The Game within the Game: Applying Behavioural Economics and Adaptive Markets to Football Psychology — Part 1

Dear Deep Green Community,

The world of sports, particularly football, is a microcosm of human behaviour and psychology. Its pulsating dynamics, much like the fluctuations of financial markets, owes much to the complex interplay of decisions, actions, and reactions made under varying conditions of stress, risk, and reward. It’s a fascinating arena where strategy, skill, and psychology weave the intricate dance of the game.

In recent years, we’ve seen significant strides in our understanding of human behaviour within the realm of economics and its impact on markets. Concepts such as Behavioural Economics and the Adaptive Markets Hypothesis have offered fresh perspectives on how individuals make decisions and adapt to their environments. Understanding these principles can empower us to not only make better decisions in our financial endeavours but also improve our understanding and appreciation of the intricate dynamics in sports, particularly football. Notable scholars like Richard Thaler and Daniel Kahneman have explored behavioural economics, focusing on how humans often act irrationally, driven by cognitive biases rather than pure logical decision-making. Andrew Lo’s Adaptive Market Hypothesis, on the other hand, looks at financial markets from an evolutionary perspective, emphasizing adaptability over efficiency.

These theories, initially designed to examine economic phenomena, also offer insightful parallels in the realm of sports. As such, we’re thrilled to announce a new series of articles, where we delve into this fascinating intersection of behavioural economics, adaptive market hypotheses, and football.

However, we want to clarify that this series doesn’t aim to be exhaustive to any extent. Our purpose is to give an overview of these concepts, to bring them closer to football enthusiasts and to show how they can apply to understanding football dynamics.

This journey will unfold over three parts.

In this first instalment, we’ll explore these captivating dynamics more in depth, focusing primarily on behavioural economics. We’ll discuss how this field of study, which examines the effects of psychological, cognitive, emotional, and social factors on economic decisions, can provide illuminating insights into football’s intricacies.

Our upcoming second part will delve into the Adaptive Market Hypothesis, extending our exploration into how this theory, which emphasizes adaptability, can be effectively applied to both football and sports trading.

Finally, in the concluding part of this series, we’ll investigate the impact of high-stake situations on decision-making processes in football and in financial markets. By drawing parallels between these two scenarios, we aim to deepen our understanding and improve our predictive models for the beautiful game.

We invite you to join us on this exciting journey, one where psychology meets football and where our understanding of football and sport at large will be broadened and deepened.

Behavioural Economics in Football

Behavioural economics, at its core, is an approach that takes into account the often irrational and emotional decision-making processes of individuals and organizations. It challenges the conventional economic theory that assumes humans always make rational choices that maximize their benefits. Instead, behavioural economics introduces concepts such as anchoring, loss aversion, and herd behaviour to explain why people often make decisions that deviate from strict rationality.

Let’s look at each of these concepts more closely and how they apply to football.

1. Anchoring

Anchoring is a cognitive bias where an individual relies too heavily on an initial piece of information (the “anchor”) when making decisions. In the world of football, this bias can heavily influence how players, teams, and even fans perceive performances and abilities.

For example, the valuation of a player is often anchored to their transfer fees. Expensive players are expected to perform exceptionally well, and their performances are often judged in light of the high price paid for them. Similarly, the perception of a team’s ability could be anchored to their past successes or failures, which might not necessarily reflect their current potential or form. This might lead to incorrect predictions or assessments of upcoming matches. Kahneman and Tversky’s work on anchoring and adjustment provides a good foundational understanding of this concept [1].

In a previous article, we discussed the concept of ‘form’ in football and how it can be considered a form of anchoring. The ‘hot-hand fallacy,’ the belief that a player or team on a winning streak has a higher chance of winning their next game, is based on the anchor of recent success. However, each game is a separate event, and the outcome of one game doesn’t necessarily influence the next [2].

2. Loss Aversion in Football and Sports Trading

Loss aversion’s influence extends beyond the decisions made on the field to those who participate in sports trading and betting. Traders often exhibit this bias, adopting strategies that might reduce potential losses but could limit their overall return.

One common manifestation of this in football betting is the use of double chance bets as a risk reduction strategy. A double chance bet allows you to cover two of the three possible outcomes in a football match with one bet. It includes combinations like Home-Draw, Draw-Away, or Home-Away. This strategy seems to offer a safety net as the bet will win as long as the backed team does not lose.

While this approach might appear to decrease the risk associated with unpredictable match outcomes, our analysis in a previous series of articles showed that such strategies can negatively affect the return. This is mainly because, in football, draws are generally not a desired outcome for teams but a result of various events and dynamics during the match. Teams, especially in the era of three points for a win, strive to secure a victory rather than settling for a single point from a draw.

One factor that can lead to late match draws is the manifestation of loss aversion on the field. Teams holding a narrow lead towards the end of the match may adopt more defensive strategies, trying to maintain their advantage rather than seeking to extend it. This approach, while seemingly reducing the risk of conceding a goal, often invites pressure from the opposing team, increasing the likelihood of a late equalizer and resulting in a draw.

These tendencies, both in on-field strategies and betting behaviours, underline the impact of loss aversion. Acknowledging these biases is the first step in improving our decision-making processes, both in sports trading and in developing more refined football predictions.

The work of behavioural economists, such as Daniel Kahneman and Amos Tversky [3], offers us valuable insights into these biases. Incorporating these concepts in our analysis not only improves our understanding of football dynamics but also enhances the sophistication of our predictive models. The marriage of sports analysis and behavioural economics is a promising avenue for gaining deeper insights into the complex world of football.

3. Herd Mentality in Football and Sports Trading

Herd behaviour, a well-studied phenomenon in behavioural economics, is not exclusive to financial markets — it permeates into the world of football as well. Herd mentality refers to situations where individuals mimic the actions of a larger group, irrespective of whether those actions are rational or not. This mimicry, while seemingly simplistic, can have profound implications for football, sports trading, and, by extension, our predictive models at Deep Green.

Consider the tactical aspect of football, where successful strategies or styles of play are often emulated by other teams. For example, the ‘tiki-taka’ style popularized by Barcelona under Pep Guardiola or the ‘gegenpressing’ approach propagated by Jurgen Klopp at Liverpool were strategies that many teams attempted to replicate. While this mimicry testifies to the success of these styles, it can also lead to predictability, making a team vulnerable to well-prepared opponents. An in-depth exploration of this phenomenon in economics is provided by Banerjee[4].

The application of herd mentality is equally striking in the realm of sports trading. Here, market odds are largely shaped by the weight of money bet on particular outcomes. The result is a substantial number of bettors piling onto the same bet, swayed by the market’s direction rather than by objective evaluation. This creates a situation where the odds may not accurately reflect the teams’ underlying performances.

This dynamic can lead to misplaced confidence among traders. When odds suggest a strong favourite, traders might erroneously believe that the favourite's victory is almost a certainty, irrespective of whether the team’s on-field performance supports this belief. This perception can be particularly misleading for late bettors, who are faced with diminished returns due to shortening odds as more bets pile onto the favourite.

In contrast, the odds might paint the picture of a clear favourite when, in reality, the underdog’s performance doesn’t lag significantly behind the favourite's, or might even outperform them. This can cause traders to overlook potential valuable betting opportunities.

In these scenarios, SportGPT-1 finds its unique edge. By analysing a team’s actual performance metrics and staying unaffected by market direction, SportGPT-1 can offer a more accurate representation of match dynamics. Particularly in situations where market odds are driven by herd mentality rather than performance indicators, SportGPT-1 can provide a clearer understanding of the situation and reveal potential betting opportunities.

The concept of herd mentality, a cornerstone of behavioural economics, thus offers an intriguing lens to view and understand the world of football and sports trading. By integrating these insights with our rigorous data analysis, we at Deep Green aim to bring you the most comprehensive and accurate predictions possible.

4. Overconfidence

Overconfidence, a widespread cognitive bias, can be described as an unwarranted faith in one’s abilities, skills, or judgments. This overestimation often leads individuals to underestimate risks and overestimate their capacity to overcome challenges.

In the world of football, overconfidence can significantly affect the performance of teams and players. When a team, for example, is on a winning streak, players and even the coaching staff may fall prey to overconfidence. They might begin to believe that their victories are solely the result of their skills and strategies, thereby undervaluing the role of other contributing factors such as luck, the weaknesses of the opposing teams, or situational advantages. This inflated self-perception can lead them to become complacent in their preparations for future matches, underestimating the capabilities of their upcoming opponents [5].

An overconfident player might take unnecessary risks on the field, believing they can pull off ambitious moves when a simpler, more effective option might be available. They may attempt to make that spectacular long shot or attempt a complex play, rather than opting for a pass that would keep possession of the ball. While these moments can sometimes lead to spectacular goals and memorable plays, they often result in missed opportunities and lost possession [6].

Furthermore, overconfidence can lead to tensions within the team. Players, believing too much in their abilities, may dismiss constructive criticism, resist cooperative play, or challenge the tactical decisions of the coach. This can disrupt team dynamics, negatively impacting morale and performance [7].

In trading and financial markets, overconfidence can lead to equally problematic outcomes. Traders may overestimate their ability to predict market movements, underestimate the potential risks, and as a result, make poorly judged trades. This overconfidence often results in traders taking on too much risk, ultimately leading to substantial losses when their predictions don’t pan out [8].

Understanding overconfidence, its potential consequences, and the situations in which it is most likely to occur, can greatly enhance decision-making processes in both football and trading. Recognizing the signs of overconfidence allows teams, players, and traders to take a step back, reassess their strategies, and approach their next moves with a more balanced, realistic perspective [9].

To combat this, SportGPT-1 employs a 10-level risk assessment system. This system aims to reduce, if not completely eliminate, overconfidence by providing a clear view of the real risks involved, promoting better informed and more strategic decision-making.

Conclusion: The Interplay of Behavioural Economics and Football

Behavioural economics, with its focus on the irrational and emotional aspects of decision-making, has provided a valuable perspective on football’s intricate dynamics. By examining the cognitive biases at play and their manifestations within football, we can start to form a more nuanced understanding of the strategies and behaviours observed in the sport.

Anchoring, loss aversion, and herd behaviour, key concepts of behavioural economics, all find parallels in football. Anchoring, the reliance on an initial piece of information, affects the perception of player performances and team abilities, often resulting in skewed expectations. Loss aversion, the fear of losses exceeding the desire for similar gains, influences both on-field strategies and betting behaviours, potentially limiting overall returns. Herd mentality, the tendency to mimic the actions of a group, permeates the tactical aspects of football and sports trading, resulting in predictable patterns and misjudged opportunities.

Behavioural economics gives us the tools to recognize these tendencies, providing a valuable first step towards enhancing our predictive models for football. Understanding these biases helps us reassess our strategies, making it possible to refine our decision-making processes and gain a more sophisticated understanding of football dynamics.

However, it’s important to remember that this exploration is not exhaustive. The world of behavioural economics and football is vast, with numerous other biases and theories left untouched in this initial article. Yet, the concepts we’ve discussed provide a solid foundation upon which to build a more comprehensive understanding of the interplay between human behaviour and football.

As we conclude this first part of our series, we want to underscore that the marriage of sports analysis and behavioural economics opens up a promising avenue for deeper insights into the complex world of football. By applying these concepts to our rigorous data analysis, we at Deep Green aim to provide you with comprehensive, accurate, and thought-provoking football predictions.

We hope you found this exploration of behavioural economics in football insightful and thought-provoking. As we journey further into this series, we invite you to continue learning with us, challenging conventional perspectives, and uncovering hidden dynamics within the beautiful game. Don’t miss our next article where we’ll delve into the Adaptive Market Hypothesis. Stay tuned and keep exploring the fascinating intersection of psychology, economics, and football with us!

Best,

The Deep Green Team

References

1. Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science.
2. Miller, J.B., & Sanjurjo, A. (2018). Surprised by the Gambler’s and Hot Hand Fallacies? A Truth in the Law of Small Numbers. IGIER Working Paper №552.
3. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica.
4. Banerjee, A.V. (1992). A Simple Model of Herd Behavior. The Quarterly Journal of Economics.
5. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.
6. Rössler, R., Noël, B., Stoll, S., Wyss, T., & Seil, R. (2017). Risk factors for football injuries: a systematic review of the literature. Physical Education & Sport.
7. Jordan, J., & Audia, P. (2012). Self-enhancement and learning from performance feedback. Academy of Management Review.
8. Odean, T. (1998). Volume, volatility, price, and profit when all traders are above average. The Journal of Finance.
9. Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence. Psychological review.