate differential — simply the difference between their non-first batter walk rates in the two conditions. From there, I came up with the overall population change in walk rate.
Why do it this way instead of simply looking at the overall population walk rate in each instance? Simply put, that wouldn’t work. The two samples aren’t the same. Pitchers who walk the first batter have higher walk rates, on average, than pitchers who don’t walk the first batter. You don’t have to just believe that on faith, because I ran the numbers. In the first-batter-walk sample, pitchers ran a 13.3% walk rate. In the sample of pitchers who didn’t walk the first batter, the walk rate was a comparatively low 9.5%.
That’s not meaningful, though, because as I said above, the samples aren’t the same. We’re not asking whether a first-batter walk means more walks are coming, ignoring everything else. We’re asking this question: for a given pitcher, does a walk to the first batter he faces portend an elevated walk rate for the remainder of his appearance?
That question is why I looked at each pitcher’s differential walk rate across the two conditions — it keeps the proportion of each pitcher in each sample constant. That allows us to measure what we’re looking for rather than capture the fact that wilder pitchers tend to walk more batters — good, great, not what we’re looking for.
With that preamble now completed, what are the results? I won’t lie to you — they shocked me. The average pitcher runs a lower walk rate over the balance of their appearance when they walk the first batter than when they don’t. It’s a tiny margin — 0.15 percentage points — but don’t get caught up in the exact details. The key here is that first-batter walk doesn’t tell us much of anything about how the remainder of the outing will go.
This finding stunned me, so I spent a while looking for explanations. My first thought: maybe the act of walking the first batter changes the game in a way that makes future walks less likely. Having a runner on first, just by itself, might entice pitchers to challenge the next hitter now that first base isn’t open.
To handle this, I adjusted each pitcher’s walk rate based on the base/out states they faced. I tried a simple adjustment — simply adding or subtracting walk rate for each batter faced based on how different that situation is from league average. That moved the results slightly — after this adjustment, the sign of our finding flipped. Adjusting for the situations they find themselves in, pitchers have a higher walk rate in appearances where they walk their first batter — by a still-tiny 0.2 percentage points.
There’s room to wonder about that adjustment, because I’m not taking score into account, or the identity of the pitcher and batter. I’m also skeptical about potential selection effects; perhaps in appearances where the pitcher starts off well, their managers give them enough opposing batters that they eventually tire and start to walk more batters despite starting out with better command of their stuff.
Finally, there’s one massive problem with using walks as our indicator variable. Say a pitcher walks the first batter he faces, then compensates by throwing meatballs right down the middle to the next batter. The next batter promptly homers, a terrible outcome for the pitcher — but one that wouldn’t show up in this test. By looking only at walks, we’re missing a key question about pitcher performance. Having no command doesn’t only mean issuing walks.
These are all potential problems with my findings, and I’m hoping that you will help me find more. Consider this an interactive article — I’d love to hear things I missed, because this puzzle of pitcher walk rates is an itch I can’t stop scratching. I feel like the answer is in there, somewhere, and I simply haven’t happened upon it yet. It can’t be that pitchers walk batters at a roughly equivalent clip regardless of whether they started off with a walk or not — even though the evidence is squarely pointing that way at this point.
So let me know what you think, either in the comments here or wherever you normally talk to me (hi Dad!). I won’t rest until I dig a little bit deeper into this puzzle. Or, fine — I will rest, in the normal seven-to-eight-hour nightly increments, but I’ll think a lot about walks before bed. With any luck, we’ll even arrive at a satisfactory answer!
Ben is a writer at FanGraphs. He can be found on Bluesky @benclemens.
Interesting study. I think you found what I learned from reading The Book years ago: what your gut tells you based on what just happened tells you almost nothing about what will happen in the future. Of course, you never want to walk the leadoff man because putting yourself in a worse base/out state than before is always a bad idea.
Edit: to add, this reminds me of the chapter in The Book talking about starting pitcher performance. Whether a pitcher is “on” or “off” on a given night in the early innings and what it means for his performance as the game goes along. Not the same as your question but a similar question. For a reliever, of course, we have a shorter time to determine if they’re “on” or “off.” I’d love to dive deeper to find if something acted as an indicator. Maybe fastball velocity?
That could be interesting, and I think I could use my existing framework with some pitch-level modifications to check on that.
I’m curious to hear how many pitchers are out there who walk the first batter *more than half the time*.
Going purely from memory, all the ones on my team.
Exactly two in this sample; Justin Lawrence and Brooks Kriske
I wonder if there’s a pitch count effect.
If a reliever comes in and goes full count with a batter and misses a bit for ball four, I don’t think that indicates the command isn’t there – just a product of the situation. If he comes in and walks him on four or five pitches, that might be a different story.
To answer your question about relievers who have bad non-walk results after walking the first batter, the obvious inquiry is wOBA allowed to subsequent batters in each sample, right?
Other ideas:
– percent of appearances allowing at least one run in each sample (often all that matters)
– average number of runs allowed in each sample
– how the above compare to the expected outcome based on base/out state (i.e., does a pitcher who walks the first batter give up more runs over the course of the appearance than you’d expect following a man on first nobody out situation because they “don’t have it?”)
A complicating factor on this would be the possibility of walk-off losses where a pitcher only gives up one run because the game ends. Another issue is pitchers who get pulled before the runs actually score or who have a longer/shorter leash because of what they do with the first batter (another possible inquiry). But maybe over a large enough sample that balances out?
For that complicating factor, I think wOBA makes the most sense — runs are always so weird. I like that idea though.
Yeah wOBA is definitely better. I was thinking about runs because what I usually want from a reliever is to get through the inning without allowing a run.
Here’s a thought, if you’re looking at wOBA after the first batter – does *anything* make a difference? If a pitcher gives up a .300 wOBA overall, is there anything he could do with the first batter that would make him measurably likely to allow a different wOBA (higher or lower) against subsequent batters? For all we know, even a 450-foot HR by the leadoff man doesn’t actually change what we should expect. Or maybe striking out the first batter is a sign that a pitcher has his good stuff that night.
Should you look at the data by further discriminating the base-out state for both the first batter walks and first batter non-walks? Should you discriminate by whether the first batter they faced was a pinch hitter? Should you do the study by calculating wOBA for batters after the first.
It also might be interesting to record the ERA and DRA for each appearance based on walk/non-walk. Maybe they’re not looking so much for chases with a runner on base and therefore the walk rate goes down but the overall batter wOBA goes up. That would likely flag the first batter walk as a clearly bad event.
There are somethings that I think are incredibly hard to figure out if there is an effect or not over the noise (i.e., this is something that if it is real is unlikely to apply to the entire reliever population). It would almost be easier to work backwards. Find pitchers that have a higher walk % after first batter walk than not in odd years; look for commonalities; create a test to see if commonalties are a factor; and test even years to see if data holds.
Not all walks are the same. It it a series of non competitive pitches or is he just missing?Is the umpire’s strike zone tight or loose.?Is it a close game or a blowvout? Is it a pitch around situation?
jw
I agree with the basic point, but wouldn’t that only affect the magnitude of the impact, not whether there is one? Even if some percentage of walks were “good” walks that we knew had no predictive value, there would still be lots of “bad” walks in the sample. The fact that Ben found essentially no effect suggests that those bad walks aren’t predictive either. Or that their effect is so small as to be lost in the noise, which is close to the same thing. If Ben had found a small effect, you could wonder whether it would be larger if you could isolate the bad walks, but in this case it’s more likely that there’s just nothing there.
What about taking into account wind-up vs stretch? If coming in at the start of an inning, that first batter will often be from the windup, but after walking the first batter faced, against subsequent batters the pitcher will go from the wind-up. I know some (a handful? most? almost all? – I really do not know) relievers pitch entirely from the stretch, but maybe there are different effects for those that do vs those that do not.
What’s the overall walk rate with a runner on first vs. bases empty?
I’ve considered something similar when I looked at trying to determine if pitchers have meaningfully different stuff and command abilities between appearances.
I found that it’s very difficult to quantify when pitchers have significantly different levels of command to normal. The only case I found in the 2020 season was Josh Hader’s 5 walk outing on August 29.
Meanwhile the value of a pitcher’s stuff is much more variable between appearances, whether due to atmospheric conditions (Coors), pitch mix or a change in velocity/movement profiles.
Full write up is here:
https://baseballaheadinthecount.blogspot.com/2021/04/are-there-meaningful-differences.html
As mentioned by another user below you could attempt to use wOBA but xwOBA would probably give you a better idea since it will take into account more situations where the pitcher got luckly and gave up a lazer beam hit right at the short stop. However, if you really want your answer (you’re almost certainly not going to like this solution because it’s a giant pain and I would hate to get this advice because deep deep down I’d know it was the only way and I’d have been avoiding it) you are probably going to have to replicate command+ on a per-outing basis for every reliever in the sample and then check if the sample of all reliever outings is bimodal and if so perform a bimodal separation.
Great stuff. First, you may want to control for umps if that is possible. That is, it seems some umps are more or less strick with the strike zone. Other thing, I would love to see the analysis fo starting pitchers as well. That is compare the pitchers, how does their control deteriorate over time? Teams could use this to help decide slotting an arm as relief or starter. Also even in drafting.
Finally, this comment applies to ALL fangraph authors. PLEASE include tests of significance. For example Ben stated “he walked just over 10% in each sample, roughly what you’d expect from a pitcher with a career 11.4% walk rate.” and “pitchers ran a 13.3% walk rate. In the sample of pitchers who didn’t walk the first batter, the walk rate was a comparatively low 9.5%.” I’m a retired stats prof, so my question is the difference between 10% and 11.4% , and 13.3 and 9.5 SIGNIFICANT? Better question would be at what level of signficance (confidence level) is 10% and 11.4% SIGNIFICANT. Stating a difference between any two measures, is largely meaningless with knowing the sample size AND the level of significance. It’s not difficult to do, As long as you have all the data, just run an “Analysis of Variance (ANOVA)”. Thanks for reading this far,
Meg, could you please consider sharing this with all your boys and girls?
I love Fangraphs, and I love’s Ben’s writing even more. Anyone notice Ben’s last name? How about his middle name? OK one more hint, put Ben’s middle and last name together and you have the best American author’s birth name…
Love this stuff!!!
You really shouldn’t look at the performance after the first batter for the entire appearance because there will be selection biases. A pitcher who walks the first two batters is less likely to face a 3rd (or 4th) than one who doesn’t walk the first but walks the second (especially if he retires the 1st). You should just look at the 2nd batters or look at the 3rd, 4th, etc. separately.
Statisticians call that “survivor” bias
This is a fascinating avenue to explore. I can’t say I have the stats-brain to really think of any significant critique [at least at the moment] but I can assure you I will read every article you post about this, and encourage you to keep going. This seems like the start of a really interesting rabbit hole. Here’s hoping to more productive findings!