Ironically, in Single Elimination no tournament structure is actually necessary at all. Any 63 games will yield a single undefeated champion. For instance, you could simply have a single “king of the hill” team take on challengers one by one until it is dethroned, at which point whoever defeated it takes over its spot and continues. This format would have the drawback of needing 63 separate rounds, however, as games couldn’t happen in parallel; also, one team could potentially have to play as many as 63 games in a row, which might not be ideal from a fatigue standpoint.
Though born well over a century after Dodgson, perhaps no one carries forward his mathematical take on sporting into the twenty-first century as strongly as Michael Trick. We met Trick back in our discussion of optimal stopping, but in the decades since his hapless application of the 37% Rule to his love life he’s become not only a husband and a professor of operations research—he’s now also one of the principal schedulers for Major League Baseball and for NCAA conferences like the Big Ten and the ACC, using computer science to decide the year’s matchups.
As Trick points out, sports leagues aren’t concerned with determining the rankings as quickly and expeditiously as possible. Instead, sports calendars are explicitly designed to maintain tension throughout the season, something that has rarely been a concern of sorting theory.
For instance in Major League Baseball, you often have races to see who is going to win the division. Now, if we ignored the divisional setup, some of those races might get resolved fairly early in the season. But instead what we do is we make certain in the last five weeks, everybody plays everybody else within their division. The purpose of that is it doesn’t matter who’s in a divisional race: they’re going to have to play their next closest opponent at least six games in the final five weeks of the season. That allows for more interest in the schedule or interest in the season because in this case, uncertainty is delayed in its resolution.
What’s more, sports are not, of course, always designed strictly to minimize the number of games. Without remembering this, some aspects of sports scheduling would otherwise seem mysterious to a computer scientist. As Trick says of baseball’s regular season of 2,430 games, “We know that n log n is the right number of comparisons to do a full sort. That can get you everybody. Why do they do n2 in order to just get, in some sense, the top, if that’s all they care about?” In other words, why do a full O(n2) Round-Robin and then some, if we know we can do a full sort in linearithmic time, and can crown an undefeated Single Elimination champion in less than n games? Well, minimizing the number of games isn’t actually in the league’s interest. In computer science unnecessary comparisons are always bad, a waste of time and effort. But in sports that’s far from the case. In many respects, after all, the games themselves are the point.
Griping Rights: Noise and Robustness
Another, perhaps even more important way of training an algorithmic lens on sports is to ask not what confidence we should have in the silver medal, but what confidence we should have in the gold.
As Michael Trick explains, in some sports, “for instance baseball, a team is going to lose 30% of their games and a team is going to win 30% of their games practically no matter who they are.” This has disturbing implications for the Single Elimination format. If NCAA basketball games, say, are won by the stronger team 70% of the time, and winning the tournament involves prevailing in 6 straight games, then the best team has only a 0.70 to the 6th power—less than 12%—chance of winning the tournament! Put another way, the tournament would crown the league’s truly best team just once a decade.
It may be that in some sports, having even 70% confidence in a game’s outcome might be putting too much stock in the final score. UCSD physicist Tom Murphy applied numerical modeling techniques to soccer and concluded that soccer’s low scores make game outcomes much closer to random than most fans would prefer to imagine. “A 3:2 score gives the winning team only a 5-in-8 chance of actually being a better team … Personally, I don’t find this to be very impressive. Even a 6:1 blowout leaves a 7% chance that it was a statistical fluke.”
Computer scientists call this phenomenon noise. All of the sorting algorithms that we’ve considered thus far assume perfect, flawless, foolproof comparisons, ones that never mess up and mistakenly judge the lesser of two quantities to be the greater. Once you allow for a “noisy comparator,” some of computer science’s most hallowed algorithms go out the window—and some of its most maligned have their day of redemption.
Dave Ackley, professor of computer science at the University of New Mexico, works at the intersection of computer science and “artificial life”—he believes computers can stand to learn a few things from biology. For starters, organisms live in a world where few processes have anywhere near the level of reliability that computers depend on, so they are built from the ground up for what researchers call robustness. It’s time, argues Ackley, that we started recognizing the virtues of robustness in algorithms too.
Thus, while the authoritative programming tome Sorting and Searching boldly declares that “bubble sort has no apparent redeeming features,” the research of Ackley and his collaborators suggests that there may be a place for algorithms like Bubble Sort after all. Its very inefficiency—moving items only one position at a time—makes it fairly robust against noise, far more robust than faster algorithms like Mergesort, in which each comparison potentially moves an item a long way. Mergesort’s very efficiency makes it brittle. An early error in a Mergesort is like a fluke loss in the first round of a Single Elimination tournament, which can not only dash a favored team’s championship hopes but also permanently relegate them to the bottom half of the results.* In a Ladder tournament, on the other hand, as in a Bubble Sort, a fluke loss would only set a player back a single place in the standings.
But in fact it isn’t Bubble Sort that emerges as the single best algorithm in the face of a noisy comparator. The winner of that particular honor is an algorithm called Comparison Counting Sort. In this algorithm, each item is compared to all the others, generating a tally of how many items it is bigger than. This number can then be used directly as the item’s rank. Since it compares all pairs, Comparison Counting Sort is a quadratic-time algorithm, like Bubble Sort. Thus it’s not a popular choice in traditional computer science applications, but it’s exceptionally fault-tolerant.
This algorithm’s workings should sound familiar. Comparison Counting Sort operates exactly like a Round-Robin tournament. In other words, it strongly resembles a sports team’s regular season—playing every other team in the division and building up a win-loss record by which they are ranked.
That Comparison Counting Sort is the single most robust sorting algorithm known, quadratic or better, should offer something very specific to sports fans: if your team doesn’t make the playoffs, don’t whine. The Mergesort postseason is chancy, but the Comparison Counting regular season is not; championship rings aren’t robust, but divisional standings are literally as robust as it gets. Put differently, if your team is eliminated early in the postseason, it’s tough luck. But if your team fails to get to the postseason, it’s tough truth. You may get sports-bar sympathy from your fellow disappointed fans, but you won’t get any from a computer scientist.
Blood Sort: Pecking Orders and Dominance Hierarchies
In all the examples we’ve considered so far, the sorting process in every case has been imposed from the top down: a librarian shelving books, the NCAA telling teams whom to play and when. But what if head-to-head comparisons happened only voluntarily? What does sorting look like when it emerges organically, from the bottom up?
It might look something like online poker.
Unlike most sports, which are governed by a ruling body of some kind, poker remains somewhat anarchic despite exploding in popularity over the past decade. Though some high-profile tournaments do explicitly sort their contestants (and remunerate them accordingly), a substantial portion of poker is still played in what are known a
s “cash games,” where two or more players spontaneously agree to play with real money on the line with every hand.
Virtually no one knows this world more deeply than Isaac Haxton, one of the world’s best cash-game poker players. In most sports it’s sufficient to be as good as possible, and the less self-conscious one is about one’s skills the better. But, Haxton explains, “In some ways the most important skill as a professional poker player is to be able to evaluate how good you are. If you’re anything short of the very best poker player in the world, you can be pretty much assured of going broke if you are endlessly willing to play people better than you.”
Haxton is a heads-up, no-limit specialist: “heads-up” meaning one-on-one poker, and “no-limit” meaning just that—the highest stakes, limited only by what they can bankroll and stomach. In multi-handed poker cash games, there will often be one weak player—a wealthy amateur, for instance—feeding a table full of professionals, who then don’t much care who among them is better than whom. In the world of heads-up, it’s different. “There has to be a disagreement between you and them about who’s better—or somebody has to be willingly losing.”
So what happens when there’s a fairly established consensus and no one’s willing to play anyone better than they are? You get something that looks a lot like players simply jockeying for seats. Most online poker sites have only a finite number of tables available. “So if you want to play heads-up no-limit, with blinds of fifty and one hundred dollars, there are only ten available tables for that,” says Haxton, “and so only the consensus ten best players who are out right now … sit and wait for someone to show up who wants to play.” And if a superior player arrives and sits down at one of these tables? If the person sitting isn’t willing to ante up, they scram.
“Imagine two monkeys,” says Christof Neumann. “One is sitting and feeding in its spot, very peacefully, and another one is coming up [to] where the other guy is sitting. And that guy would then stand up and leave.”
Neumann isn’t making a poker metaphor. He’s a behavioral biologist at the University of Neuchâtel who studies dominance in macaques. What he’s just described is known as displacement.
Displacement happens when an animal uses its knowledge of the hierarchy to determine that a particular confrontation simply isn’t worth it. In many animal societies, resources and opportunities—food, mates, preferred spaces, and so forth—are scarce, and somehow it must be decided who gets what. Establishing an order ahead of time is less violent than coming to blows every time a mating opportunity or a prime spot of grass becomes available. Though we may cringe when we see creatures turning their claws and beaks on each other, biologists tend to think of pecking orders as the violence that preempts violence.
Sound familiar? It’s the search-sort tradeoff.
The creation of a pecking order is a pugilistic solution to a fundamentally computational problem. For this reason, incidentally, debeaking chickens on farms may be a well-intentioned but counterproductive approach: it removes the authority of individual fights to resolve the order, and therefore makes it much harder for the flock to run any sorting procedure at all. So the amount of antagonism within the flock in many cases actually increases.
Looking at animal behavior from the perspective of computer science suggests several things. For one, it implies that the number of hostile confrontations encountered by each individual will grow substantially—at least logarithmically, and perhaps quadratically—as the group gets bigger. Indeed, studies of “agonistic behavior” in hens have found that “aggressive acts per hen increased as group size increased.” Sorting theory thus suggests that the ethical raising of livestock may include limiting the size of the flock or herd. (In the wild, feral chickens roam in groups of ten to twenty, far smaller than flock sizes on commercial farms.) The studies also show that aggression appears to go away after a period of some weeks, unless new members are added to the flock—corroborating the idea that the group is sorting itself.
The key to thinking about decentralized sorting in nature, argues Jessica Flack, codirector of the Center for Complexity and Collective Computation at UW–Madison, is that dominance hierarchies are ultimately information hierarchies. There’s a significant computational burden to these decentralized sorting systems, Flack points out. The number of fights in, say, a group of macaques is minimized only to the extent that every monkey has a detailed—and similar—understanding of the hierarchy. Otherwise violence will ensue.
If it comes down to how good the protagonists are at keeping track of the current order, we might expect to see fewer confrontations as animals become better able to reason and remember. And perhaps humans do come closest to optimally efficient sorting. As Haxton says of the poker world, “I’m one of the top heads-up, no-limit hold ’em players in the world, and in my head I have a fairly specific ranking of who I think the twenty or so best players are, and I think each of them has a similar ranking in their mind. I think there is a pretty high degree of consensus about what the list looks like.” Only when these rankings differ will cash games ensue.
A Race Instead of a Fight
We’ve now seen two separate downsides to the desire of any group to sort itself. You have, at minimum, a linearithmic number of confrontations, making everyone’s life more combative as the group grows—and you also oblige every competitor to keep track of the ever-shifting status of everyone else, otherwise they’ll find themselves fighting battles they didn’t need to. It taxes not only the body but the mind.
But it doesn’t have to be that way. There are ways of making order without the costs.
There’s one sporting contest, for instance, where tens of thousands of competitors are completely sorted within the time that it takes to hold just a single event. (A Round-Robin tournament with ten thousand players, on the other hand, would require a hundred million matchups.) The only caveat is that the time required for the event is determined by its slowest competitors. This sporting contest is the marathon, and it suggests something critical: a race is fundamentally different from a fight.
Consider the difference between boxers and skiers, between fencers and runners. An Olympic boxer must risk concussion O(log n) times, usually from 4 to 6, to make it to the podium; allowing a greater number of athletes into the games would imperil the health of all. But a skeleton racer or ski jumper or halfpipe specialist needs to make only a constant number of gambles with gravity, no matter the size of the field. A fencer puts herself at her opponent’s mercy O(log n) times, but a marathoner must endure only one race. Being able to assign a simple numerical measure of performance results in a constant-time algorithm for status.
This move from “ordinal” numbers (which only express rank) to “cardinal” ones (which directly assign a measure to something’s caliber) naturally orders a set without requiring pairwise comparisons. Accordingly, it makes possible dominance hierarchies that don’t require direct head-to-head matchups. The Fortune 500 list, to the extent that it creates a kind of corporate hierarchy, is one of these. To find the most valuable company in the United States, analysts don’t need to perform due diligence comparing Microsoft to General Motors, then General Motors to Chevron, Chevron to Walmart, and so forth. These seemingly apples-to-oranges contests (how many enterprise software installations equal how many oil futures?) become apples-to-apples in the medium of dollars. Having a benchmark—any benchmark—solves the computational problem of scaling up a sort.
In Silicon Valley, for instance, there’s an adage about meetings: “You go to the money, the money doesn’t come to you.” Vendors go to founders, founders go to venture capitalists, venture capitalists go to their limited partners. It’s possible for the individuals to resent the basis of this hierarchy, but not really to contest its verdict. As a result, individual pairwise interactions take place with a minimum of jockeying for status. By and large, any pair of people can tell, without needing to negotiate, who is supposed to show what level of respect to whom. Everyone knows
where to meet.
Likewise, while maritime right-of-way is governed in theory by an extremely elaborate set of conventions, in practice one straightforward principle determines which ships give way to which: the “Law of Gross Tonnage.” Quite simply, the smaller ship gets out of the way of the larger one. Some animals are also lucky enough to have such clear-cut dominance hierarchies. As Neumann observes, “Look at fish, for example: the bigger one is the dominant one. It’s very simple.” And because it’s so simple, it’s peaceful. Unlike chickens and primates, fish make order without shedding blood.
When we think about the factors that make large-scale human societies possible, it’s easy to focus on technologies: agriculture, metals, machinery. But the cultural practice of measuring status with quantifiable metrics might be just as important. Money, of course, need not be the criterion; a rule like “respect your elders,” for instance, likewise settles questions of people’s status by reference to a common quantity. And the same principle is at work between nations as within them. It is often noted that a benchmark like national GDP—which underlies the invite lists to diplomatic summits such as the G20—is a crude, imperfect measurement. But the existence of any benchmark at all transforms the question of national status from one demanding at least a linearithmic number of tussles and resolutions into something with a single reference point that ranks all. Given that nation-to-nation status disputes often take military form, this saves not only time but lives.
Linearithmic numbers of fights might work fine for small-scale groups; they do in nature. But in a world where status is established through pairwise comparisons—whether they involve exchanging rhetoric or gunfire—the amount of confrontation quickly spirals out of control as society grows. Operating at industrial scale, with many thousands or millions of individuals sharing the same space, requires a leap beyond. A leap from ordinal to cardinal.
Algorithms to Live By Page 10
