Throw-ins are the most frequent set-piece in football yet historically the least analyzed. Modern analytics has revealed that throw-in quality and strategy measurably affect match outcomes. At 1X2.TV, our AI models incorporate throw-in analytics for teams where this data enhances prediction accuracy.

Throw-In Frequency and Territory

The average football match features 40-50 throw-ins, making them the most common restart. Teams that win more throw-ins in the attacking third generate additional territorial advantage. Our models track throw-in territory distribution as a proxy for sustained attacking pressure, which correlates with goal-scoring opportunities.

Long Throw Specialists

Teams with long throw specialists effectively convert throw-ins into corner kick-like set-piece opportunities. A throw-in delivered into the penalty area from the sideline creates a dangerous aerial challenge that defenses must deal with. Our models identify teams with long throw capabilities and increase their expected set-piece threat accordingly, particularly for matches involving teams with known long throw specialists.

Throw-In Retention Rates

Possession retention from throw-ins varies significantly between teams. Elite teams retain possession from approximately 60-65% of their throw-ins, while lower-quality teams may retain only 45-50%. High retention rates sustain attacking pressure and contribute to territorial dominance. Our models incorporate throw-in retention quality as a component of overall possession efficiency.

Coaching Innovation

Several clubs have invested in dedicated throw-in coaching, most notably through specialist throw-in coaches who design rehearsed routines for various pitch positions. Teams that invest in throw-in coaching show measurable improvements in throw-in retention and attacking threat from throws. Our models track these coaching innovations and adjust expectations accordingly.