Is The Hot Hand Fallacy A Fallacy?



A simple idea that everyone missed, and more?



Composite of src1, src2, src3 —A myth of a myth of a myth?



Joshua Miller and Adam Sanjurjo (MS) have made a simple yet striking insight about the so-called hot hand fallacy.

Today Ken and I want to discuss their insight, suggest an alternate fix, and reflect on what it means for research more broadly.



I believe what is so important about their result is: It uses the most elementary of arguments from probability theory, yet seems to shed new light on a well-studied, much-argued problem. This achievement is wonderful, and gives me hope that there are still simple insights out there, simple insights that we all have missed—yet insights that could dramatically change the way we look at some of our most important open problems.

I would like to point out that I first saw their paper mentioned in the technical journal WSJ—the Wall Street Journal.

The Fallacy

Our friends at Wikipedia make it very clear that they believe there is no hot-hand at all:

The hot-hand fallacy (also known as the “hot hand phenomenon” or “hot hand”) is the fallacious belief that a person who has experienced success with a random event has a greater chance of further success in additional attempts. The concept has been applied to gambling and sports, such as basketball.

The fallacy started as a serious study back in 1985, when Thomas Gilovich, Amos Tversky, and Robert Vallone (GTV) published a paper titled “The Hot Hand in Basketball: On the Misperception of Random Sequences” (see also this followup). In many sports, especially basketball, players often seem to get hot. For example, an NBA player who normally usually shoots under 50% from the field may make five shots in a row in a short time. This suggests that he is “in the zone” or has a “hot hand.” His teammates may pass him the ball more often in expectation of his staying “hot.”

Yet GTV and many subsequent studies say that this is wrong. They seem to show rather that shooting is essentially a series of independent events: that each shot is independent from previous ones, and that this is equally true of free throws. Of course given the nature of this problem, there is no way to prove mathematically that hot hands do not exist. There are now many papers, websites, and a continuing debate about whether hot hands are real or not.

The Fallacy Of The Fallacy

Let me start by saying that Ken and I are mostly on the fence about the fallacy. I can imagine many situations that make it mathematically possible. For one, what if a player in basketball is not doing independent trials but rather executing a Markov process. That is, of course, a fancy way of saying that the player has “state.” Over a long term they will make about of their free-throws, but there are times when they will shoot at a much higher percentage. And, of course, times when they will shoot at a much lower percentage—a “cold hand” is more obvious with free throws and may lead the other team to choose to foul that player.

I personally once experienced something that seems to suggest that there is a hot hand. I was a poor player of stick-ball when I was young, but one day played like the best in the neighborhood. Oh well. Let me explain this in more detail another day.

Ken chimes in: This folds into a larger question that is not fallacious, and that concerns me as I monitor chess tournaments:

What is the time or game unit of being in form?

In chess I think the tournament more than game or move is the “unit of form” though I am still a long way from rigorously testing this. In baseball the saying is, basically, “Momentum stops with tomorrow’s opposing starting pitcher.”

Nevertheless, I (Ken) wonder how far “hot hand” has been tested in fantasy baseball. In my leagues on Yahoo! one can exchange players at any time. Last month I dropped the slumping Carlos Gomez in two leagues even though Yahoo! still ranked him the 8th-best player by potential. In one league I picked up Jake Marisnick, another Houston outfielder who had been hot. Marisnick soon went cold with the bat but stole 6 bases to stay within the top 100 performers anyway. The leagues ended with the regular season, but had they continued into the playoffs I definitely would have picked up Houston’s Colby Rasmus after 3 homers in 3 games. While I’ve been writing the last three main sections of this post, Rasmus hit another homer today.

The New Insight

MS have a new paper with the long title, “Surprised by the Gambler’s and Hot Hand Fallacies? A Truth in the Law of Small Numbers.” Their paper is interesting enough that it has already stimulated another paper by Yosef Rinott and Maya Bar-Hillel, who do a great job of explaining what is going on. The statistician and social science author Andrew Gelman explained the basic point in even simpler terms on his blog in July, and we will follow his lead.

For the simplest case consider sequences of coin tosses. We will generate lots of these sequences at random. We will look at times the coin comes up heads ( ) and say it is “hot” if the next throw is also . If the sequence is all tails, however:

then we must discard it since there is no to look at. This is our first hint of a lurking bias. Likewise if only the last throw is heads,

there is no chance for a hot hand since the “game is over.” Hence it seems natural to use the following sampling procedure:

Generate a sequence at random. If it has no head in the first places, discard it. Else pick a head at random from those places. That is, pick such that . Score a “hit” if , else “miss.”

Let HHS be the number of hits after trials—not counting discarded ones—divided by . That is our “hot hand score.” We expect HHS to converge to as gets large. Indeed, there seems a simple way to prove it: Consider any head that we chose in step . The next flip is equally likely to be or . If it is we score a hit, else a miss. Hence our expected hit ratio will be 50%. Q.E.D. And wrong.

To see why, consider . The sequences and are discarded, so we can skip them. Here are the other six sequences and their expected contribution to our hit score. Remember only a head in the first two places is selected.

This gives HHS , not . What happened?

A Statistical Heads-Up

What happened is that the heads are not truly chosen uniformly at random. Look again at the six rows. There are 8 heads total in the first two characters. Choose one of them at random over the whole data set. Then you see 4 heads are followed by and the other 4 by . So we get the expected 50%. The flaw in our previous sampling is that it was shortchanging the two heads-rich sequences, weighting each instead of . This is the bias.

You might think the bias would quickly lessen as the sequence length grows but that too is wrong. Try . You get the weird fraction . This equals , which has gone down. For we get . As the expectation does come back up to but for , which is a typical high-end for shots by a player in an NBA game, it is still under . Rinott and Bar-Hillel derive the case with probabilities of heads and of tails and as

The bias persists if we condition on heads in a row. “Appendix B” in the MS paper attempts to find a formula like that above for but has to settle on partial results that imply growth with among other things. We can give some concrete numbers: For and the chance of seeing heads next is the same . For and it has dipped to with 17 of 32 sequences being thrown out. The larger point, however, is that these numbers represent the expectation for any one sequence that you generate, provided you follow the policy of selecting at random from all the subsequences in through place and predicate on the next place being .

Did GTV really follow this sampling strategy? It seems yes. It comes about if you follow the letter of conditioning on the event that the previous flips were heads. Rinott and Bar-Hillel quote words to that effect from the GTV paper, ones also saying they compared the probability conditioned on the last shots being misses. The gist of what they and MS are saying is:

If you did your hot-hand study as above and got 50%, then chances are the process from which you drew the data really was “hot.”

And since the way of conditioning on misses has a bias toward , if you got again then your data might really also show a “Cold Hand.” It should be noted, however, that the GTV study of free throws is not affected by this issue and indeed already abides by our ‘grid’ suggestion below—see also the discussion by MS beginning at page 10 here.

Subtler Bias Still

MS give some other explanations of the bias, one roughly as follows: If a sequence starts with heads and is followed by , then you had no choice but to select those heads. But if they are followed by another , then we do have a choice: we could select the latter heads instead. This has a chance of failure, so the positive case is not as rich as the negative one. Getting positive results—more heads—also gives more ways to do selections that yield negative results.

This suggests a fix, but the fix doesn’t work: Let us only condition on sequences of heads that come after a tail or start at the beginning of the game. The above issue goes away, but now there is a bias in the other direction. It shows first with and ; we have grouped sequences differing on the last bit:

This gives . The main culprit again is the selection within the sequence , with the discarding of also a factor.

A Grid Idea

This train of thought led me (Ken) to an ironclad fix, different from a test combining conditionals by MS which they refer to their 2014 working paper. It simply allows only one selection from each non-discarded sequence, namely, testing the bit after the first consecutive heads. The line then gives and makes the whole table overall.

That this is unbiased is easy to prove: Given in the set of non-discarded sequences, define to be the sequence obtained by flipping the bit after the first consecutive heads. This is invertible— back again—and flips the result. Hence the next-bit expectation is . This works similarly for any probability of heads and does not care whether the overall sequence length is kept constant between samples. Hence our proposal is:

Break up the data into subsequences of any desired lengths . The may depend on the lengths of the given sequences (e.g., how many shots each player surveyed took) but of course not on the data bits themselves. For every that has a run of consecutive “hits” in the first places, choose the first such sequence and test whether the next bit is “hit” or “miss.”

The downsides to putting this kind of grid on the data are sacrificing some samples—that is, discarding cases of that cross gridlines or come second in an —and the arbitrary nature of choosing rules for . But it eliminates the bias. We can also re-sample by randomly choosing other grid divisions. The samples would be correlated but estimation of the means would be valid, and now every sequence of might be used as a condition. The re-sampling fixes the original bias in choosing every such sequence singly as the condition.

A larger lesson is that hidden bias might be rooted out by clarifying the algorithms by which data is collated. Researchers count as part of the public addressed in articles on how not to go astray such as this and this, both of which highlight pitfalls of conditional probability.

Open Problems

Has our grid idea been tried? Does it show a “hot hand”?

Update (10/18): The idea is apparently new, though as we admit in the post it is “lossy.” Meanwhile this has been covered in the Review section of the Sunday 10/18 New York Times, which in turn references and gives a chart from this post by Steven Landsburg on his “Big Questions” blog.