The recognition heuristic in sports betting predictions.

The recognition heuristic in sports betting predictions.

Stuart and Penny enjoyed to travel, on this occasion they took advantage of the excitement over the world cup as an excuse to go on holiday. They saw the sights, enjoyed the local delicacies and watched a few matches. Stuart decided to place a bet on one of the matches but had never gambled before. Penny noticed that many people when betting simply chose the team, or athlete, that they were familiar with as the winner. Since neither Stuart nor Penny had gambled before and they did not know a lot about the athletes they both agreed that choosing the familiar team as the winner was the best way to gamble.

When gambling on the outcome of an event such as a football match, tennis match, or even a golf game many people predict that the familiar athlete or team will win. Regardless of the type of event or scoring criterion choosing the familiar is often the strategy that is used by laypeople (i.e., non-experts). This ‘familiar strategy’ is called the ‘recognition heuristic’ (Gigerenzer & Goldstein, 1996). The recognition heuristic has been studied extensively in the decision-making literature with many different examples of predictions for sporting events.

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Researchers from the University of Illinois investigated the role of the recognition heuristic when making predictions about the outcome of basketball matches (Jacobson et al., 2009). Jacobson and colleagues (2009) choose to use basketball from the National Collegiate Athletic Association (NCAA) as the medium to study the recognition heuristic. In 2007 alone, an estimated $2.25 billion US dollars was bet on the final four rounds of this basketball tournament (McCarthy, 2007). Of the teams to get into the final four rounds 70% of these teams were highly seeded. Gambling records show that the favourite teams were over-backed when betting.  The researchers used data from matches between 1985 and 2009. They asked people to predict the outcome of basketball matches based on very little information – just the names of the teams. The results revealed that most of the participants used the recognition heuristic, predicting that the well-known basketball teams would win the match.

A second experiment that used basketball data to investigate the role of the recognition heuristic utilised data from the National Basketball Association (Hall et al., 2007). One hundred and sixteen undergraduate students from Princeton University took part in the experiments by Hall et al (2007). Firstly, participants were asked to list as many NBA teams as they could recall. The data from sixteen games involving these teams was then selected. In this experiment all participants received statistics (win record, halftime scores), and half the participants received the names of teams (e.g., NY Knicks vs NJ Nets). Participants attempted to predict the outcome of games based on the information they received. The Los Angeles Lakers are a very familiar team with many people but did not have the best statistics – if using the recognition heuristic participants would ignore the statistics and predict the LA Lakers as a winner (unless against a more familiar team). Contrary to the recognition heuristic most people would expect that the more information we are given the better we can make predictions. The results of this study found that participants overestimated the chances of winning for familiar teams – using the recognition heuristic.

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The recognition heuristic has also been studied with football (soccer) data (Pachur & Biele, 2007). In a third study laypeople and experts were asked to make predictions about the outcome of football matches in the 2004 European Soccer Championship. Pachur et al (2007) investigated five mechanisms for making predictions. Although the experts made more correct forecasts than laypeople the recognition heuristic accounted for 90% of all predictions made – the recognition heuristic was found to be an effective way to make predictions based on little information.

In the case of tennis, the recognition heuristic has also been investigated (Serwe & Frings, 2007; Scheibehenne & Arndt, 2007). In one study using data from the 2003 Wimbledon tournament 90% of predictions made when a recognised player was against an unrecognised player relied on the recognition heuristic to make predictions (Serwe & Frings, 2007). The final study used data from the 2005 Wimbledon tournament (Scheibehenne & Arndt, 2007). Amateur tennis players and laypeople were asked about which tennis players they recognised. Laypeople recognised only 11 or the 128 players (9%) on average, whilst amateur tennis players recognised 49 players (39%) on average. Here the recognition heuristic accounted for 70% of all predictions made about the winner of a tennis match regardless of whether the person making the prediction was an amateur tennis player or layperson.

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As we have seen the recognition heuristic accounts much of the predictions made when forecasting the outcome of sporting events. Like Stuart betting for the first time many of us use the recognition heuristic whether we know about the heuristic or not. Although at first thought making predictions based on very little information seems like a bad idea the recognition heuristic can often help in making the correct predictions, after all, familiar teams are normally the most successful teams. So, if you are placing a small bet on a sporting event and you are a layperson (not an expert in the sport) then it is likely that this heuristic will be very useful tool in the decision-making toolbox.


How do we make decisions quickly when placing bets in a casino?

How do we make decisions quickly when placing bets in a casino?

Like many tourists, Rebecca and Catherine always wanted to visit Las Vegas. They took in the dazzling sights of the bright lights, enjoyed the stage magic and drifted into one of large casinos. When they walked into the casino they were amazed by the sounds and lights. Catherine had always enjoyed the game of roulette so headed straight to the roulette table. Catherine placed three consecutive bets, losing all three. Watching this Rebecca quietly said to Catherine “Maybe you should try another game, the odds are against you winning.” To this Catherine replied “No, I have lost three times I am overdue a win, I will win soon.”

When we make decisions, we like to think that we make all of our decisions rationally. Casino environments are designed to be one of the most complex environments that we ever encounter, there are colourful, flashing lights, loud machines playing music and people cheering. It is hard to deal with the environment, tune-out and make clear decisions. To deal with complex environments the brain has developed a number of judgement heuristics. Judgement heuristics are mental short cuts that enable us to make quick decisions whilst ignoring part of the decision-making environment. One classic example of a judgement heuristic was given by Catherine above when speaking to Rebecca at the roulette table. The aptly named gambler’s fallacy, as demonstrated above can also be seen in the following classic scenario. Imagine that someone is flipping a coin multiple times, the coin lands head side up three times in a row. Someone watching the coin flipping is asked to guess whether the next coin will land head side up or tail side up. If the person guesses tail side up they are using the gambler’s fallacy, a belief that randomness follows a pattern. Many people believe that for the coin flipping to be truly random the outcome of a sequence of five coin flips must be something like HEADS-TAILS-HEADS-TAILS-HEADS. If an outcome of five flips is HEADS-HEADS-HEADS-HEADS-HEADS then the coin is overdue to land with the tail side up otherwise this is not random. The belief in a pattern of randomness persists in most areas of life.

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In a field study by researchers at the University of Navada 18 hours worth of overhead security camera footage of a roulette table was obtained from a large Reno casino, consisting for 904 bets (Sundali & Croson, 2006). The researchers watched all of the bets and coded the patterns of betting for every gambler that sat down at the table. On an American-style roulette table there are 36 different sections of the roulette wheel, coloured as red or black (European-style roulette has 37 sections). Players can bet on more than one number and colour by placing their chips on the corners or side of each of the numbered squares. If a gambler bets randomly then after the researchers coded each bet there should be a 2.6% chance that a bet would fall on each number. The researchers found that after an outcome of RED-RED-RED gamblers responded with a gambler’s fallacy type logic by betting on BLACK. Like Catherine above and the coin flipping above, the gamblers believed in a representation, or pattern of randomness.

A study by Gal and Baron in 1996 investigated the gambler’s fallacy in a laboratory-based experiment. Gal and Baron examined whether a change in betting strategy was due to boredom by asking participants why they choose to bet in the way that they did. Most of the participants in the Gal and Baron study responded by saying that they were attempting to maximize their earnings by using the gambler’s fallacy-type logic. It would appear that the gambler’s fallacy is not simply caused by boredom.

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Besides gambling at the roulette table other casino games in which heuristics play a major role include the craps table and fruit machines. One prominent heuristic at the craps table is the belief in illusory correlations. When rolling the dice at the craps table gamblers often blow on the dice for luck or roll the dice softly in the belief that a low number will come up, and roll the dice harder when they want a higher number (Griffths. 1994). These gamblers believe that if a number comes up that they did not want then they did not through the dice hard or soft enough.

Cognitive short cuts as heuristics are used in every area of life where decisions need to be made. With respect to gambling, heuristics have been documented in fruit machine users, and at the poker, blackjack, roulette and craps tables. Even when betting on horse or dog races gamblers reliably use these mental short cuts (Terrell, 1998). I have highlighted two of uses of heuristics above, namely the gambler’s fallacy and illusory control. Like Catherine above many people fall for these heuristics and make sub-optimal decisions based on incorrect beliefs. When it comes to gambling, there are two ways to avoid these cognitive traps. If gambling alone take your time to think about the decisions that you want to make. When gambling in a group of with a friend you could listen to the advice of your friend as Catherine should have above.