Tag Archives: Sports

As my son is a senior marching band drum major, Friday I went to a local high school football game to film him for posterity.  And I noticed something obvious.

People like to cheer their teams on, but prefer to be encouraged by announcers, cheerleaders, bands, pep squads, and so on.  And while fans seem to care that these various leaders of cheer are impressive and loyal, fans don’t seem to care nearly as much if these leaders sound sincere.  The announcer carefully controlled his voice inflections, the cheerleaders carefully synchronized their arm movements, and everything they said was consistently loyal, but you couldn’t possibly have mistaken them for people who deeply and sincerely believed the words they spoke.

Sport cheers are often considered an analogy to political and ideological partisanship; we like to vote and declare our opinions similar to the way we like to cheer sport teams.  We prefer to support positions that have have loyal impressive cheerleaders.  It is nice if those cheerleaders are also sincere, but it is not especially important to us.  It is, however, important that our idea cheerleaders be impressive and loyal.  We might eagerly point out when leaders on the other side sound insincere, but that is mostly hypocrisy, since we don’t care much about our leaders’ sincerity.   

Just as we care more that our team wins than that they were actually the strongest team, we probably care more that our idea sides look good than that our ideas are true.

A computer has beaten top human poker players:

Humanity was dealt a decisive blow by a poker-playing artificial intelligence program called Polaris during the Man-Machine Poker Competition in Las Vegas.  Poker champs fought the AI system to a draw, then won in the first two of four rounds (each round had Polaris playing 500 hands against two humans, whose points were averaged.) But in the final two rounds of the match, Polaris beat both human teams, two wins out of four, with one loss and one draw. IBM’s Deep Blue beat chess champion Gary Kasparov in 1997.

This has gained very little news coverage, in stark contrast to the Chess case, even though far more people play poker than chess.  To explain both facts, note that most people can more easily see that they lack chess than poker expertiSe.  They can watch a poker expert fold or raise and imagine that they would have done the same, but know they have no idea why a chess expert makes his moves.

This fools people into thinking they could be a poker champion, which is why so many more people try at poker.  And being less impressed by existing poker champions, people are less impressed by a computer who beats those champions.  The moral: beware of underestimating computers by underestimating the difficulty of the tasks at which they excel. 

Analyzing the neutrality of referees during 12 German premier league (1. Bundesliga) soccer seasons, this paper documents evidence that social forces influence agents’ decisions. Referees, who are appointed to be impartial, tend to favor the home team by systematically awarding more stoppage time in close matches in which the home team is behind. They also favor the home team in decisions to award goals and penalty kicks. Crowd composition affects the size and the direction of the bias, and the crowd’s proximity to the field is related to the quality of refereeing.

That is from Economic Inquiry. 

The October Journal of Economic Psychology says goalies do too much:

In soccer penalty kicks, goalkeepers choose their action before they can clearly observe the kick direction. An analysis of 286 penalty kicks in top leagues and championships worldwide shows that given the probability distribution of kick direction, the optimal strategy for goalkeepers is to stay in the goal’s center. Goalkeepers, however, almost always jump right or left. … The claim that jumping is the norm is supported by a second study, a survey conducted with 32 top professional goalkeepers. The seemingly biased decision making is particularly striking since the goalkeepers have huge incentives to make correct decisions, and it is a decision they encounter frequently. Finally, we discuss several implications of the action/omission bias for economics and management. …

The action/omission bias might affect the decision of investors whether to change their portfolio (action) or not (inaction). It can affect the choice of managers whether to leave their company’s strategy or investments unchanged (inaction), or to change them (action). The bias may also have implications for the decision of workers whether to stay in their job (inaction) or look for a better job (action), and one’s decision whether to re-locate to another city or not. In the macro-economic level, the action/omission bias may also affect decisions made by governments and central banks whether to change various policy variables (interest rates, tax rates, various types of expenditures, etc.), or leave them unchanged.

Of course if you do nothing you risk being seen as cowardly or ignorant, so it might not pay you to correct this bias.   

Daniel Kahneman posted the following on the Judgment and Decision Making site:

Have there been studies of the calibration of expert players in judgments of chess situations — e.g., probability that white will win?

In terms of the amount and quality experience and feedback, chess players are at least as privileged as weather forecasters and racetrack bettors — but they don’t have the experience of expressing their judgments in probabilities. I [Kahneman] am guessing that the distinction between a game that is "certainly lost" and "probably lost" is one that very good players can make reliably, but I know of no evidence.

Despite knowing much less about decision making and (likely) less about chess than Kahneman, I have three conjectures:

Continue reading "Calibration in chess" »

Rock, Paper, Scissors is a simple game whose winner should be determined completely by luck. Yet here is a guide to winning at the game. (HT Andrew Sullivan.)

Against a rational player you should randomize and play Rock, Paper and Scissors each with 1/3 probability. If your opponent does this you can never come up with a strategy that will give you an advantage over him. But, as the guide says, “Humans, try as they might, are terrible at trying to be random, in fact often humans in trying to approximate randomness become quite predictable.” For example, according to the guide, an inexperienced player will never play the same thing three times in a row. Taking advantage of this, you can gain an edge over your opponent.

On Wednesday the New York TImes covered OB contributor Justin Wolfers’s paper showing:

White referees called fouls at a greater rate against black players than against white players.  … [There is] a corresponding bias in which black officials called fouls more frequently against white players, though that tendency was not as strong.

The NBA says its own study refutes this, though they won’t release their data to allow independent confirmation. 

Yes, Justin hasn’t actually posted here yet, but we continue to hope.