Why Analytics Don’t Work for Soccer
The Unpopular Reasons You Can't Overanalyze Soccer
This is going to be the unpopular opinion – analytics don’t work in soccer, the world’s largest sport.
With over 5 billion fans soccer dwarfs all other sports, period. It keeps growing and growing and growing. It remains unpredictable, exciting, and fun.
Analytics, which is over analyzing and predicting game outcomes and is making sports boring to watch doesn’t work in soccer. That might be a good thing.
Why Don’t Analytics Work in Soccer?
1. Size of the field.
2. Low scoring events.
3. Play conditions vary.
4. Inherent randomness.
And this is really why –
Historical data, like points scored per game and assists and passes, if you can get accurate sets globally, does little to predict a low scoring game on an enormous playing field in different countries with altitudes and climates on different time zones all vying for the same end goal. The randomness is simply nuts!
And even if you use an entropy metric which is just an added variable to try to account for the enormous uncertainty of soccer, the ability to predict a game outcome is still a toss-up.
So, What’s the Compromise if You are Really Interested in Analytics for Soccer?
Qualitative analytics.
When you take the uncertainty core to soccer, qualitative analytics becomes the only way to forecast game outcomes.
What are Qualitative Analytics?
Qualitative analytics (QA) is real-time player analysis.
QA reveals player movement (biomechanics) changes that help to explain why a player or team is gaining momentum or losing its edge — ahead of the scoreboard.
QA is a vital aspect of predictive analysis that most people overlook because it requires a combination of domain knowledge (what you know about sports skills and performance) and integrates that with current play conditions.
Bottom line: it’s easier to sit behind a computer and plug in stats to an equation than analyze sport specific real-time human movement.
But the person who can do QA has the edge to forecast player performance and forecast outcomes in real-time ahead of everyone else.
Anybody with knowledge about sports and exercise can do it!
Real-Time Analysis is the Gap.
Real-time analysis like qualitative analysis (QA) is the present and future, and it is easier than you’ve been led to believe.
Anyone with gym and/or sports experience can learn how to do QA in the real world.
Remember that QA is ongoing in real-time in the real world, not sitting behind a computer. You are out there living it, and this is the advantage.
If you see an odd movement, and you determine a reason for that strange movement could affect play you have successfully formed a decision-ready analytic and done a QA. Congratulations!
What is a Decision-Ready Analytic?
A decision-ready analytic is a live game performance insight.
It is something you see and are ready to act on. It is the ability to recognize critical movement changes or changes in player behavior that might impact play.
How Do You Analyze Player Movement?
Look at macro and micro movements.
There are two types of movements that are critical to doing biomechanics analysis.
1. Macro. Big.
2. Micro. Small.
It’s that simple.
Follow these easy live-game steps to perform a simple biomechanics analysis.
Step #1: Look at the big picture first. Whole body movements. Easy to spot.
Step #2: Second, look at smaller, joint-level movements. More difficult to spot.
Step #3: Next, think about your time in the gym or playing sports and think in terms of big strength versus small strength expression.
*If the movement is big like a squat that’s a macro movement.
* If it’s small like wrist movement that’s NOT a big strength expression. That’s a micro biomechanic. It’s joint level, and it’s a small movement.
Step #4: Finally, micro biomechanics will go in the direction of looking at small angle changes within a joint.
For example, if an athlete is a sport specific stance you look for minor angle deviations in stance that impact motion.
Macro and micro biomechanics are essential to doing biomechanics qualitative analysis; however, it is not hard to learn how to identify them and apply those changes to forecast player performance and game outcomes when you’ve been playing or watching a sport for a long time.
Remember, soccer forecasting relies heavily on current play conditions and player movement because the BIG things are obvious. Everyone is taking their best players. No one is playing with a serious injury. Everyone knows who the best teams are.
But the soccer field is enormous and creates uncertainty that all the box stats in the world can’t overcome. Human beings are unpredictable; embrace the joyous randomness of human movement and error. The World Cup and soccer are one of the last frontiers of enjoying the unexpected in sports. And, if you do see something and want to act it, engage and have fun.