21 Jun A new paradigm to understand success in professional football: analysis of match statistics in LaLiga for 8 complete seasons
Practical Summary for Coaches, Performance Analysts and Staff
This study dives into eight full seasons of LaLiga (2010–2018), analyzing over 3,000 matches to answer a central question: What truly explains why teams succeed over the course of a season?
Forget possession for a moment. The analysis reveals that shot quality and shooting accuracy are far more predictive of final league position than metrics like passes or ball control. In fact, the teams that consistently finish at the top aren’t necessarily those that dominate possession—but those that turn their chances into goals efficiently and avoid conceding high-quality shots.
On the attacking side, the key driver is shooting accuracy, followed by number of shots and corners. These are practical indicators of a team’s ability to reach and threaten the opponent’s box with purpose. On the defensive side, the most powerful variable is not how many fouls you commit or yellow cards received—it’s your ability to prevent goals from the shots you do concede.
Key takeaways for your training & match planning:
- Design attacking strategies that maximize shot efficiency, not just volume.
- Encourage defensive structures that reduce clear shooting opportunities for the opponent.
- Rethink over-reliance on possession-based metrics when evaluating team performance.
- Use match data to prioritize drills and tactical plans aligned with high-impact variables (e.g., finishing under pressure, compact defensive blocks).
The model developed in the paper was able to predict up to 84.1% of the variance in end-of-season points using attacking stats, and 73.5% using defensive stats—a level of accuracy that confirms its value for elite football decision-making.
This evidence supports a more vertical, direct playing style, especially in LaLiga, where accuracy in both boxes seems to matter more than the path in between.
🔗 Full article (DOI): https://doi.org/10.1080/24748668.2019.1632580