The Impact of Throw-ins on Soccer Performance: A Detailed Analysis

Table of Contents

Overall Summary

Overview

This study explores the influence of throw-ins on soccer team performance, specifically within the 2018-2019 English Premier League (EPL) season. By analyzing over 16,000 throw-ins, the research investigates the relationship between throw-in success metrics (such as first contact success, possession retention, time in possession, and shot creation) and a team's final league position. Additionally, it examines how the direction and length of throw-ins affect these outcomes. The study finds that higher league positions correlate with better throw-in performance, particularly for backward and lateral throws, which challenge the traditional preference for forward throws.

Key Findings

Strengths

Areas for Improvement

Significant Elements

table

Description: Table 1 details the distribution of throw-ins across EPL teams, showing totals and averages per match for each team, vital for understanding the dataset's scope.

Relevance: This table provides context for the overall analysis, illustrating the frequency and variance of throw-ins across different teams.

figure

Description: Figure 2 visually represents the correlation between league standings and throw-in performance metrics through scatter plots, offering a clear visual insight into the study's primary findings.

Relevance: The figure effectively communicates the strength and direction of the relationships between league position and throw-in success, crucial for understanding the study's implications.

Conclusion

The study highlights the significant role of throw-ins in soccer performance, suggesting that teams with effective throw-in strategies, particularly using backward or lateral directions, tend to achieve higher league standings. These findings challenge traditional coaching practices that favor forward throw-ins, offering new insights into how strategic throw-ins can impact game dynamics. The study encourages coaches to rethink their approach to throw-ins, potentially leading to improved team performance. Future research should explore these findings in different leagues and contexts to validate the generalizability of these results and further understand the tactical nuances of throw-in strategies.

Section Analysis

Abstract

Overview

This research investigates the impact of throw-ins on soccer performance, a relatively understudied set piece compared to free kicks and corners. The study analyzed over 16,000 throw-ins from the 2018-2019 English Premier League season to determine the relationship between throw-in performance (first contact success, possession retention, time in possession, and shot creation) and a team's final league position. Additionally, it examined how throw-in direction (backward, lateral, forward) and length affect these performance metrics. The study found a correlation between higher league positions and better throw-in performance, particularly for backward and lateral throw-ins, which were associated with higher first contact success, possession retention, and shot creation.

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Overview

This introduction sets the stage for a study on the impact of throw-ins in professional soccer. It emphasizes that while other set pieces like free kicks and corners have been extensively studied, throw-ins remain largely unexamined. The introduction highlights the frequency of throw-ins in a match, suggesting their potential importance in influencing possession and match outcomes. It also points out the existing gap in research regarding the strategic use of throw-ins and how different approaches (e.g., direction, length) might affect a team's success. The study aims to address this gap by investigating the relationship between throw-in performance and overall team success, as well as the influence of throw-in direction and length on key performance indicators like first contact success, possession retention, and shot creation.

Key Aspects

Strengths

Suggestions for Improvement

Method

Overview

This section details how the researchers investigated the relationship between throw-in strategies and soccer performance. They analyzed data from all 380 English Premier League matches during the 2018-2019 season, focusing on 16,154 throw-ins. The data, sourced from the StatsBomb database, included information about the throw-in location, direction, length, outcome, and subsequent possession. The researchers categorized throw-ins based on length (short, medium, long), direction (backwards, lateral, forwards), and pitch location. They then analyzed how these factors related to first contact success, possession retention, time in possession, and shot creation. To ensure data reliability, a subset of matches was independently coded and compared to the StatsBomb data, showing excellent agreement. Statistical analysis included descriptive statistics, correlations, and three-way ANOVAs to examine the relationships between the variables.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

table 1

Table 1 presents the total number of throw-ins and the average number of throw-ins per match for each of the 20 teams in the 2018-2019 English Premier League season. This table helps understand the distribution of throw-ins across teams and provides context for the overall sample size used in the study.

First Mention

Text: "see Table 1"

Context: This resulted in a sample of 16,380 phases of play starting from a throw-in. After excluding throws-ins from injury clearances (i.e. possession freely given back to the opposition following the ball being kicked out of play due to an injury) a total of 16,154 throw-in’s were included in the sample (see Table 1) and resulted on average of 808 throw-in’s per team (range 716–912 throw-ins).

Relevance: This table is relevant because it provides descriptive statistics about the throw-ins included in the study. It shows the distribution of throw-ins across teams and gives an idea of how many throw-ins occur in a typical match. This information is crucial for understanding the scope and context of the analysis.

Critique
Visual Aspects
  • The table is clear and easy to read.
  • The column headers are descriptive.
  • The data is presented in a logical order.
Analytical Aspects
  • Including the range of throw-ins per team (716-912) adds valuable context.
  • Presenting the average number of throw-ins per match helps understand the frequency of this event in a typical game.
  • The table effectively sets the stage for further analysis by providing basic descriptive statistics about the sample.
Numeric Data
  • Total Throw-ins (AFC Bournemouth): 804
  • Mean Throw-ins per Match (AFC Bournemouth): 21
  • Total Throw-ins (Arsenal): 804
  • Mean Throw-ins per Match (Arsenal): 21
  • Total Throw-ins (Brighton & Hove Albion): 831
  • Mean Throw-ins per Match (Brighton & Hove Albion): 22
  • Total Throw-ins (Burnley): 867
  • Mean Throw-ins per Match (Burnley): 23
  • Total Throw-ins (Cardiff City): 768
  • Mean Throw-ins per Match (Cardiff City): 20
  • Total Throw-ins (Chelsea): 734
  • Mean Throw-ins per Match (Chelsea): 19
  • Total Throw-ins (Crystal Palace): 800
  • Mean Throw-ins per Match (Crystal Palace): 21
  • Total Throw-ins (Everton): 902
  • Mean Throw-ins per Match (Everton): 24
  • Total Throw-ins (Fulham): 741
  • Mean Throw-ins per Match (Fulham): 20
  • Total Throw-ins (Huddersfield Town): 912
  • Mean Throw-ins per Match (Huddersfield Town): 24
  • Total Throw-ins (Leicester City): 841
  • Mean Throw-ins per Match (Leicester City): 22
  • Total Throw-ins (Liverpool): 884
  • Mean Throw-ins per Match (Liverpool): 23
  • Total Throw-ins (Manchester City): 716
  • Mean Throw-ins per Match (Manchester City): 19
  • Total Throw-ins (Manchester United): 825
  • Mean Throw-ins per Match (Manchester United): 22
  • Total Throw-ins (Newcastle United): 805
  • Mean Throw-ins per Match (Newcastle United): 21
  • Total Throw-ins (Southampton): 764
  • Mean Throw-ins per Match (Southampton): 20
  • Total Throw-ins (Tottenham Hotspur): 810
  • Mean Throw-ins per Match (Tottenham Hotspur): 21
  • Total Throw-ins (Watford): 737
  • Mean Throw-ins per Match (Watford): 19
  • Total Throw-ins (West Ham United): 792
  • Mean Throw-ins per Match (West Ham United): 21
  • Total Throw-ins (Wolverhampton Wanderers): 817
  • Mean Throw-ins per Match (Wolverhampton Wanderers): 22
figure 1

Figure 1 illustrates the definitions of key variables used in the study, including pitch location (divided into four areas), direction of throw-in (backward, lateral, forward), and length of throw-in (short, medium, long). The figure uses a simplified representation of half a soccer field to visually define these categories. This visual aid is essential for understanding how the researchers categorized and analyzed the throw-in data.

First Mention

Text: "see Figure 1"

Context: Based on the performance indicators, three independent variables were examined, length (short, medium, long), direction (backwards, lateral, forwards) and pitch location (4 areas, see Figure 1).

Relevance: Figure 1 is crucial for understanding the methodology and analysis. It clearly defines the categories used for the independent variables (pitch location, direction, and length of throw-in), which are central to the research questions. The visual representation makes it easy to grasp the different throw-in scenarios being analyzed.

Critique
Visual Aspects
  • The figure is clear and well-labeled, making it easy to understand the different categories.
  • The use of arrows and lines effectively illustrates the directions and lengths of throw-ins.
  • The division of the pitch into zones helps visualize the different pitch locations.
Analytical Aspects
  • Clearly defining the categories enhances the reproducibility of the study.
  • The visual representation aids in understanding the operational definitions of the variables.
  • The figure effectively communicates the scope of the analysis by showing all possible combinations of direction, length, and location.
Numeric Data

Results

Overview

This section presents the results of the study, starting with descriptive statistics of the throw-ins. It then details the correlations found between a team's final league position and their throw-in performance metrics (first contact success, possession retention, time in possession, and shot creation). The results also show how throw-in direction and length influence these performance outcomes. Key findings include a positive correlation between higher league position and better throw-in performance, and the superior effectiveness of backward and lateral throw-ins compared to forward throws, especially in terms of first contact success and possession retention. The results also highlight how different throw-in strategies affect the likelihood of creating a shot at goal.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

table 3

Table 3 presents a descriptive analysis of throw-in strategies in the 2018-2019 English Premier League season, categorized by length, direction, and pitch location. It shows the number and percentage of throws for each category, as well as the success rates for first contact and possession retention (for at least 7 seconds). This table allows for comparison of different throw-in strategies and their effectiveness.

First Mention

Text: "Table 3. Descriptive analysis of throw-in strategy (n = 16,154), first contact success (n = 13,376) and possession retained (n = 8847) in relation to throw-in length, direction, and pitch location."

Context: Results section

Relevance: This table is highly relevant as it provides a detailed breakdown of the core data analyzed in the study. It directly addresses the research questions about the effectiveness of different throw-in strategies based on length, direction, and pitch location. By presenting both raw numbers and percentages, it offers a comprehensive overview of the observed patterns.

Critique
Visual Aspects
  • The table is well-organized and easy to read, with clear headings and labels.
Analytical Aspects
  • Presenting both raw numbers and percentages is helpful for understanding the data distribution.
  • The categorization by length, direction, and pitch location allows for direct comparison of different throw-in strategies.
  • The table effectively summarizes the descriptive statistics, providing a foundation for further statistical analysis.
Numeric Data
  • Short Throw-ins: 3134
  • Medium Throw-ins: 6736
  • Long Throw-ins: 6284
  • Backward Throw-ins: 4805
  • Lateral Throw-ins: 4677
  • Forward Throw-ins: 6672
figure 2

Figure 2 displays the correlations between a team's final league position and four throw-in performance metrics: first contact success, 7-second possession retention, mean time in possession, and shots resulting from throw-ins. Each metric is presented as a separate scatter plot (a-d), with league position (rank) on the x-axis and the performance metric (rank) on the y-axis. The figure aims to visually represent the relationships between league standing and throw-in effectiveness.

First Mention

Text: "Figure 2. Correlations between final league position and first contact success (a), possession retention for 7s (b), mean time in possession from the throw-in (c), and (d) throw-ins resulting in a shot from the possession achieved after a successful first contact."

Context: Results section

Relevance: This figure is crucial for understanding the relationship between throw-in performance and overall team success. It visually represents the correlations between league position and key throw-in metrics, providing a direct answer to one of the study's primary research questions. The scatter plots allow for a quick assessment of the strength and direction of these relationships.

Critique
Visual Aspects
  • The scatter plots are clear and easy to interpret, with distinct data points and labeled axes.
  • The use of separate plots for each metric avoids clutter and facilitates individual analysis.
Analytical Aspects
  • While the figure visually represents correlations, it would be beneficial to include the correlation coefficients (e.g., Spearman's rho) on each plot or in the caption to quantify the strength of the relationships.
  • Clarifying what 'rank' represents for each performance metric would improve understanding. Does it represent the team's rank based on that specific metric, or is it another type of ranking?
Numeric Data
figure 3

Figure 3 illustrates the correlations between a team's final league position and the percentage of throw-ins they perform in each direction (backwards, forwards, and lateral). Each subplot shows a scatter plot with the team's final league position (rank) on the x-axis and the percentage of throw-ins in a specific direction on the y-axis. A negative correlation suggests that higher-ranked teams tend to perform more backward throws, while a positive correlation suggests lower-ranked teams favor forward throws.

First Mention

Text: "Figure 3. Correlations between final league position and percentage of throw-ins performed in the backwards (a), forwards (b) and lateral (c) direction."

Context:

Relevance: This figure is relevant because it visually represents the relationship between throw-in direction strategy and league standing. It helps to understand whether certain throw-in directional preferences are associated with better or worse overall team performance.

Critique
Visual Aspects
  • The figure is clear and easy to understand. The scatter plots effectively show the relationships between variables.
Analytical Aspects
  • The figure supports the study's findings regarding the correlation between throw-in direction and league position. It would be beneficial to include the correlation coefficients (rs values) directly on the plots for better interpretation. Additionally, clarifying what 'rank' represents on the y-axis (e.g., is it percentage rank or simply the raw percentage?) would improve clarity.
Numeric Data
figure 4

Figure 4 presents the first contact success rate for throw-ins, broken down by pitch location, throw-in direction, and throw-in length. It uses a diagram of half a soccer pitch, divided into attacking and defensive halves. Different colored arrows represent the direction and length of the throw-in, and the percentages next to the arrows show the success rate of the first contact. The numbers in parentheses represent the absolute number of successful first contacts. This figure helps visualize how the combination of location, direction, and length influences the success of the first contact.

First Mention

Text: "Figure 4. First contact success rate (percentage and absolute values) based on pitch location, throw-in direction and throw-in length."

Context:

Relevance: This figure is highly relevant as it visually demonstrates the effectiveness of different throw-in strategies in terms of first contact success. It allows for a direct comparison of success rates across different combinations of location, direction, and length, providing key insights into optimal throw-in strategies.

Critique
Visual Aspects
  • The figure is generally clear, but the arrows could be made more visually distinct (e.g., by using different line weights or arrowhead styles) to improve readability. Labeling the pitch areas directly on the diagram would also enhance clarity.
Analytical Aspects
  • Presenting both percentages and absolute numbers is helpful. However, adding a brief explanation of how these success rates were calculated would further enhance transparency. Consider adding a key to explain the different arrow types (e.g., solid vs. dashed) if any visual distinctions are used.
Numeric Data
figure 5

Figure 5 illustrates the success rate of retaining possession after a throw-in, categorized by pitch location (rest of attacking half, rest of defensive half), throw-in direction (backwards, lateral, forwards), and throw-in length (short, medium, long). The figure uses a simplified representation of half a soccer field, with percentages indicating the possession retention success rate for each combination of factors. Absolute values (number of successful possessions retained) are shown in parentheses, and mean time in possession (for possessions lasting at least 7 seconds) is provided at the bottom of some columns. This figure helps visualize the effectiveness of different throw-in strategies in maintaining possession.

First Mention

Text: "see Figure 5"

Context: The three-way repeated measure ANOVA showed an interaction for direction * length * location for possession retention F(2.647, 50.292) = 4.02, p < 0.05, gp2 = .175 (see Figure 5).

Relevance: Figure 5 is highly relevant as it directly addresses the research question about the impact of throw-in strategy on possession retention. It visually presents the complex interplay between throw-in location, direction, and length, allowing for a clear comparison of their effectiveness in maintaining possession. This information is crucial for understanding how different throw-in strategies contribute to team performance.

Critique
Visual Aspects
  • The figure is generally clear, but the arrangement of percentages and absolute values within the field diagram could be improved for better readability. Consider using a separate table or key for the absolute values and mean times to declutter the diagram.
  • The color coding or shading of different zones could enhance the visual distinction between pitch locations and throw-in directions.
  • Adding a clear title directly on the figure would improve its standalone understandability.
Analytical Aspects
  • The figure effectively presents the three-way interaction between direction, length, and location, but it could benefit from a more concise summary of the key takeaways within the caption. For example, explicitly state which throw-in strategies were most effective for possession retention in different pitch locations.
  • While the figure shows mean time in possession, it doesn't clearly indicate whether these times are statistically different. Consider adding significance markers or providing this information in the caption.
  • The figure focuses on successful possession retention, but it could also be informative to show the failure rates or turnover rates for different throw-in strategies to provide a more complete picture.
Numeric Data

Discussion

Overview

This discussion section interprets the study's findings on how throw-in strategies relate to soccer performance in the 2018-2019 English Premier League. The study found that higher-ranked teams had better throw-in performance (first contact success, possession retention, and shot creation). Backward and lateral throw-ins proved more effective than forward throws, challenging traditional coaching practices. The discussion explores why backward and lateral throw-ins might be advantageous, suggesting they allow more time and space for building attacks, while forward throws often lead to contested situations and turnovers. The findings highlight the importance of strategic throw-ins for gaining and maintaining possession, creating scoring opportunities, and potentially influencing match outcomes. The discussion also acknowledges the need for further research to confirm these findings and explore the tactical implications in more detail.

Key Aspects

Strengths

Suggestions for Improvement

Conclusion

Overview

This conclusion summarizes the study's findings on the relationship between throw-in success and soccer team performance. The research suggests a link between how teams perform on throw-ins and their final league standing. Specifically, throwing the ball laterally or backward, rather than forward, is associated with greater success in various aspects of throw-in plays, such as first contact success, possession retention, and shot creation. Higher-ranked teams tend to utilize this backward/lateral strategy more often. The authors suggest that coaches should re-evaluate their throw-in strategies to potentially improve team performance.

Key Aspects

Strengths

Suggestions for Improvement

↑ Back to Top