Analyzing the Art of the Throw-In: A Tactical Breakdown of Soccer's Most Underappreciated Set Piece
Building on the foundation laid by Eliot's research, the updated analysis incorporates a more comprehensive approach, examining the goals added for possessions following throws to assess the value of throw choices. This expanded framework enables a more nuanced understanding of the impact of throw-ins on the game, allowing for the identification of the most effective throw-in strategies and the players who excel in this often-overlooked aspect of the game. By evaluating the goals added for every possession following each throw, it is possible to determine whether throws down the line, despite their lower success rate, offer increased goal-scoring opportunities. Furthermore, this analysis can help teams strike a balance between minimizing the risk of turnovers and generating attacking value, ultimately informing their throw-in tactics and improving their overall performance. The updated data provides a more detailed picture of the best throw choices in different areas of the pitch, offering valuable insights for coaches and players seeking to optimize their throw-in strategy.
The complexities of throw-in strategies become increasingly apparent when examining the varying contexts in which they occur. A throw received on the half line, for instance, could have originated from numerous positions, each with its unique implications. The distribution of throw targets in the attacking third differs significantly from those in the defensive third, making it essential to visualize these patterns to gain a deeper understanding.
Interactive visualization tools can facilitate this process, enabling a more nuanced exploration of throw-in data. By examining the touchlines and metrics, distinct patterns emerge, revealing how throws tend to unfold and their eventual outcomes. Both sidelines exhibit similar patterns, allowing for a focus on the variations that occur when moving up and down the touchline.
Throws taken from defensive corners are relatively rare and pose significant risks, with low retention rates and possession values. However, as one progresses up the touchline, patterns begin to emerge. Forward throws towards the goal are popular but often result in low retention rates and possession values. In contrast, backwards passes yield more favorable retention rates, while throws directed towards a team's own goal surprisingly exhibit the highest possession values.
As the midpoint of the field is approached, these patterns evolve and become more pronounced. The distribution of throw-in targets expands, and it becomes clear that forward throws in the defensive half offer limited value, consistently resulting in lower retention rates and possession values. Backwards throws, on the other hand, yield better retention and possession values. The zones with the highest possession values are often the furthest, including backwards, lateral, and attacking directions. Long throws to teammates in open spaces appear to be high-upside choices, likely due to the opposition's tendency to condense the area.
In the attacking half, the options available to the ball thrower increase significantly. Placing the ball near the box becomes a high-value option, and new opportunities emerge near the goal line. The retention rates of forward passes improve, surpassing completion rates when thrown into the box. However, the risks also increase, with a higher incidence of negative possession values. Backwards passes, while less reliable, can still generate dangerous possession in certain areas of the final third. Short throws also become viable, as the reduced distance to goal may force defenders into withdrawn positions, creating more space. Ultimately, long throws into the mixer remain a worthwhile strategy, offering significant potential for creating scoring opportunities.
The geography of the pitch plays a crucial role in optimizing throw-ins, with team strategy influencing the number and location of throws. Players can better understand where to look and move when putting the ball back into play, taking into account the team's overall approach. For instance, crowding everyone forward can be a bad strategy unless the team is built to win 50-50 challenges or can reach the box.
Using a modeling framework similar to Eliot's, but with some tweaks, the analysis reveals that possession definitions can impact the results. The possessions defined for goals added show that far fewer goals added possessions reached longer than 7 seconds after a throw, leading to lower retention rates. However, the conclusions drawn from the analysis remain intact, if not stronger.
The results of this analysis are consistent with previous models and interactive visualizations of 13 seasons of MLS throw-ins. Completion and retention rates are low for throws down the line towards goal, but increase as the angle opens and pans back towards a player's own goal. The chances of throw success also increase as the throw-in gets further from a player's own goal, with a slight decline as you approach the opponent's goal.
Incorporating the distance thrown as a continuous variable improves model accuracy, with the current xThrow model tracking observed values closer than previous models. Taking throws within 10 seconds of the previous on-ball action maximizes the chance of throw success. However, the analysis also reveals that retention and completion rates are overrated, at least in the final third. The outcome of an improbable event, such as a long throw into the box, can be so worthwhile that the completion rate becomes secondary.
The relationship between distance thrown and success is complex, with xThrow indicating that the odds of success decline with longer throws, while xRetain suggests it has little effect on possession. However, long throws have more value in terms of goals added, with a linear relationship between distance and value. The analysis also shows that throw angle has a significant impact on outcomes, with xThrow and xRetain increasing as the angle approaches a lateral throw and plateauing once throws pass 90 degrees.
Time since last action is a consistent trend across all throw outcomes, with goals added value peaking just short of five seconds alongside xThrow and xRetain. While these models provide valuable insights, they are limited by their reliance on event data, which cannot capture the spatial context surrounding throw-in decisions and outcomes. Clubs can build on these findings by developing theories, testing them with film study and scouting, and operationalizing them using tracking data to gain a more nuanced understanding of throw-in strategies.
Modeling possession g+ based on reliable patterns of throw-ins allows for the identification of players who consistently generate more value than expected. By examining 100 career throw-ins between 2013 and 2025, it is possible to distinguish between skilled players and those who may be benefiting from small sample sizes.
Excluding strikers due to their limited involvement in throw-ins, the analysis focuses on other positions. Ranking players by per-throw adjusted model overperformance reveals that Djordje Mihailovic leads the league in g+ per throw. Notably, Michael Boxall, known for his long throws, scores lower in this metric.
However, successful throw-ins require not only a skilled thrower but also a suitable target. Coaching teammates to create space and opportunities within seconds of the ball going out is crucial for maximizing team success. Therefore, identifying players who can maximize the value of throws they receive is essential.
The list of top receivers is notable for featuring players who excel at heading, holding up the ball, and retaining possession. Andrea Pirlo stands out for his ability to elegantly receive throws and distribute the ball effectively.
To optimize throw-in strategy, several data-driven suggestions can be made. While these patterns are general and not prescriptive, they can help teams get more value from throw-ins in the long run. Key principles include getting open as soon as possible, avoiding defensive third throw-ins, prioritizing possession retention, and targeting open teammates near the middle of the pitch.
Interestingly, these findings seem to encourage a possession-style approach that utilizes space and smart movement rather than relying on brute force and long throws. This contrasts with the typical emphasis on field position in other aspects of the game. The value of backwards passes, for instance, lies in their ability to help teams maintain possession and recirculate the ball.
Ultimately, the key to successful throw-in strategies may lie in exploiting space with quick recognition and smart movement by both the thrower and the target. By focusing on these aspects, teams can unlock the full potential of throw-ins and gain a competitive edge.
The complexities of throw-in strategies become increasingly apparent when examining the varying contexts in which they occur. A throw received on the half line, for instance, could have originated from numerous positions, each with its unique implications. The distribution of throw targets in the attacking third differs significantly from those in the defensive third, making it essential to visualize these patterns to gain a deeper understanding.
Interactive visualization tools can facilitate this process, enabling a more nuanced exploration of throw-in data. By examining the touchlines and metrics, distinct patterns emerge, revealing how throws tend to unfold and their eventual outcomes. Both sidelines exhibit similar patterns, allowing for a focus on the variations that occur when moving up and down the touchline.
Throws taken from defensive corners are relatively rare and pose significant risks, with low retention rates and possession values. However, as one progresses up the touchline, patterns begin to emerge. Forward throws towards the goal are popular but often result in low retention rates and possession values. In contrast, backwards passes yield more favorable retention rates, while throws directed towards a team's own goal surprisingly exhibit the highest possession values.
As the midpoint of the field is approached, these patterns evolve and become more pronounced. The distribution of throw-in targets expands, and it becomes clear that forward throws in the defensive half offer limited value, consistently resulting in lower retention rates and possession values. Backwards throws, on the other hand, yield better retention and possession values. The zones with the highest possession values are often the furthest, including backwards, lateral, and attacking directions. Long throws to teammates in open spaces appear to be high-upside choices, likely due to the opposition's tendency to condense the area.
In the attacking half, the options available to the ball thrower increase significantly. Placing the ball near the box becomes a high-value option, and new opportunities emerge near the goal line. The retention rates of forward passes improve, surpassing completion rates when thrown into the box. However, the risks also increase, with a higher incidence of negative possession values. Backwards passes, while less reliable, can still generate dangerous possession in certain areas of the final third. Short throws also become viable, as the reduced distance to goal may force defenders into withdrawn positions, creating more space. Ultimately, long throws into the mixer remain a worthwhile strategy, offering significant potential for creating scoring opportunities.
The geography of the pitch plays a crucial role in optimizing throw-ins, with team strategy influencing the number and location of throws. Players can better understand where to look and move when putting the ball back into play, taking into account the team's overall approach. For instance, crowding everyone forward can be a bad strategy unless the team is built to win 50-50 challenges or can reach the box.
Using a modeling framework similar to Eliot's, but with some tweaks, the analysis reveals that possession definitions can impact the results. The possessions defined for goals added show that far fewer goals added possessions reached longer than 7 seconds after a throw, leading to lower retention rates. However, the conclusions drawn from the analysis remain intact, if not stronger.
The results of this analysis are consistent with previous models and interactive visualizations of 13 seasons of MLS throw-ins. Completion and retention rates are low for throws down the line towards goal, but increase as the angle opens and pans back towards a player's own goal. The chances of throw success also increase as the throw-in gets further from a player's own goal, with a slight decline as you approach the opponent's goal.
Incorporating the distance thrown as a continuous variable improves model accuracy, with the current xThrow model tracking observed values closer than previous models. Taking throws within 10 seconds of the previous on-ball action maximizes the chance of throw success. However, the analysis also reveals that retention and completion rates are overrated, at least in the final third. The outcome of an improbable event, such as a long throw into the box, can be so worthwhile that the completion rate becomes secondary.
The relationship between distance thrown and success is complex, with xThrow indicating that the odds of success decline with longer throws, while xRetain suggests it has little effect on possession. However, long throws have more value in terms of goals added, with a linear relationship between distance and value. The analysis also shows that throw angle has a significant impact on outcomes, with xThrow and xRetain increasing as the angle approaches a lateral throw and plateauing once throws pass 90 degrees.
Time since last action is a consistent trend across all throw outcomes, with goals added value peaking just short of five seconds alongside xThrow and xRetain. While these models provide valuable insights, they are limited by their reliance on event data, which cannot capture the spatial context surrounding throw-in decisions and outcomes. Clubs can build on these findings by developing theories, testing them with film study and scouting, and operationalizing them using tracking data to gain a more nuanced understanding of throw-in strategies.
Modeling possession g+ based on reliable patterns of throw-ins allows for the identification of players who consistently generate more value than expected. By examining 100 career throw-ins between 2013 and 2025, it is possible to distinguish between skilled players and those who may be benefiting from small sample sizes.
Excluding strikers due to their limited involvement in throw-ins, the analysis focuses on other positions. Ranking players by per-throw adjusted model overperformance reveals that Djordje Mihailovic leads the league in g+ per throw. Notably, Michael Boxall, known for his long throws, scores lower in this metric.
However, successful throw-ins require not only a skilled thrower but also a suitable target. Coaching teammates to create space and opportunities within seconds of the ball going out is crucial for maximizing team success. Therefore, identifying players who can maximize the value of throws they receive is essential.
The list of top receivers is notable for featuring players who excel at heading, holding up the ball, and retaining possession. Andrea Pirlo stands out for his ability to elegantly receive throws and distribute the ball effectively.
To optimize throw-in strategy, several data-driven suggestions can be made. While these patterns are general and not prescriptive, they can help teams get more value from throw-ins in the long run. Key principles include getting open as soon as possible, avoiding defensive third throw-ins, prioritizing possession retention, and targeting open teammates near the middle of the pitch.
Interestingly, these findings seem to encourage a possession-style approach that utilizes space and smart movement rather than relying on brute force and long throws. This contrasts with the typical emphasis on field position in other aspects of the game. The value of backwards passes, for instance, lies in their ability to help teams maintain possession and recirculate the ball.
Ultimately, the key to successful throw-in strategies may lie in exploiting space with quick recognition and smart movement by both the thrower and the target. By focusing on these aspects, teams can unlock the full potential of throw-ins and gain a competitive edge.
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