Data-driven decision making is harder than you think
By: Mehul Nagpurkar
Tags: Data Driven Decisions, Business Analytics, Life Lessons, Soccer, Reflections

One of my earliest memories of football dates back to the 2006 FIFA World Cup finals between Italy and France. Before the infamous headbutt, Zinedine Zidane had opened the scoring for France through a penalty kick. I remember watching the match live and I was vehemently supporting France during that World Cup. I was on my seat and I could hear my heart pounding in my chest just as Zidane walked towards the spot-kick. I couldn’t help but feel bad for Gianluigi Buffon (Italian team’s goalkeeper). He is now expected to save a shot from one of the world’s best players at the time, for a harsh tackle that his teammate committed. It's surprising how a lot of what happens in sports mirrors life.

The football is 22 centimetres in diameter while the goal post is 7.32 meters wide and 2.44 meters high. To have a player in his prime take a kick from 11 meters away, you barely have 0.1 seconds to react with all of Italy’s hopes resting on your shoulders. That is a tremendous amount of pressure to deal with. Think about the striker too in this case, if he does score, that is expected of him. But if he misses, he would have let 66 million French people (and me) down in the process.

Source: YouTube

Referencing the novel by Prakash Iyer, ‘The Habit of Winning’ which highlights the research done by a team of scholars in Israel to understand the goalkeeper’s mindset. This team studied 286 penalty kicks from major league football games around the world. In the 0.1 seconds that the goalkeeper has to react, the goalkeeper must guess which way the ball will go. S/he has to commit to a dive left/right or stay in the centre. The research team then aggregated the statistics of the direction of the kicks taken by the strikers. The research concluded that the goalkeeper’s best chance of blocking a penalty kick is if s/he doesn’t dive but stays put in the centre.

The probability of stopping the kick is highest when the goalkeeper did not move. Despite that, the team found that in 92% of the cases, the goalkeeper would commit themselves to a dive either left or right.

Zidane 7' (pen) : 2006 World Cup Finals

Surely world-class professionals like Buffon would be aware of this statistic, and yet they dive. Knowing fully well, if they stood still, it would give them the best chance of success. I believe the answer to that question lies in the human psyche, or as psychologists like to call it, Action Bias. The bias for action that most sportspeople (i.e. high achievers) have. Let me try to illustrate action bias in this context, there are two independent variables and one dependent variable.

Independent variables:

1. Goalkeepers Decision: Dive or Stand Still

2. Outcome: Goal Scored or Stopped/Missed

Dependent variable:

3. People’s reaction

If the goal is saved, everyone is going to be delighted but if the goalkeeper does manage to save it with a dive, s/he is going to come off looking spectacular. Things get more interesting if you think about the situation when the goal is scored. If the goalkeeper decided to stand still, s/he would be heavily criticized and subject to ridicule. Angered comments like ‘Didn’t even try!’ and ‘They don’t get paid 150k a week to stand in like that’ would be chanted amongst friends and fans. However, if the goalkeeper dove and the goal still went in. People would be more sympathetic about the fact that it isn’t much you can do when somebody is firing from 11 meters.

Which is why even if not consciously goalkeepers around the world, match after match dive. When standing still could perhaps improve their chances of success. This brings me back to the essence of the article, why data-driven decision making is harder for individuals. We all face the goalkeeper syndrome, conditioned by our bias for action. We are compelled to act, inaction is not seen very favourably. Even if research shows us otherwise, we try to rationalize the act of doing something. A lot of businesses and in turn people use data to support their hypothesis, their gut and the actions they inherently want to take.

I think it is just as important if not more to be able to realize that sometimes doing nothing is just as powerful. It explains CEO behaviour, whenever a new CEO joins a firm, they try to change things up. Be it

acquiring new businesses, divesting, upsizing or downsizing, decentralizing operations,

anything they can do to make their presence felt throughout the organization. Almost always, these decisions are for the benefit of the firm but I can’t help but feel some of it has to do with the fact that they want to be perceived as proactively doing something to the shareholders.

I remember analyzing salary data of an organization as part of our statistics learning course. The objective was to check for gender discrimination among older female employees. Initial graphs and statistics showed that the median salary for a male employee was higher than that of a female employee, I almost felt compelled to use this as evidence to support the hypothesis. I did not see a reason why not, my mind had been made because deep down I wanted the data to tell me that there is discrimination.

Descriptive Statistics for Salary Data

Mean Value of Salary (000's)

It was only when the lecturer challenged my findings that I collected other parameters such as levels of education and years of relevant work experience. After which the data did not show enough statistical evidence to support the hypothesis.

The difference of Experience in Male and Females

It is scary to realize how much of our biases play into our decision making, decisions that you inherently think are being driven by data. I personally feel this is where the challenge lies, we are humans and our gut sometimes can be a very powerful tool. The ability to differentiate between the decisions that should be made by data and then stepping back to make sure that those decisions are unbiased is a true skill I hope we are able to cultivate.