The Problem with Samuel Sterns’ Powers
Spoiler Warning For Captain America: Brave New World
As a someone with a background in statistics and also a Marvel fan, I watched Captain America: Brave New World with excitement. But one aspect of the film left my analytical mind uneasy: Samuel Sterns (aka The Leader) is portrayed as so intelligent that he can calculate the probability of future events. In the movie, Sterns even claims he has "seen an impending attack from other worlds in the probabilities" essentially predicting a multiversal war. This concept makes for dramatic storytelling, but from a scientific and statistical perspective, it raises big questions.
Probability calculations are not magic; they hinge on data and empirical evidence. Given Sterns’ long imprisonment and isolation, one must critically ask: how on Earth (or any world) is he confidently predicting such complex events with no fresh or reliable data?
In this article, I’ll break down why Sterns’ probability-predicting power is scientifically problematic. We’ll examine how ultra-intelligence ≠ (is not equal to) omniscience, why probability requires data, how imprisonment limits those data, and why any predictions he makes would carry huge uncertainty. In the end, even a super-genius like Sterns would be rolling statistical dice in the dark, making his supposed foresight highly suspect.
Intelligence Isn’t Omniscience
Sterns is introduced speaking in probabilities as though he’s calculating fate itself, making it seem like he's analysing outcomes with mathematical certainty.
But intelligence alone can’t generate accurate probabilities. Whether forecasting wars or interdimensional chaos, accurate predictions require information; preferably lots of it. Without data, even a super-genius is guessing. In data science, we say: “garbage in, garbage out.” With no recent input, Sterns’ calculations are just dressed-up speculation.
Imagine trying to predict a stock market crash or a natural disaster without any data feeds. You might have a brilliant mind, but you'd still be operating blind. Sterns is portrayed as brilliant, but brilliance doesn’t compensate for a lack of information.
Probability Without Data Is Just Guesswork
It’s worth noting that the movie glosses over how Sterns calculates these probabilities. From a scientific lens, one could ask if he’s envisioned as a frequentist (analyzing many hypothetical scenarios in his mind) or Bayesian (updating a personal belief network). The truth is likely neither – the film simply uses “probability calculation” as a plot device, not a rigorous methodology. But if we play along:
A frequentist Sterns might try to imagine running the world events over and over in his head like experiments. He might say, “In 9 out of 10 imagined trials, this happens.” Yet without real trials or data, these are just scenarios in his imagination. He cannot truly sample alternate universes (unless he secretly has Doctor Strange’s Time Stone, which he doesn’t), so he’s frequentist in name only. There’s no objective repeatable process behind his numbers.
A Bayesian Sterns might rely on a colossal prior – perhaps his encyclopedic knowledge of history gives him a gut-level prior probability for events. He could then update this if he hears any news or sees any evidence. But locked in a cell, he’s getting almost no new evidence to update with. Bayesian inference without updates is just prior belief. Sterns might be extremely smart, so his prior beliefs could be well-educated guesses, but they’re still guesses. And if the situation he’s predicting is unprecedented (say, nobody in history has opened a portal to other worlds before), even a Bayesian has basically no prior to go on. At best, Sterns might draw analogies to known events (e.g., “the probability of a world war given certain political tensions”). Yet an interdimensional war is beyond even that – it’s a completely new category, the ultimate “unknown unknown.”
In reality, statisticians faced with trying to predict a singular, novel event would acknowledge the limits of their methods. In fact, the Black Swan theory popularized by Nassim Taleb comes to mind: a "black swan" event is something extremely rare and unforeseen that standard probability models can’t predict. A multiversal conflict in the MCU context is pretty much a Black Swan event – one that "cannot be predicted beforehand" and for which "standard forecasting tools... fail to predict". As Taleb argues, traditional statistics depend on large sample sizes and past data, which are never available for rare, unprecedented events. If professional statisticians and AI models in our world struggle with predicting rare crises (think of how few saw the 2008 financial crash or the COVID-19 pandemic coming), it’s even less plausible that a lone genius in a cell could foresee an interdimensional war with any accuracy. No data, no precedent, and extremely complex variables = essentially unpredictable.
Thus, whether you lean frequentist or Bayesian, the scenario fails. The only way Sterns' powers make sense is as a fictional exaggeration of intelligence – basically, treating him as if he has a quasi-supernatural foresight (even if the script tries to label it as “probability”). As a statistician, I can’t help but chuckle at that. It’s a classic case of Hollywood taking a real concept (predictive analytics) and amplifying it to the point of fantasy.
Isolation Means Obsolete Input
Sterns has been locked away for years, first by General Ross and later in The Raft — a high-security prison. He wasn’t browsing headlines or accessing the S.H.I.E.L.D. database. His exposure to current events was minimal, if not zero.
Any “model” Sterns has is based on old knowledge (likely pre-2008 MCU events) and whatever information Ross fed him during his covert detainment. Meanwhile, the MCU’s world has drastically changed: alien invasions, the Snap, and multiverse-shaking events have all taken place while Sterns sat in a cell.
In machine learning, we call this model drift: over time, without new input, a model becomes less and less reliable. Even if Sterns built an accurate framework years ago, it would now be wildly out of date.
The Confidence (or Lack Thereof) in His Predictions
Let’s suppose, for the sake of argument, that Sterns does manage to compute some probability for a given outcome using whatever limited data he has. Even then, as a statistician I’d ask: what is the confidence level or error margin on his estimate? Given his minimal data, the answer would be: the uncertainty is enormous. In statistical analysis, the less data you have, the less confidence you should have in your conclusions. Small sample sizes lead to very wide confidence intervals, meaning your estimate could be off by a lot.
To illustrate, consider a concrete example from basic stats: if you survey 20 people and find 12 of them like a movie, you might estimate “60% probability of a person liking this movie.” But with only 20 data points, the 95% confidence interval around that estimate is very wide – in one example it might be something like 35% to 80%! In contrast, if you surveyed 200 people and still got 60% liking it, the confidence interval would narrow to roughly 53% to 67%. More data = more precision; less data = huge uncertainty. In Sterns’ case, how many “data points” could he possibly have for a multiverse war or any specific complex chain of events? Virtually none or just a handful of personal observations. Statistically, his “confidence interval” on such predictions would span the entire spectrum of possibilities – effectively making his probability estimate meaningless.
Another way to think of it: imagine Sterns says, “I calculate a 90% probability that World Power X will start a war next year.” Without ample evidence, that "90%" is not a rigorous number, it might as well be 50% or 10%. The variance of his estimate is huge. If he ran his mental simulation again with a slight tweak (which in real modeling terms could represent new data), the outcome could swing wildly because there's not enough data to stabilize the prediction. In statistics, any model built on scant data is extremely sensitive and unreliable.
We could also talk about confidence levels: even if Sterns assigns a probability, what's the confidence that this probability is accurate? Likely very low. If he were honest about it, he might say something like, "I estimate a 90% chance, plus or minus 50%." Such a statement is obviously absurd practically – it shows that with limited information, one’s degree of certainty collapses.
In summary, not only is Sterns’ data limited, but any probabilities he computes would come with such low confidence (high uncertainty) that they would be arguably worthless for decision-making. A probability that has an error margin nearly as large as its value doesn’t provide guidance – it’s just statistical noise. This undermines the usefulness of Sterns’ supposed power: if he tells a villain “there’s an 80% chance your plan will succeed,” but his knowledge is so incomplete that the real chance could be anywhere from, say, 20% to 95%, then his advice is not much better than a coin flip. And that’s being generous – it assumes his method is sound in principle. In reality, as we’ve argued, the method itself is built on sand without data.
Fictional Math: Frequentist, Bayesian… or Neither?
The film never explains how Sterns derives these probabilities. Is he simulating hypothetical timelines like a frequentist? Is he relying on intuitive priors like a Bayesian? The answer is neither; because the movie treats “probability” like a narrative shortcut, not a mathematical process.
Sterns lacks both experimental trials (as a frequentist would need) and fresh updates (as Bayesians rely on). His “calculations” are closer to educated guesses or instinct; no more predictive than flipping a coin while sounding smart.
This also echoes Nassim Taleb’s Black Swan theory: rare, unprecedented events like multiversal warfare, are fundamentally unpredictable. These are events that don’t follow normal probability rules because there’s no history to build from. If experts in our world struggle to predict financial crashes or pandemics, Sterns forecasting multiversal calamity from prison is laughable in comparison.
People Are Not Predictable
Even if Sterns had a perfect model, there’s another flaw: humans. People aren’t deterministic systems, they surprise you. The film itself demonstrates this. Despite his planning, Sterns fails to anticipate Sam Wilson’s choices (and most other characters apart from the ones he is controlling.
Captain America becomes a living contradiction to Sterns’ predictions, the wildcard that stats didn’t account for. In real-world terms, Wilson is an outlier or disturbance in the model, the “error term” that throws off the whole equation.
Sterns also misjudges Ross, someone he knows well. If he can’t accurately forecast the behavior of a man he’s worked with for years, how can he predict cosmic-scale conflicts involving unknown entities?
Simply Put
Sterns’ powers make for compelling cinema, but they don’t hold up scientifically. Probability demands data, and Sterns doesn’t have it. His years in captivity deprived him of the input needed for sound predictions. What the film paints as foresight is, statistically speaking, fiction.
It’s a classic case of Hollywood borrowing real concepts (like predictive analytics) and stretching them into fantasy. A genius villain who sees the future through “numbers” is a cool idea; but without fresh data or a working model, it’s all illusion.
If Sterns truly wanted to see the future in probabilities, he’d need not just brains, but bandwidth: real-time feeds, surveillance, databases, the works. Lacking all that, he’s not calculating destiny. He’s just guessing in a lab coat.
As a fan, I enjoyed the film. With a background in stats, I laughed out loud. Next time Sterns invokes the “probabilities,” I’d love to see Captain America toss him a stats textbook. Not that it would matter, I’d put the probability of ego deflation at 0%.
References
Investopedia. (n.d.). Black swan. https://www.investopedia.com/terms/b/blackswan.asp
Marvel Cinematic Universe Wiki. (n.d.). Leader (Samuel Sterns). Fandom. https://marvelcinematicuniverse.fandom.com/wiki/Leader
Marvel Cinematic Universe Wiki. (n.d.). Captain America: Brave New World. Fandom. https://marvelcinematicuniverse.fandom.com/wiki/Captain_America:_Brave_New_World
Pennsylvania State University. (n.d.). STAT 200: Introduction to Statistics. https://online.stat.psu.edu/stat200/
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.
Wikipedia contributors. (n.d.). Bayesian probability. Wikipedia. https://en.wikipedia.org/wiki/Bayesian_probability
Wikipedia contributors. (n.d.). Frequentist probability. Wikipedia. https://en.wikipedia.org/wiki/Frequentist_probability
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