Wednesday, March 20, 2024

What does the learning game have to learn from the beautiful game - football? Data really matters...

Most professional sports employ data to improve performance. Yet football (soccer), in data terms, is not so much the beautiful game as a rather messy and random affair or in statistical terms – stochastic. This refers to the level of unpredictability and the influence of random factors on its outcomes. Football is often considered highly stochastic compared to some other sports due its low scoring, flow with few stoppages and more unpredictable events and player performance and other variables.

It's important to note that all sports have some level of stochasticity but comparatively, sports like tennis and basketball have higher scoring and more frequent scoring opportunities, which can reduce the impact of a single random event. Baseball and cricket, with their more structured and turn-based nature, allow for more consistent application of skill and strategy, though they still have elements of unpredictability.

Unlike many sports, such as basketball, American football and baseball, in soccer the ball changes sides so often it is difficult to identify patterns in the numbers. That is not to say they don’t exist. As usual, the data, although messy, reveals some surprising facts:

1. Corners don’t matter that much. Mourino was amazed when English supporters cheered corners, as he knew they rarely led to goals. The stats support this. There is no correlation between corners and goals – the correlation is essentially zero.

2. Then there’s an old myth that teams are at their most vulnerable after scoring a goal. Teams are not more vulnerable immediately after scoring goal. In fact, the numbers show that this is the least likely time that a goal will be conceded.

3. Coin toss is the most significant factor in penalty-shootout success. 60% of all penalty shootouts have been won by coin toss winners. Goalkeepers who mess about on the line and hold their hands high to look bigger also have an effect, making a miss more likely. Standing 10 cms to one side also has a significant, almost unconscious effect on the goalscorer, making one side look more tempting.

4. It’s also a game of turnovers. The vast amount of moves never go beyond four passes. This has huge consequences – ‘pressing’ matters, especially in final third of field. Avoiding turnovers is perhaps the most important tactic in football.

These were just a few of the secrets revealed by Chris Anderson and David Sally, two academics, from Cornell and Dartmouth, in their book The Numbers Game – Why Everything You Know About Football is Wrong.

Artificial Intelligence

A new tool has caused a bit of a splash, called Tactic AI. A paper in Nature confirms its use in the taking of corners – although, As I say above, this is an odd focus as other tactics are more valuable. Google have worked for four years at Liverpool FC. Yet it is in other areas that data matters more, in scouting and transfers. Brighton (my home team) are lowest in Premiership on corners won but have one of the best track records in transfers, as they use data more widely. Brighton have sold on a nearly decent Premiership team to rest of Premiership: Sanchez, Curucalla, White, Bissouma, Ciaicedo, MacAllister, Trossard, Burn, Maupay, Knockaert... for getting on to a half a billion. These are key players in these other top teams.


Seasoned managers, coaches, trainers, players often get it wrong because, in football, our cognitive biases exaggerate individual events. We exaggerate the positives and what is obvious and seen at the expense of the hidden, subtle and negative. A good example is defending. Mancini may have been the greatest defender ever because of what he never did – tackle. We prize tackling, yet it is often a weakness not a strength. We think that corners matter when they don’t. Similarly in education, we prize the opinions of seasoned practitioners over the data: exams, uniforms, one hour lectures, one hour lessons and all sorts of specious things just because they are part of the traditional game. Yet, what good teachers don't do really matters. This is why guided coaching and tons of deliberate and variable practice matter in sports but is rarely taken seriously in education.

Soccer and learning

If a sport like football, which is random and chaotic, can benefit from data and algorithms that guide action such as buying players, picking players, strategy, and tactics, then surely something far more predictable, such as learning, will benefit from such an approach? What we can learn is that data about the ‘players’ is vital, what they do, when they do it and what leads to positive outcomes. It is this focus on the performance of the people that really counts, a personalised approach to learners, that is so often missing in learning.

Education gathers wrong data

Education has, perhaps, been gathering the wrong data – bums on seats, contact time, course completion, results of summative assessments, even happy sheets. What is missing is the more fine-grained data about what works and doesn’t work. Data about the learner’s progress. Here we can lever data, through algorithms to improve each student’s performance as they take a learning journey. We need the sort of data that a satnav uses to identify where they start, where they’re going and, when they go off-piste, how to get them back on track. In modern sports going over videos of a team's performance and those of the opposition has become normal, as has the gathering of stats. What has most often led to the goals you've scored this season? It may not be the quality of the striker but what wing is better, the feeder players from midfield, the importance of dead-ball opportunities.

Just as the ‘nay-sayers’ in football claimed that the numbers would have no role to play in performance, as it was all down to good coaches, trainers and scouts, so education claims that it is all down to good teachers. This is a stupid, silver-bullet response to a complex set of problems. It is partly down to good teachers but aided by good data, learners have the most to gain from other interventions. Education needs to take a far more critical look at pedagogic change and admit that critical analysis leads to better outcomes. This means using data, especially personal data, in real time to improve learner performance


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