Neuroscience Can Project On-Base Percentages Now
I have an early, hazy memory of Benito Santiago explaining to a reporter the approach that had led to his game-winning hit moments earlier. “I see the ball, I hit it hard,” said Santiago in his deep accent. From which game, in what year, I can’t remember. Also, it isn’t really important: it’s a line we’ve heard before. Nevertheless, it contains multitudes.
We know, for example, that major-league hitters have to see well to hit well. Recent research at Duke University has once again made explicit the link between eye sight, motor control, and baseball outcomes. This time, though, they’ve split out some of the skills involved, and it turns out that Santiago’s deceptively simple description involves nuanced levels of neuromotor activity, each predictive of different aspects of a hitter’s abilities. Will our developing knowledge about those different skills help us better sort young athletes, or better develop them? That part’s to be determined.
A team of researchers spread across Duke ran baseball players from two full professional organizations through a battery of nine tests on Nike Sensory Stations to measure different aspects of a player’s sensory motor abilities. After creating something similar to Major League Equivalency lines for each player, the researchers were able to test the effect of each of the scores against real-life baseball outcomes.
“If you have a 23-year-old, completely average outfielder, the model predicts that his on-base percentage in the major leagues would be .292,” explains Kyle Burris, one of the researchers on the project. “The model would expect a similar player who scores one standard deviation higher on the perception span task to have an OBP of .300.”
The high-level, easy takeaway from their study is that these skills, taken as a whole, are predictive of good plate discipline. There was no link to slugging percentage, though, so we’re not quite yet predicting full batting lines from your neuromotor scores.
But if you drill down a bit into these new findings, you’ll see that there is a great deal here to get excited about. Here’s a profound image that shows how each subsection of the larger skill set was linked to baseball outcomes. Darker colors denote a stronger relationship between the skill and the baseball statistic.
Take a look at the row labeled “perception span,” in particular, and you find an interesting story. That task was linked to good on-base percentages and strikeout rates, but not necessarily good walk rates.
“It’s kind of like a game of Simon,” says Burris as he tries to explain the perception-span task, “but for a split second, it gives you shapes that appear in various aspects of your peripheral vision, and you have to determine was there a square there, or a pentagon there, and it flashed at you in a split second and you have to try and remember what the shape was.”
When we asked players what they see when the ball is released, a good portion of the responses detailed how little is ultimately visible to the eye. And there’s that study of cricket which suggests that cricket players get more from information they gather before the release of the ball than after. This finding fits right in: players who are good at noticing things on the periphery — like the way a forearm might look different on a breaking ball, or the way the body might drag on a changeup — are better at making contact.
Hidden within the other differences between the tasks and their links to outcomes is a similar story: both the ability to suss out quickly the difference between shapes seen both near and far, and also to capture a target quickly were both good for making contact. That makes sense.
But why would hand-eye coordination be better for player’s walk rate than his strikeout rate?
Partly, this could be because players have to start their swing before they know if they want to swing — a requirement velocity puts upon them — and hand-eye coordination helps them to better stop that swing if the pitch is a ball.
Partly, this could be a result of the limited capacity for actually testing hand-eye coordination. The particular task linked to that number requires respondents to tap baseballs as they appear on a screen, testing how fast they can do so.
“I’m not sure that it actually goes and tests hand-eye coordination,” admitted Burris, who is headed to Cleveland for a summer internship with the Indians. “There is a little bit of hand-eye coordination in that you have to see it and then immediately translate that to a motor response, but I’d say that that was almost response-time-esque.”
If you look at the separate reaction-time outcomes, you’ll see a similar link to walk rate, so maybe that’s the key skill in taking walk. Reacting quicker.
Or there’s another way to separate the skills. You could consider the first three tasks — visual clarity, contrast sensitivity, and depth perception — as “hardware.” They’re linked to outcomes, of course, because there’s a decent part of the game that requires good eye sight. But they’re the sort of thing with which you’re born.
“There will never be a blind ballplayer,” said co-author Gregory Appelbaum.
Those other six tasks, though? They represent the software of our neuromotor system. They represent our ability to take the visual information given to us and process it. Software is more malleable, subject to updates. Software can be changed for the better.
“There is evidence that these processes can be improved,” agreed Appelbaum. “There have been demonstrations of neuroplasticity in these processes.”
Appelbaum pointed to two interesting studies that pointed to the fact that our neuromotor system’s software could be trained. A study from 2015 of which he was part showed that “significant learning was observed in tasks with high visuomotor control demands but not in tasks of visual sensitivity,” for one.
A 2014 study at the University of California-Riverside found that actual baseball outcomes could be improved by using a “perceptual learning program.” In that study, players reported improvements such as being able to see further, and having eyes that felt stronger and didn’t tire as quickly.
Appelbaum is ready to find out what these visual training technologies will look like as we go forward. He’s helping launch the Duke Vision Sports Center, a clinic and lab where researchers will use sensory stations, immersive reality, and more, in order to pursue this line of thinking.
When it comes to new stats coming out of Statcast, I’ve personally seen a change in how players assess the numbers. Early distaste has given away to curiosity, as more players — Yonder Alonso and Andrew Heaney, for example, in my own experience — now speak up at the end of interviews to ask me about launch angle, exit velocity, and how they can use that data to train and improve.
So, while the Boston Red Sox have long been using the link between neuromotor skills and baseball outcomes in their minor leagues in an effort to bring “neuroscouting” to their own organization, these new findings offer a different use for neuromotor study. Instead of sorting players, there’s major potential to use these activities to develop players and get the most out of them.
There may never be a blind baseball player, sure. But that’s just hardware. Let’s see how we can make the most out of our favorite player’s software.
With a phone full of pictures of pitchers' fingers, strange beers, and his two toddler sons, Eno Sarris can be found at the ballpark or a brewery most days. Read him here, writing about the A's or Giants at The Athletic, or about beer at October. Follow him on Twitter @enosarris if you can handle the sandwiches and inanity.

Interesting idea, can we also ban spammers? (I.e tanya, they have been spamming many articles)
Can someone explain “intercept” in the chart above?
The intercept is what you would get if all the variables are 0. Obviously age is a very important factor here and nobody is anywhere near age 0, so it’s worthless.
Mr Appelbaum, I am afraid you are mistaken
http://www.sportsnet.ca/baseball/mlb/toronto-blind-jays-hit-road-beep-baseball-world-series/
This is cool stuff; neurosensory capabilities are growing quickly – even more information to parse on deck
Please take this as my ignorance rather than condescension, but shouldn’t we be concerned about data (or the fitting of data) that produces a -7 to 7 range of z-scores?
I gave it a very quick read and obviously, you use the data you have, but setting the minimum at 30 at-bats seems very light. I didn’t see the full data table anywhere.
They appear to be getting an correlation coefficient of -1.295 (what?) for catchers/obp while getting a .159 for catchers/bb which hints to me that their 19 catchers were comprised solely of JP Arencibia clones.
As an aside, I think this is all really freakin’ awesome and I hope it expands to a baseball combine where we get 1000s of data points because I’m sure there’s a whole lotta predictive stuff in there.
You’re misinterpreting the chart. These “z-scores” represent the number of posterior standard deviations above zero that the posterior means of the coefficients in the Bayesian linear model are. Think of these as z-values (or t-values) of coefficients in a multiple linear regression; the greater the magnitude, the more significant the coefficient.
For catchers, note that the baseline category here is outfielders. Comparing catchers to outfielders, catchers in the sample tended to have significantly worse on-base percentages, but slightly better walk rates and strikeout rates.
Thanks for the prompt replies. I see the chart in context now.
With that said, some of the assumptions you’ve made about the data after 30 ABs doesn’t seem to hold true when compared to MLB data for the past year, three years, or five years.
Maybe it’s true given the entire population, but I can’t get it to pass the sniff test on the mlb data.
I also understand that it’s probably annoying to explain yourself to someone who is barely speaking the same language, but why would batting average be left out entirely? It’s clearly a component of OBP, yet it’s the most likely to suffer from noise. It seems like such an obvious inclusion, that it’s exclusion makes me curious.
Can’t believe a researcher would make the claim that ‘There will never be a blind ballplayer.’ without directly siting and providing evidence to the contrary of Sam Miller’s tremendous work on the subject.
They are all diverted into the umpiring program.
1. This data is amazing and a brilliant article.
2. I can’t help but worry that it starts to become deterministic. How far away are we then from sticking kids into these tests and then a psych evaluation to determine make up and then that’s your player evaluation. I’m not sure I’ve explained myself well but I’m not sure teams or any employer should have acces to what goes on inside your head
100% on number two. Makes me nervous. But this is mostly something that happens once you are drafted, so hopefully we can steer this towards the development side rather than ‘trade away all the guys who score badly’ type of neuroscouting.
At least the way the model is right now, I don’t think teams will yet because there’s still so much unaccounted for in the model. I.e. catchers have a negative value because they are worse hitters than outfielders even more so than the other variables account for. This shows selection bias as you can only get onto these teams as an OF if you’re a good hitter, but if you’re a catcher you’re allowed to be worse.
vi800, it is unclear if catchers have lower OBP because of actually having worse perception/reaction time or because of other reasons (i.e. physical demands of squatting, large devotion of time spent with pitchers and going through pitching strategies, etc.). These could potentially have a mediating effect (physical demands effect reaction time which in turn effect OBP). Regardless, you are totally right about the selection bias and rating catchers on neurosensory measures would have to be done a positional basis (comparing catchers to catchers, shortstops to shortstops, etc).
To give some context to the rest of this comment, I am an active researcher (finishing my PhD) in the field producing this research. The full article does not seem to be available yet, but I have some notes of caution based the available information.
The first, and trivial, this is not neuroscience. They did behavioral tests. Saying this is neuroscience is a buzzword that many people in the field use to “sell” their work.
More importantly, this is exploratory science (from what I can tell) and no strong conclusions should be made from it. Measuring performance on a battery of tests and correlating them all with a handful of outcomes (in this case, baseball performance) is well known to increase the possibility of a false positive result. Good science dictates that they should have collected data from a second group of players specifically looking at the factors the first study indicated mattered. In other words, look specifically at at perceptual span and OBP, and nothing else.
Finally, this statement, “There is evidence that these processes can be improved,” agreed Appelbaum. “There have been demonstrations of neuroplasticity in these processes.” is highly controversial. For one, good evidence that getting better at a computerized test generalizes to everyday life is non-existent, from what I know. Note that Luminosity, claiming to improve these processes, was fined $50 million by the FTC for deceptive advertising (only paid $2 million, which is obnoxious). It is also interesting to note that Appelbaum worked with Stephen Mitroff while Mitroff was funded by Nike. Furthermore, Appelbaum is currently partially funded by the Department of Defense, likely to find ways to improve these skills, so he has a compelling reason (money) to support this claim that has nothing to do with the science behind it.
Nonetheless, this is intriguing research and I look forward to where it goes! The Yankees recently posted a job ad looking to hire researchers to develop computer based player evaluation methods, which is much fun.
Psychologists calling themselves neuroscientists (or other more “hard” sounding scientists) to fluff themselves up is a long and distinguished tradition.
It’s kind of the opposite of how psychologists who do fun, headline-grabbing research are called “behavioral economists” despite making no claim to it at all.
Personally, I think psychology is interesting enough on its own, at least as interesting as those other fields. But I’m also not a taste-maker.
Agreed! It happens because in many cases one needs to prove they can get grants to get tenured. Neuroscience and behavioral economics are more readily funded that psychology.
I should point out that I am not a bitter academic just lashing out. I have been reasonably successful, but do get annoyed about people publishing what is more or less incomplete work.
Put the neuroscience one on us, neither of the people I talked to used that word. Neuromotor, perhaps.
No worries, like I said it is a trivial point. The work is intriguing regardless of what name you give it! Thanks for the good work!
To the ethics point, I think it is absolutely insane that Nike collected this data and passed it along to someone who has an internship with the Cleveland Indians lined-up.
The assumption that you cannot put a name to a slashline, age, level (etc) is absolutely ridiculous. It would literally take you 1 second in Bill Petti’s wonderful package, BaseballR
I would be shocked if you weren’t able to correctly identify 100% of the sample based solely on those features.
So if you’re a Nike athlete playing for the Cleveland Indians and you’re coming up to Free Agency, you now have to worry about whether some throw-away test you did for your shoe company will affect how much money you’ll make.
I should have been more clear on the ethics issue. I do not think this particular research was funded by Nike at all. It was just that Appelbaum had a previous relationship with Nike through another researcher.
Regardless, valid and interesting points about the possibility Nike tests affecting future earnings and that over-specified models will always be able to predict things.
I would never imply that research conducted on something called a “Nike Sensory Station” from 2011-2015 would be funded by Nike. That is insane.
With that said, there’s obviously nothing beyond the fact that they got a Nike Sensory Station that shows Nike gave them anything other than the data. I’m not making some crazy speculation that there’s some hidden money channels. Nike obviously benefits if their sensory station is shown to predict and improve performance, but I’m not going down that road.
My only concern was the overly specific data and how some athletes probably never thought the data would have a chance to impact their careers.
Ahhh I see, very good points all around!
I wonder what the consent form looked like that the subjects signed.
The union should definitely have an eye on this! It’s not fair to expect experimental subjects to have to figure out “my anonymous stat line, it says, but you could de-anonymize that with a database scan.”
Well done, sir! This reply is just as important as the article.
Another consideration here is that interpreting coefficients in a multiple regression is questionable when the covariates are highly correlated. They do mention removing one test due to correlation with another test, but otherwise they don’t mention it. Naively it seems like you would expect some degree of relationship between the various tests and particularly age.
Thank you for adding a dose of reality. This is extremely exciting research, but I think it’s imperative to continue to remain skeptical precisely because it is so exciting (we want to believe! but that’s just a terrible approach to science.)
I would also be interested to know what type of holdout they used for testing. The study lead was referred to as a statistician, so I would expect rigor, but the proof is always in the pudding.
Finally, it’s incredibly important to remain skeptical of the work, without inferring anything against the researchers. We are all blind to our biases.
“‘There will never be a blind ballplayer,’ said co-author Gregory Appelbaum.”
Ha! Tommy Pham laughs at your so-called “science.”
My original post was going to be about cricket. But heck, neuroscience, yeah! Gimme some more of that monkey brain!
Frankly, this is ludicrous. And comparing the “six magic bits” to software doesn’t really help the case.
That said, teams are desperate for predictives these days, and it wouldn’t surprise me at all if Duke didn’t coin it.
Fearless Eno logs the first non-ironic use of Benito Santiago in the lede of a story about on-base percentage.
I would look at o swing rather than walk rate. Walk rate is influenced big time by zone rate (“fear of pitcher”) and also other things like contact rate.
Also we shouldn’t equal obp and walks. Walks help obp but the more important factor for obp is batting average (about 2/3 is BA and 1/3rd is walk rate on average).
I’ve always wondered how some fielders, like Hamilton and Gordon (and now Buxton), seem to see the ball so well off the bat and react instantaneously, but can’t seem read pitches nearly as well, whereas some athletic batters see the ball well off the mound, but not off the bat.
This study makes me wonder if it could be used to help both facets of the game.
Hamilton actually has a decent eye. His career o swing rate is 28%. Of course that is not mauer/votto territory but well above average and better than kris bryant, miggy and harper.
The reason he doesn’t walk much is that he can’t hit and pitchers thus throw fastballs down the middle to him since he can’t punish them. And actually a career 6.6% walk rate isn’t bad for a guy who can’t hit, I mean there are more and more 25+ hr guys who only walk half as much (odor,de jong).
Don’t get me wrong Hamilton’s plate discipline isn’t elite but it is above average, the reason his hitting is so bad is that he couples zero power with just ok contact rates (at least guys like gordon and pierre had single digit K rates while Hamilton has a 19% career k rate which isn’t bad but not enough for a guy with no power.
It’s not clear: did they measure baseball performance after collected the testing data, or the other way around?
The interesting question about all of this is whether any of the tests added value to a player’s Steamer projection.