Thursday, June 12, 2014

What does the learning game have to learn from the World Cup?

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. 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’s 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 the 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 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 are 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.
Bias
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’re part of the traditional game.
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 who really count, 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.

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’s 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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