The era of big data with machine learning, artificial intelligence and algorithms is here. The future is said to be decisions made according to insights from data with its objectivity being superior to human subjectivity.

Sport has not been immune to this spreading ideology. Making decisions on data, from player recruitment to evaluating individual performance, has gained widespread acceptance. This was helped by Moneyball, the book and film starring Brad Pitt, that told the story of how a baseball team used statistical insights to achieve success. Data scientists are now working in top teams.

The truth is, in all the excitement of the potential data promises, its capabilities have been overplayed. It can certainly help improve decisions, but it is not capable of completely eliminating subjectivity nor is human judgement obsolete if we want to make decisions that are effective as possible.

Brad Pitt in Moneyball
Brad Pitt played Billy Beane, a baseball executive, in the Hollywood film Moneyball

The world is hugely complex, and this is equally true of sport. Even in those with minimal rules and limited choices, like chess, have more possible game iterations than atoms in the observable universe!

Making decisions is about predicting future outcomes, but the environment comprises of so many variables and possible situations. How can we account for such a vastly complicated world that is impossible to fully comprehend?

The truth is we can’t. So, we simplify. We convert the reality of a world of complex systems with interconnections of numerous elements and feedback loops into one based on basic linear cause-and-effect relationships; i.e. changing this here will consistently produce that effect there.

But this comes with a cost. In reducing the complexity we risk losing the nuance and arriving at conclusions that might not provide a true understanding of reality.

In the filtering process we try our best to consider only the relevant data (signal) that impacts the outcome of our decisions and dismiss the irrelevant (noise). However, it is unavoidable that we unintentionally keep irrelevant data and omit some of the relevant.

With the information captured we try to identify patterns to inform us of likely cause-and-effects chains that can be used to guide us in our future decisions and actions. To give a straightforward example, maybe we find a link between having more ball possession and an increased chance of winning.

This belief becomes a useful rule of thumb. We apply these shortcuts to guide us as we go through the world. They aim to reveal underlying truths while preventing us from becoming overwhelmed by having to rigorously assess the best option every time we encounter a new situation.

But they come with the risk that during the process of arriving at these conclusions either the quality of the data inputted wasn’t good enough or the cause-and-effect relationship observed was misjudged and our assumption is false. This type of error is known as a bias.

For the ball possession example, maybe information about the opponent’s tactics were not considered in the model and so having more possession against certain styles of play might have no influence or even make us worse. Or possibly the link between more possession and winning wasn’t causal, one doesn’t directly lead to the other. So, players were encouraged to increase possession, but they stopped making forward passes and the team’s performance deteriorated.

A good example of how we simplify but lose important context is highlighted in the NFL Draft. They assess players through extensive testing that incorporate speed, strength, power, agility, various technical skills, psychometric ability, physical measurements, injury evaluations and cognitive capacity.

Yet it is still consistently very unreliable at identifying who will be successful. In fact, possibly the greatest quarter-back in NFL history was the 199th pick and that only included the players entering the league in one single year (which brings into question those who claim they can identify children that will become elite players but that’s a whole other topic).

Tom Brady was picked at 199 in draft despite extensive testing
Picked at 199 in the draft, Tom Brady has a record 4 Super Bowl MVP awards

The fact is a player’s true playing ability depends on those areas tested and many more,  plus how they all interact with each other. This is the real world of complex systems with interconnectedness of numerous elements that can’t be accurately replicated when simplifying.

The general process of arriving to an assumed cause-and-effect relationship between two things is the same for computers as it is for humans. The difference is a computer uses a database, mathematics and computer processing and humans use memory and the brain’s processing power to subconsciously identify patterns.

Both are very good at giving us a generally accurate picture of how things work but both have biases and subjectivity that occasionally lead to errors. This is because, due to the complexity mentioned earlier, there is always a gap between simplified models of the world and reality.

It is not a case of objective data versus subjective human intuition as is often claimed.

So, if both have biases, which method should be preferred for making decisions? Neither. Instead, they should be used together because their differences can cancel each other out.

When people at a fair guess the number of sweets in a jar, the average of everyone’s guess is usually very close to the actual amount. Randomness means the false information that overestimates is cancelled out, more or less, by the false information that underestimates.  What remains is the information based on true beliefs.

Further, each has different strengths and weaknesses. There is a version of chess called freestyle where players are allowed to use computers. Amateur players with everyday computers won tournaments against the best supercomputers. While computers have the advantage of immense calculation power to explore data for patterns, humans can apply a strategic outlook whilst integrating broad concepts and principles.

Hybrid decision making with humans and computers

There are many fields outside sport that have attempted to rely on exclusively data-based decision making. The initial optimism of better outcomes was misplaced, and it was realised it is essential to incorporate human judgement. Examples include court judges deciding whether suspected criminals should be awarded bail after assessing the chances they will reoffend or pathologists who try to identify whether human tissue is cancerous or not.

The push for decisions based only on data suggests this lesson is yet to be learned in sport. The simplification from the real world means the utopian ideal of perfectly accurate decisions are unattainable but using a hybrid method will get us as close as possible.

At a time when it is popular to adopt dogmatic positions on the extremes, in this case either having an old school and out of touch approach or a numbers geek that doesn’t understand the game, there needs to be some balance. Truth often lies in the less glamorous position, nearer the middle of the continuum.

In sport, just as in life, the approach we choose to take is determined by our beliefs about the relationships that exist in the world. If we take this action, we’ll get this result. In recent years the increased capabilities of computers and data collection has provided us with a new tool that can help us arrive at better conclusions and decisions. But it would be foolish to discount the value of human intuition, a tool that has evolved over millions of years for the same objective.

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  1. Richard SPJ Frost

    Interesting as ever. i do love the film “Moneyball” but some sports have tried and failed in this area. For me a phrase in baseball like “Clutch” player (delivers at big moments) cannot be known at youth level, as your Tom Brady example shows.
    As an Everton fc fan, i am guessing a lot of our disastrous transfer policies have been based on data. Handling pressure, decision making (a big part of futsal) are areas not easy to “data sort”. A player could have a pass % of 90 but its because they play safe and predictable. i like players who pass and run in unorthodox ways.

    Having said that as an MS Excel geek, data is a big help. i think its sometimes down to how the manager uses this. The current Stoke FC manager Martin O’Neill is being praised by players because he barely goes into the data with the players. He filters it accordingly but doesnt burden them.

    Ciaches become too academic: one former football player who i am guessing lives futsal, Marco van Basten, (his recommendations for rule changes in football mirrored futsal) i used to see him just writing notes all the time during matchez but not interacting with the players.

    • Doug Reed

      Thanks for the comments Richard.

      In sport we are dealing with people and these are one of the complex systems referenced in the article. There will never be only one way to manage them as every person/group is unique and every situation different. What works with one player in one moment may not be appropriate at a different time.

      Insights from data is one tool that can help and contribute but the art is how to apply the “science”. We have to be aware of the strengths and weaknesses of both data and human intuition. To offer the best solutions both should be considered.

  2. JD

    While data can help select for valid strengths and attributes that make for what should be a ‘good player’, were the team admins / management selected by a similar process? Can AI now or ever include every angle? Interactions between players surely change the players as games progress. Sticking to the system and doing the ‘right thing’ vs creative, visionary play driven by an inner connectedness between players where the ‘system’ evolves in harmony with need and opportunity.

    Can AI ever take into account the unique traits and abilities of players and predict the optimal path to achieve the highest performance?

    May be the question is if there is such a thing as a perfect player or a perfect system of play that a team can be programmed to follow. If you need fully interchangeable players, then a blunt, almost mechanical approach to team selection might work. But for really getting near to perfection I think the system must remain human at heart. Individual passion can drive the highest levels of performance while the job of the coaching team should be to craft those individuals to form the best performing team that evolves in harmony while it plays.

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