Analysis of Shots Trajectory and Effectiveness in Women’s and Men’s Football European Championship Matches

Abstract

Shots on target are a crucial factor in football performance, yet the impact of categorizing shots as low or ground-level and high or parabolic has not been fully explored. The objective of this study was to analyze whether there are differences in the frequency and effectiveness (as measured by xGOT) between parabolic and low shots on target in international men’s and women’s football competitions. The results revealed that the most common shot type was the parabolic shot, occurring in 59.86% of shots on goal in the men’s competition (270 shots) and 67.12% in the women’s competition (196 shots). In the overall set of shots, 62.77% were parabolic (466 shots). No significant differences were observed between the competitions (p > 0.05). Regarding the xGOT values, no significant differences were observed for any of the interaction effects analyzed (gender, shot type and shot outcome). The conclusion was that the parabolic shot was the most frequent type of shot on target in both men’s and women’s football.

1. Introduction

Shots on target are a key performance indicator in both men’s and women’s football, though their effectiveness is shaped by contextual and technical variables [1,2]. Modern tactical analysis has shifted focus from shot volume to shot quality, with studies showing that goal efficiency—rather than the number of attempts—is more predictive of success [3,4]. For instance, 89% of goals in the 2012 UEFA European Championship were scored within the penalty area, while long-range shots were mostly saved [4]. Similarly, distance and angle have proven significant in expected goals (xG) models [5], and other factors such as passing accuracy, offensive structure, and set plays also influence scoring outcomes [6,7,8,9,10,11,12,13,14,15,16].

Analyzing the generation of goal-scoring opportunities and identifying key influencing variables is highly valuable. The integration of advanced performance metrics, such as expected goals (xG) and expected goals on target (xGOT), has significantly enhanced football research by providing deeper insights into shot quality, its effectiveness, and the key factors influencing match outcomes. The xG metric [17,18,19] is a statistical tool used in football to quantify the quality of goal-scoring opportunities and estimate the probability of a given shot resulting in a goal. The xG value assigned to a shot represents its “goal probability” and is influenced by multiple variables, including shot distance, shooting angle, shot type (e.g., header or strike), defensive positioning, and the sequence of play preceding the attempt [20]. While, the xGOT metric [21,22] provides a more refined analysis by accounting not only for the quality of the scoring opportunity but also for the execution of the shot itself. The xGOT evaluates the likelihood of a goal once a shot has been taken and is on target, incorporating additional factors such as shot placement, shot power, and the GK’s positioning and reaction time. This enhancement allows xGOT to offer a more nuanced understanding of shooting performance by capturing the interaction between shot quality and GK’s effectiveness [23,24,25].

Despite advances in shot analysis [26,27,28,29], little attention has been given to shot classification. While commercial data platforms such as StatsBomb and Opta [30] provide valuable insights into shot analysis, it is essential to highlight the limitations inherent in their methodologies. Both platforms utilize proprietary algorithms to classify shots based on a range of variables, including shot location, trajectory, and contextual factors (e.g., pressure from defenders, GK position). However, these platforms do not always provide full transparency regarding the specific variables or the modeling techniques employed, which limits the ability to critically evaluate the accuracy and generalizability of their classifications. For instance, Opta’s shot classification relies heavily on manually labeled datasets, which can introduce subjective interpretation errors and may not always account for the nuanced variations in ball trajectory that are central to our study. The lack of standardized methods for classifying shot types based on precise ball trajectory data is a significant gap in the existing literature. For the most frequent shots, namely those taken from the ground, these classifications do not distinguish between low or ground-level shots and high or parabolic shots. This gap in research is notable, as shot trajectory directly influences GK’s response times, shooting accuracy, and defensive strategies. Authors hypothesized that a parabolic shot, characterized by greater vertical displacement [31], may require different technical execution and tactical decision-making compared to a straight-line shot, which follows a more direct path. Understanding this differentiation could provide valuable insights for both players and coaches, particularly in designing specialized training drills and optimizing offensive strategies.

Therefore, the objective of this study was to analyze whether there are differences in the frequency and effectiveness (as measured by xGOT) between parabolic and low shots on target in international men’s and women’s football competitions. By incorporating this focused perspective on shots on target, the research aimed to contribute to the refinement of performance analysis methodologies and to provide practical applications for both tactical planning and technical training in football

2. Materials and Methods

2.1. Sample

The sample comprised all national teams that participated in the final stages of the 2022 UEFA Women’s Euro and the 2024 UEFA Men’s Euro tournaments. Specifically, the dataset included 16 teams from the women’s competition and 32 teams from the men’s competition. A total of 61 women’s matches and 125 men’s matches were analyzed, covering the entire final tournament phases—from the group stage to the final. Qualifying matches were intentionally excluded to ensure uniformity in competitive context and data collection standards.

Match event data were retrieved from the StatsBomb Open Data repository (https://github.com/statsbomb/open-data/blob/master/data/competitions.json, accessed on 3 February 2025) [30], a freely available and peer-reviewed source widely used in football analytics. StatsBomb data are considered highly reliable due to their manual annotation process and the validation of event coding in prior research [32,33]. Nonetheless, as a limitation, StatsBomb does not disclose the exact algorithms or models used to derive some advanced metrics (such as xG or xGOT), which constitutes a methodological constraint when interpreting or replicating certain findings.

The dataset was filtered to include only shots classified as “on target”, as defined by StatsBomb (i.e., shots resulting in goals or requiring goalkeeper intervention). Actions such as blocked or off-target shots were excluded to focus specifically on shot effectiveness. Furthermore, all duplicate events, penalties, own goals, and shootouts were removed to standardize the analysis.

Importantly, no personal or biometric information was collected or analyzed in this study, ensuring compliance with ethical standards. The research protocol received approval from the local institutional ethics committee (2024-1326).

2.2. Variables

The final dataset included 2221 total shots, of which 743 were shots on target. These shots were drawn from official tournament matches across both competitions, with 451 on-target shots from the men’s tournament and 292 from the women’s tournament.

Table 1. Variables of the study and parameters used to classify the type of shot on target, based on the STATSBOMB dataset.

Variable of the Study
Variable	Definition
Total shots on target	Total number of shots on goal, excluding penalty kicks and own goals
Goal shots	Total number of goals scored
Non-goal Shots	Total number of shots on goal that did not result in goals
Shot on target (xGOT)	Probability of a goal after considering shot placement and GK positioning
Classification of shot on target type
Parameters	Definition
Ball start location	$\mathrm{A} \mathrm{tuple} \left(\mathrm{x},\mathrm{y},\mathrm{z}\right)$$\mathrm{where} x\in \left[\mathrm{0,75}\right], y\in \left[\mathrm{0,120}\right] z\in \left[0,\mathrm{\infty }\right)$: the location of the ball before the shot (unit: meters).
Ball end location	$\mathrm{A} \mathrm{tuple} \left(\mathrm{x},\mathrm{y},\mathrm{z}\right)$$\mathrm{where} x\in \left[\mathrm{0,75}\right], y\in \left[\mathrm{0,120}\right] z\in \left[0,\mathrm{\infty }\right)$: the location of the ball at the final part of the shot (unit: meters).
Shooting technique,	Name of the technique used for the shot, differentiating between: - Backheel, a shot executed using the heel of the foot. - Diving header, a shot attempted with the head while the player dives forward to reach the ball. - Volley, a shot taken without the ball touching the ground prior to impact. - Half volley, a shot made by striking the ball immediately after it has bounced off the ground. - Lob: a shot characterized by a high-arching trajectory, intended to pass over an opposing player. - Overhead kick, a shot taken with the player’s back facing the goal. - Standard: a shot that does not conform to any of the aforementioned techniques.

summarizes the variables included in the analysis, as well as the parameters used to classify the type of shot on target.

To align with the objectives of this study, a subset of 500 standard shots on target was selected for detailed analysis from the total number of shots on target. We selected ’standard’ shots, as these are the most frequent shot types (67%) from the ground during matches (

Table 2. Types of shots on target by shooting technique, according to the STATSBOMB dataset.

Shot Types	Head	Standard	Lob	Volley	High-Volley	Backheel	Dividing Header	Overhead Kick	Total
Women competition	61	179	3	14	31	3	1	0	292
Men competition	61	321	2	19	44	2	1	1	451
Total	122	500	5	33	75	5	2	1	743

). The classification of a shot as either ground-level and parabolic was conducted by trained analysts and was determined based on the z-coordinate of the ball’s trajectory (Figure 1 and Figure 2). This value represents the height of the ball relative to the ground: if the z-value fell within the range from 0 to 0.4 m, the shot was classified as a low or ground-level shot; but if the value met or exceeded this threshold, the shot was classified as a parabolic shot [34,35]. The threshold of 0.4 m was established based on the previously studies [35,36,37,38], which discuss the accuracy of tracking data. The tracking error in the z-coordinate ranges from 0.25 to 0.5 m, depending on the speed of the tracked object. Consequently, we interpreted all values between 0 and 0.4 m as ground-level shots.

All data were obtained from the aforementioned sources. To date, both traditional and advanced football metrics are rarely available to the public, as companies such as Opta and StatsBomb collect and publish these datasets exclusively on their platforms. Regarding the xGOT metric, their values were taken directly from the StatsBomb dataset and were not recalculated, due to the proprietary nature of the metric. Although xGOT is a well-established measure of shot execution quality, its internal calculation model remains undisclosed, limiting full transparency regarding the variables and weighting used. This opacity is commonly referred to in scientific literature as a “black-box” approach. In response to this limitation, several researchers have begun developing their own xGOT models, with an emphasis on transparency and reproducibility, making both the methodology and calculations publicly accessible [35].

2.3. Statistical Analysis

Statistical analyses were conducted using SPSS (version 18; SPSS Inc., Chicago, IL, USA).

Normality of the data was assessed using the Kolmogorov–Smirnov test, which indicated that each variable followed a normal distribution and the equality of variances was assumed. Descriptive statistics were calculated for all variables, including means and standard deviations, as well as frequencies and percentages for specific variables (e.g., shots on target, goals shots, and non-goal shots, categorized by shot types). The percentage of absolute difference was used to identify the variation in percentages between men and women.

A three-way ANOVA model with interaction effects was applied to examine the influence of three factors on the dependent variable xGOT. The factors were: gender (women/men), shot type (ground-level/parabolic) and shot outcome (goal/non-goal). The Bonferroni post hoc test was used for multiple comparisons. Statistical significance was set at p < 0.05.

3. Results

Table 3. Frequency distribution of all shots on target, goal shots, and non-goal shots, categorized by shot type, in both women’s and men’s competitions.

Type of Shot on Target	All Shots on Target (n = 743)		Men Competition (n = 451)		Women Competition (n = 292)		Absolute Difference
	Goal Shots	Non-Goal Shots	Goal Shots	Non-Goal Shots	Goal Shots	Non-Goal Shots	Goal Shots	Non-Goal Shots
Ground-level shot	88 (11.84%)	189 (25.44%)	54 (11.97%)	127 (28.17%)	34 (11.64%)	62 (21.23%)	0.33%	7.28%
Parabolic shot	129 (17.36%)	337 (45.36)	72 (15.96%)	198 (43.90%)	57 (19.52%)	139 (47.60%)	3.56%	3.70%

presents the frequency distribution of all shots on target, goal shots, and non-goal shots, categorized by shot type, in both women’s and men’s competitions. The data revealed that the proportion of each shot type was relatively similar across sexes, with minimal absolute differences of less than 8%. This suggested that the distribution of shot types did not markedly differ between male and female players, indicating comparable shooting patterns in terms of shot trajectory.

Table 4. The xGOT of all shots on target, goal shots, and non-goal shots, categorized by shot type, in both women’s and men’s competitions. Additionally, the mean differences and confidence intervals were reported.

Type of Shot on Target	All Shots on Target (n = 743)		Men Competition (n = 451)		Women Competition (n = 292)		Difference of Mean		Confidence Intervals
	Goal Shots	Non-Goal Shots	Goal Shots	Non-Goal Shots	Goal Shots	Non-Goal Shots	Goal Shots	Non-Goal Shots	Goal Shots	Non-Goal Shots
Ground-level shot	0.36 ± 0.08	0.12 ± 0.02	0.36 ± 0.08	0.11 ± 0.01	0.35 ± 0.07	0.13 ± 0.02	−0.004	0.017	(−0.1250, 0.1174)	(−0.0282, 0.0627)
Parabolic shot	0.30 ± 0.07	0.09 ± 0.02	0.32 ± 0.08	0.10 ± 0.02	0.28 ± 0.06	0.08 ± 0.01	−0.047	−0.019	(−0.1406, 0.0472)	(−0.0464, 0.0075)

reports the xGOT values for all shots on target, goal shots, and non-goal shots, again categorized by shot type and competition. The absence of significant differences (p > 0.05) between groups indicated that, regardless of the shot type or sex of the player, the expected goal on target (xGOT) metric remained consistent. Additionally, the mean differences and confidence intervals were reported in detail. No significant interaction effects were detected between the analyzed factors, indicating that the combined influence of shot type, sex, and shot outcome did not significantly alter the xGOT values. This further reinforced the stability of xGOT as a metric independent of these variables.

Figure 3 illustrates the percentage distribution of all shots on target, goal shots, and non-goal shots by shot type and competition. The figure corroborated the tabular data, illustrating similar proportions between men’s and women’s competitions. The graphical representation aided in the intuitive understanding of the data distribution, highlighting the consistent shooting behavior and shot effectiveness across sexes and shot types.

4. Discussion

The key finding of this study revealed that the parabolic shots were the most frequent type of shots on target, in both the men’s competition (270 shots on target; 59.86%) and women’s (196 shots on target; 67.12%) competitions, as well as in the overall set of shots on target (466 shots on target; 62.77%) (Table 3, Figure 3). No significant differences were observed between the two competitions (p > 0.05). It is worth mentioning that a shot a target should be considered as a parabolic shot when its z-coordinate reached a value equal to or greater than 0.4 m [36,37,38]. These data reinforce the importance of considering the trajectory of shots in football analysis. The consistent prevalence of parabolic shots on target across both men and women competitions suggested that these may present greater technical challenges for shooters and GKs due to their trajectory and interaction with the goal frame. Unlike ground-level shots, which follow a direct path, parabolic shots require additional anticipation and positional adjustments, potentially affecting a GK’s ability to make successful saves. These findings align with previous research highlighting the complexity of shots with greater vertical motion and their impact on match outcomes [29].

Similarly, no significant differences were found when evaluating the shot performance, measured through the xGOT, when comparing the men’s and women’s competitions (Table 4, p > 0.05). The lack of significant differences in xGOT values between the two competitions suggests that the performance of shots on goal may be similar, regardless of gender. This result was consistent with previous research [26] and may imply that factors other than shot type, such as player skill or defensive pressure, could be influencing goal-scoring outcomes.

To date, no study has systematically differentiated between ground-level and parabolic shots on target in football, making this research a novel contribution to the field. The authors performed a simplified analysis of this type of shots, which could serve as a basis for future research. While previous studies have examined pre-shot [19,20,27], post-shot actions [4,22,26], and even GK performance, the introduction of a trajectory-based shots on target classification provides a more detailed framework for understanding shooting performance. This distinction has direct implications for player training, particularly in developing targeted drills for both shooters and GKs. Our results showed no statistically significant differences in xGOT values between the two shot types (p > 0.05), which may reflect a genuine similarity in effectiveness or be a consequence of limited sensitivity due to the opaque nature of the model. Nevertheless, the authors consider that this training-oriented perspective remains relevant and should be integrated into applied practice. Based on our data, the fact that both types of shots exhibited similar xGOT values may suggest that, once the ball is directed on target, the likelihood of scoring is comparable, regardless of whether the shot is ground-level or parabolic. This finding implies that shot quality, especially in terms of placement and power, may compensate for the influence of shot trajectory. Moreover, if parabolic shots were not less effective than ground-level ones when on target, shooters may base their shot selection on contextual factors such as GK positioning, available space, and defensive pressure, without an expected decrease in effectiveness. Conversely, if below-average xGOT values were identified for either shot type (Table 4), tailored drills could be introduced to enhance performance in those specific scenarios.

Coaching and training football players (including GKs) require various sources of information derived directly from the game itself. The identification of shot type as a key factor in shot success may offer an additional layer of insight for tactical analysis and training development. The authors propose that, should the majority of shots on target follow a parabolic trajectory (Table 3), this would require adjustments to training protocols aimed at enhancing the performance of both shooters and GKs. These data highlighted the necessity for customized training programs that address the distinct characteristics of shots on target. For instance, training exercises designed to improve GK responses to parabolic shots could enhance reaction time and positioning, while shooters could refine shot execution (biomechanical technique) to maximize goal-scoring efficiency.

4.1. Practical Applications

These findings provide valuable insights for coaches, players, and performance analysts, helping to design more effective training sessions. Specifically, they highlight the importance of improving the technical execution of parabolic shots for shooters and enhancing GKs’ ability to save these attempts (both of which can significantly impact match outcomes). To develop these skills, a recommended training exercise focuses on practicing parabolic shots aimed at the lower corners and sides of the goal, as these areas have the highest scoring probability [30]. Structure of the exercise:

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For shooters: the objective should be to place shots accurately in the designated target zones.

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For GKs: The goal should be to anticipate and block these shots effectively.

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Scoring system: shooters earn points for successfully placing shots in the target areas; GKs earn points for each successful save; the player with the highest score at the end of the session would be the winner.

By incorporating this drill into regular training, players can refine their finishing accuracy, while GKs improve their reflexes and positioning, ultimately enhancing overall team performance.

4.2. Limitations of the Study

The authors recognize the following limitations in the study: 1. Given the novelty of the topic, the authors performed a simplified analysis of ground-level and parabolic shots within a highly applicable framework. This preliminary analysis could serve as a foundation for future research exploring potential variables influencing xGOT, particularly through a biomechanical analysis of shot execution. Specifically, examining factors such as foot placement, body posture, and shooting technique could provide valuable insights into their effects on shot trajectory. In addition, considering contextual control variables, such as shot zone, defensive pressure, dominant foot, specific in-game actions (e.g., set pieces), or game situations (e.g., counterattacks), would be of considerable interest; 2. The data provider for this study, StatsBomb, offers the advantage of supplying a comprehensive range of data from both national and international competitions, including both men’s and women’s football. However, a limitation is that the methodology behind the calculation of this data remains opaque (black-box). Despite this, StatsBomb data is widely regarded as the standard reference in the field of football, used daily by coaches and analysts, as well as in academic research; 3. The existing literature offers limited differentiation between distinct shot types on goal. A comprehensive understanding of these shot types and their respective xGOT values is crucial for identifying a team’s strengths and weaknesses, thereby contributing to performance optimization; 4. Research on parabolic shots on goal remains limited, despite their prevalence in football matches. One contributing factor is the ambiguity in defining a “parabolic shot.” Currently, it is characterized by the ball’s trajectory, specifically its variation in the z coordinate, ranging from 0 to 0.4 m [35,36,37]. This lack of clarity in the literature complicates the accurate interpretation of results. Nonetheless, a detailed analysis of their execution is essential to support the development of technical and tactical training interventions aimed at enhancing both shooting precision and goalkeeper performance, ultimately leading to greater team effectiveness; and 5. The authors wish to clarify that this study was conducted using data from official international elite competitions, including both men and women categories. Consequently, extrapolating these results to other contexts may be problematic. Future research that incorporates additional contextual factors could provide valuable insights and further enhance the contribution of this study.

5. Conclusions

In conclusion, the parabolic shot was the most frequent type of shot on target in both men’s and women’s football competitions. Furthermore, no significant differences in shot performance, measured by xGOT, were observed across goal outcomes, shot types, or gender. These results indicated that, once the ball was directed on target, the likelihood of scoring is comparable regardless of shot trajectory or player sex. From an applied perspective, these findings suggest that coaching and analytical efforts should prioritize the enhancing of shot accuracy, placement, and power. Training interventions should focus on developing players’ technical proficiency to optimize shot execution under diverse match conditions. Additionally, GK’ training protocols could be refined to improve their ability to anticipate and react effectively to varying shot trajectories, particularly parabolic shots, which were predominant. Incorporating these insights into tactical planning and individualized training programs may contribute to improving both offensive efficiency and defensive performance in football across genders.