Measuring This Season’s Most (and Least) Consistent Hitters

© David Richard-USA TODAY Sports

There’s a question that gets asked all the time on baseball social media. The variations are endless, but essentially, it boils down to this: Would you rather have an ultra-consistent hitter in Player X, who you can count on for a daily hit, or an uneven hitter in Player Y, who oscillates between prime Barry Bonds and a benchwarmer?

Given specific numbers, you could work out whether Player X or Y is more valuable. But what if we assume they’re players of equal caliber? That’s where it gets tricky. Maybe I’m only seeing certain answers, but in such cases, it seems like people prefer the clockwork Player X. It makes sense: The prospect of guaranteed production is reassuring, as befits our risk-averse tendencies. I have a hunch that we generally overvalue consistency in baseball, but I’m not here to prove that. Instead, I wanted to find out which hitters have been steady at the plate this season, and which hitters have been mercurial.

Over on our Splits Leaderboards, you can break down hitters’ seasons into weekly chunks. They range from Isaac Paredes’ destruction of the league in mid-June (488 wRC+) to Travis Demeritte’s hit-less and walk-less stretch a month prior (-100 wRC+). From there, measuring the variance between those weeks is a fairly simple endeavor. I grouped the weeks by each player, then calculated the standard deviation in wRC+, which represents how spread apart a player’s weeks are from his overall production. The higher the standard deviation, the more variable he is; the lower the standard deviation, the more consistent.

As uncomplicated as that sounds, there are a few caveats. A week as defined by our leaderboards spans from Monday to Sunday, which is a bit arbitrary and might fail to capture the true ups-and-downs of certain hitters. Another thing to keep in mind is that in order to make sure each week contained a meaningfully large sample, I filtered out weeks with fewer than 20 plate appearances. This affects more hitters than you might think. Even the league’s best hitters occasionally fail to reach that threshold due to a combination of rest days, minor injures, or their team’s schedule. As such, the aggregate weeks aren’t exact representations of what hitters have accomplished this season. But they’re good enough approximations; Aaron Judge is still Aaron Judge, for example.

With all that in mind, it’s time for some fun. By standard deviation, here are the five most consistent hitters of 2022 (with a minimum of 200 total plate appearances):

The Kings of Consistency
Hitter Std. Dev. Mean wRC+
Patrick Wisdom 35.9 130.7
Wilmer Flores 38.8 108.7
Alec Bohm 39.7 68.4
César Hernández 41.4 86.3
Pete Alonso 42.0 158.3

Despite Patrick Wisdom’s whiff-tastic approach, he’s been oddly consistent throughout the season, with only two weeks spent below the 100 wRC+ mark. It also means he’s never been white-hot, but that’s why he claims the throne here. Wilmer Flores has been one of the Giants’ most reliable hitters, which is a compliment to the player but not so much to the team. Meanwhile, Alec Bohm and César Hernández are cases of being bad with regularity – ouch. The real star, however, is Pete Alonso, who basically hasn’t taken a week off all season; he’s great, and so are the Mets.

You Aren't a FanGraphs Member
It looks like you aren't yet a FanGraphs Member (or aren't logged in). We aren't mad, just disappointed.
We get it. You want to read this article. But before we let you get back to it, we'd like to point out a few of the good reasons why you should become a Member.
1. Ad Free viewing! We won't bug you with this ad, or any other.
2. Unlimited articles! Non-Members only get to read 10 free articles a month. Members never get cut off.
3. Dark mode and Classic mode!
4. Custom player page dashboards! Choose the player cards you want, in the order you want them.
5. One-click data exports! Export our projections and leaderboards for your personal projects.
6. Remove the photos on the home page! (Honestly, this doesn't sound so great to us, but some people wanted it, and we like to give our Members what they want.)
7. Even more Steamer projections! We have handedness, percentile, and context neutral projections available for Members only.
8. Get FanGraphs Walk-Off, a customized year end review! Find out exactly how you used FanGraphs this year, and how that compares to other Members. Don't be a victim of FOMO.
9. A weekly mailbag column, exclusively for Members.
10. Help support FanGraphs and our entire staff! Our Members provide us with critical resources to improve the site and deliver new features!
We hope you'll consider a Membership today, for yourself or as a gift! And we realize this has been an awfully long sales pitch, so we've also removed all the other ads in this article. We didn't want to overdo it.

On the flip side, here are the five least consistent hitters of 2022, as determined by standard deviation (with the same plate appearance threshold as before):

The Finicky Bunch
Hitter Std. Dev. Mean wRC+
Mike Trout 114.4 166.3
J.D. Martinez 109.4 133.9
Giancarlo Stanton 108.6 139.5
Yordan Alvarez 108.3 203.8
Owen Miller 108.0 98.3

Hey, the Fish Man is on top of yet another leaderboard! This one’s a bit unflattering, though. Yes, Mike Trout is on pace for another 7 WAR season, but along the way, he ran into the absolute worst slump of his career. And just recently against the Astros, he struck out nine times in a three-game series without recording a hit or a walk. There’s probably no predictive value in a hitter’s week-to-week inconsistencies, but nonetheless, Trout has often been either brilliant or practically unwatchable this season.

It’s been a similar case for J.D. Martinez, who once followed up a 273 wRC+ week (May 23 to 29) with a 4 wRC+ one (May 30 to June 5). Giancarlo Stanton went upstream instead, leaping from a -57 wRC+ to a 227 wRC+ back in late April; since then, he’s mostly settled in. Yordan Alvarez has been more up and down, but his highs are so darn high that they practically quash any small-size slump he runs into. The odd one out here is Owen Miller, who of the five hitters listed is the only one producing at a below-average clip. We’ll talk more about him later.

But what attributes of a hitter are associated with the existence or lack of consistency? You might think hitters with strikeout issues are more volatile than those with strong bat-to-ball skills. Indeed, before collecting the data, my mind instantly went to Javier Báez, who’s a swing-and-miss deity and one of the streakiest players I’ve ever seen. Báez didn’t crack the top 10 in standard deviation – he’s 14th, if you’re wondering – though that might be because his signature home run binges have been few and far between this season. What about the league as whole? From our sample, here’s the correlation between a hitter’s strikeout rate and standard deviation of wRC+:

Surprisingly, the correlation is nonexistent. Strikeout-prone hitters may look ugly when they slump, but it doesn’t necessarily mean they’re more likely to do so. In a sense, Báez is a viable major league hitter because he’s capable of transcendent streaks; without them, all the strikeouts would absolutely crater his numbers. Another (recent) baseball truism is that disciplined hitters remain consistent, but the correlation between walk rate and standard deviation is nonexistent, too. If anything, there’s a very slight positive relationship!

That fact provides a nice segue into the next graph, which shows that better hitters tend to have higher standard deviations:

This isn’t because a lack of consistency leads to good results, of course. But perhaps it challenges traditionally held notions of how All-Stars come to be. We might picture our favorite hitters exuding greatness through a steady stream of hits. In reality, it seems, the very best players are explained by their ability to string together incredible streaks of slugging that appear like blips on a patient’s heart monitor. They’re deviations from the norm, and at the same time, that norm is up there to begin with. Looking at the data, it occurred to me that the highest weekly wRC+ marks often belonged to top-tier hitters. Why is Mookie Betts awesome? Because he’s capable of putting up a 294 wRC+ across an entire week, that’s why.

Lesser hitters, on the other hand, usually lack the tools needed to forge a monster week – and if they succeed, they’re probably no longer considered lackluster anyways (see: Paredes, Isaac). There’s also a positive relationship between a hitter’s standard deviation of wRC+ and average wRC+ because worse hitters are consistently not good. Or I should say, they at least maintain a level of competency deemed appropriate for the major-league level. Once hitters slip below that demarcation, they’re either given less playing time, sent down, or released altogether. So there is a bit of survivorship bias baked into the data we’re seeing, as certain hitters who do fluctuate – between merely bad and cover-your-eyes bad, that is – don’t accumulate enough plate appearances to warrant inclusion.

Then again, it’s possible simple standard deviation isn’t the best method of quantifying consistency. Consider Owen Miller, who we saw earlier. The Guardians’ infielder wasn’t in the top five because of his tendency to shift back and forth. Rather, it was because a single week had an outsized influence on his standard deviation:

Owen Miller’s 2022 by Select Weeks
Week PA wRC+
Apr 11 – Apr 17 26 330
Apr 25 – May 1 27 91
May 2 – May 8 22 142
May 9 – May 15 23 -24
May 23 – May 29 24 34
May 30 – Jun 5 26 120
Jun 6 – Jun 12 27 24
Jun 13 – Jun 19 27 70

Miller had a whopping 330 wRC+ in his first full week of the season. But without it, he resembles a consistently decent contributor, and that isn’t what we’re looking for. In search of a more robust measure, I ended up using the median absolute deviation (MAD), which is the median of the absolute deviations from the data’s median. It’s still simple, but with the added bonus of a resilience against outliers. With a new process, here are the five most consistent hitters of 2022:

The Kings of Consistency, Part Two
Hitter MAD Mean wRC+
Jesús Aguilar 14.9 115.9
Wilmer Flores 25.9 108.7
Jorge Soler 28.1 102.9
DJ LeMahieu 31.8 132.4
Patrick Wisdom 32.3 130.7

And here are the five least consistent hitters:

The Finicky Bunch, Part Two
Hitter MAD Mean wRC+
Kyle Tucker 134.3 136.3
Ryan Mountcastle 128.7 128.2
Paul Goldschmidt 123.1 187.5
Joey Votto 121.7 96.0
Yordan Alvarez 118.7 203.8

It’s hard to say for sure, but I do think this bunch passes the eye, smell, or whatever other sensory test you prefer with higher marks. For example, the most consistent list is now made up of hitters who bounce between the okay and good stratospheres rather than those who are predictably below-average. As for the least consistent list, Votto sticks out this time, but we’re not seeing a repeat of Miller here – this season, Votto started off cold, got hot, and is now trending back down. That’s not what we want as fans, but it is what we want for this article.

Ideally, there’s a method of measuring how often a player dips below and then soars above his baseline output; that might provide us with a more accurate assessment of which hitters are and are not consistent. But I haven’t figured it out, and for now, I’m content with what’s recorded here. Beyond the featured hitters, it’s good to know there’s an unexpected reason behind who tends to be consistent and why. That’s the value of putting assumptions to the test – even if it seems like a fruitless task, you never know what you might end up learning.

Statistics in this article are through the games of July 3.





Justin is an undergraduate student at Washington University in St. Louis studying statistics and writing.

23 Comments
Oldest
Newest Most Voted
NathanMember since 2018
4 years ago

I would be more interested in this with xWOBA or xBA or something that removes the luck factor. For Stanton that -57wRC+ period he had was him scorching balls directly into gloves. I don’t consider that a slump.

sadtromboneMember since 2020
4 years ago

It’s interesting because this is the exact opposite of what we see with projections for a full season. Because of injury, there are a huge number of catastrophic outcomes for total player output (potentially playing through injury, but mostly through missing time). Pete Alonso may be super consistent, but if you wanted to know whether it was more likely if he was going to lose two wins off his preseason projection or gain two wins, you’d pick losing two wins every time. All that has to happen is he sprains his foot, he recovers slowly, and doesn’t come back until it’s too late to make up for lost time.

But this doesn’t apply to the situation here because if someone gets hurt, they don’t register as a zero here, they’re just out of the sample. So the primary reason for underperformance at a cumulative, season-long level isn’t applicable for a rate-basis, week-long analysis. Here, it looks like hitting a ball is hard for everyone, but for people not named Pete Alonso, the successful hitters are guys like Mookie Betts and Yordan Alvarez who can have periods where hitting a ball isn’t hard anymore. Since basically no one has periods where they hit the ball well all the time (except for Mr. Alonso), the variation comes in terms of upside instead of downside.

kylesch87
4 years ago
Reply to  sadtrombone

“Pete Alonso may be super consistent, but if you wanted to know whether it was more likely if he was going to lose two wins off his preseason projection or gain two wins, you’d pick losing two wins every time.”

That’s not how projections work. The likelihood of losing two wins or gaining two wins from the projected total is already part of the projected total. There may be individual players with strange projection graphs, but for most players the projections will give a roughly similar chance of gaining or losing two wins. Otherwise we would expect MLB players on average to underperform their projections, which would mean the projections all needed to be revised downwards until MLB players on average were matching their projections, meaning plus or minus two wins would be roughly equally likely.

This does break down at the low end of the scale where a player projected for 0 wins will almost never receive enough playing time to produce -2 wins but if he does well has a decent chance to get enough playing time for a 2 win season, but that has to do with survivorship bias and team decision making rather than injury and projection volatility. There may also be some upper end of the scale of player talent that acts as a “speed limit” on player WAR, but for most players the chances of gaining or losing 2 wins as compared to projected has to be about equal.

sadtromboneMember since 2020
4 years ago
Reply to  kylesch87

Depends on the projection system, but a more sophisticated projection system does not have normally distributed outcomes around a mean. If you take a look at the percentile outcomes on ZiPs you’ll see what I mean.

kylesch87
4 years ago
Reply to  sadtrombone

So what you’re saying is that if offered the opportunity you would bet the under on every player’s projection? If that would be a winning bet strategy then the projections are all too high, and therefore the system needs to be downgrading the projections as part of the final output. Even if it’s just for every hitter above a certain WAR, as long as you can reliably predict every projection of a certain type to be wrong in the same direction then the system itself is wrong for not correcting for that error. Projections that are more likely to fail in one direction than the other are bad projections.

NATS FanMember since 2018
4 years ago

I think this shows that great hitters destroy bad pitching, but when they face good pitchers, the results are similar to everyone else. Not very good

HappyFunBallMember since 2019
4 years ago
Reply to  NATS Fan

Unless these weekly intervals are connected to specific pitching matchups…which I don’t see claimed anywhere…I don’t think that it shows anything of the sort.

couthcommander
4 years ago
Reply to  NATS Fan

I agree; without any adjustment for pitching matchups, I’m not sure how much this means. It’s a good article though, I’d love to see a followup answering if the difference between great and average hitters is the quality of pitching faced.

NATS FanMember since 2018
4 years ago
Reply to  NATS Fan

If you destroy someone, go like 6 for 6 with several extra base hits, and the rest of your games that week are more average like 1 for 4 with occasional power. Then over a 5 game week your wRC is going to be very high, The next week you go o for 5 then 1 for 4 the rest of a 5 game week your stats will suck. So if you’d the snot out of 1 pitcher every other week you’ll be a star.

Ostensibly RidiculousMember since 2020
4 years ago

You’d rather have Player X throughout the regular season.
But in the playoffs, you probably want Player Y.

airforce21oneMember since 2026
4 years ago

Who is more likely to have a good week/month: Owen Miller (player Y) or Mike Trout (player X)?

LloydMember since 2018
4 years ago

Better hitters have higher standard deviations because the standard deviations haven’t been normalized.

For example, if I played MLB I would have the lowest wRC+ standard deviation in the whole league, because it would be 0 every game.

D-WizMember since 2019
4 years ago
Reply to  Lloyd

Yeah, this is a huge problem with this analysis. Of course the most “inconsistent” hitters are going to be the best hitters. A bad week for a guy with a 120 wRC+ is just going to result in a higher standard deviation than a bad week for a guy with an 80 wRC+. If those two players hit exactly 120 wRC+ and exactly 80 wRC+, respectively, every single week, then the good hitter has one week of 30 wRC+ and the bad hitter has a week of 0 wRC+, this analysis would conclude that the better hitter is more inconsistent, which really doesn’t make any sense.

Last edited 4 years ago by D-Wiz
Apocalypse33Member since 2020
4 years ago
Reply to  D-Wiz

It’s harder to be consistent at an elite level than at a bad level is basically what you’re saying.

Shirtless Bartolo Colon
4 years ago
Reply to  Lloyd

My standard deviation went way up one sunny day in San Diego.

tung_twista
4 years ago
Reply to  Lloyd

Wholly agreed.

Minor nitpick: Last season, out of 114 pitchers with 10 PA or more,
only 26 of them recorded positive wRC+.
Recording 0 wRC+ is hard.

LaBellaVitaMember since 2018
4 years ago
Reply to  tung_twista

The poster was saying the standard deviation of wRC+ is 0, not value of the stat. I’m quite certain I would have wRC+ of -100 every game. Variance = 0.

LaBellaVitaMember since 2018
4 years ago
Reply to  Lloyd

The reason better hitters have higher standard deviations is because, by definition of wRC+, the floor is the same for all player but the ceiling is higher with players with who on average hit better.

Willians Astu-stu-studilloMember since 2020
4 years ago
Reply to  Lloyd

I wrote a fan post back in 2017 to compare the consistency of Aaron Judge and Jose Altuve. (Judge’s lack of consistency was repeatedly brought up in the MVP discussions as a major point against him. It turned out he was actually MORE consistent.) I did similar analysis to this article, but apparently I agreed with your assertion, because I used relative standard deviation as my standard. However, given the slope of the best fit line in that second graph, I think we need a calculation that corrects less for overall quality.

LaBellaVitaMember since 2018
4 years ago

I wonder if such a study may be useful for identifying the types of pitchers for which a batter hits or does not hit well. This question has been raised about Randy Arozarena. At a glance,  more than other similar performing batters, he appears to produce in a number of games with an wRC+ of 400+ along with producing -100 in a slew of games. One might ask, for those games of -100, is there a common attribute the pitchers had for which RA was unable to handle? This would be a check on any study that measures the outcome of RA’s performance on all pitchers with the same common attribute.

JoeyVottoIsGoneMember since 2016
4 years ago

While Joey Votto’s stats may be more inconsistent than ever in 2022, his coolness factor is as consistent as ever and timeless.

Jason BMember since 2017
4 years ago

It is interesting that the “average of weekly averages” can look different for some hitters because we still don’t have a ton of data points (12 or fewer for most hitters)…Alec Bohm’s “average of weekly wRC+” in the top chart is 68 (pretty crappy!) but his YTD wRC+ is 84 (a little less crappy!)

Last edited 4 years ago by Jason B
ab
4 years ago

I think week-to-week ‘consistency’ is kinda silly…I don’t know that anyone thinks it’s much more valuable than an equally good player who has up and down weeks. Perhaps for the playoffs since a playoff series is about a week long? Or perhaps to keep clubhouse morale consistently high by avoiding losing streaks?

But for purposes of run sequencing, I’d rather have a player who is more consistent on a game-to-game basis. I don’t know how I’d quantify this. Maybe by percentage of games in which a player reaches base at least once divided by their OBP? Or, percentage of games in which a player earns at least 2 bases (TB + BB + HBP + SB – CS)*, divided by their total for the season.

*I calculated this figure for the entire league in 2021 and it works out to 1.97 bases earned per player per game. So earning 2 bases per game is about an average performance.

Last edited 4 years ago by ab