Success from the Spot: Insights into Penalty Performance in Elite Women’s Football

Abstract

This study analyzed penalty kick performance in elite women’s football, focusing on contextual, situational, and technical factors across the Spanish Liga F and the English Women’s Super League during the 2022/23 to 2024/25 seasons. Using an observational methodology, 240 penalties were systematically coded according to twelve criteria and fifty-five categories. Chi-square goodness-of-fit and independence tests were applied to examine distributions and associations. The overall conversion rate was 80.4%, with 15.4% of attempts saved and 4.2% missed. Home teams achieved significantly higher success than away teams (85.6% vs. 72.7%), while penalties taken when leading were less effective (69.9%) compared with those executed while drawing or losing (>84%). Temporal effects were also observed, with lower effectiveness around halftime (71.8%). Laterality and goalkeeper actions showed no significant influence, although some league-specific tendencies were noted. Shot placement emerged as the strongest determinant of success, with upper and central zones achieving near-perfect results, whereas medium-height shots were least effective. These findings extend existing knowledge by providing the first longitudinal evidence from elite women’s domestic leagues in Spain and England. Practical implications include emphasizing accuracy toward optimal zones, reinforcing psychological preparation when leading, and addressing performance drops during specific match periods.

1. Introduction

Penalty kicks represent one of the most decisive situations in football, often determining match outcomes and even the destiny of entire tournaments. Given the low-scoring nature of the sport, the conversion or failure of a single penalty can shift league standings, influence promotion or relegation, and decide progression in knockout competitions [1,2]. Unsurprisingly, penalty kicks have become a central topic of performance analysis, with research addressing their technical, tactical, psychological, and contextual determinants [3,4,5].

Extensive research in men’s football has established reference success rates ranging between 70% and 85% [1,2,6,7,8]. Studies in top domestic leagues such as the Premier League and LaLiga have shown that vertical shot direction, goalkeeper positioning, and rule changes (e.g., Video Assistant Referee, VAR) are key determinants of penalty success [7,8,9]. These findings provide a theoretical framework for understanding technical and contextual influences on penalty performance.

Psychological research has further clarified the influence of pressure and decision-making: stress and public expectations can reduce conversion rates in international shootouts [10], attentional focus toward the goalkeeper can disrupt motor execution [11], and anxiety, self-regulation, and coping strategies modulate outcomes [12]. Large-scale evidence also highlights the combined influence of skill and pressure dimensions on penalty success [13], underscoring that performance depends on both technical execution and psychological resilience under extreme pressure.

The interaction between goalkeepers and penalty takers has also been studied extensively. Goalkeepers often display an “action bias,” diving even when statistical models suggest that staying in the center would maximize save probability [3,14]. Anticipatory cues—such as the orientation of the supporting foot or contralateral arm—have been proposed as predictors of shot direction [15,16], and subtle goalkeeper off-centering has also been shown to bias kick direction toward the side with a larger perceived area [17]. However, such strategies may lose reliability at the elite level, where kickers frequently disguise intentions through deceptive movements [18]. From a technical perspective, shot placement has consistently been identified as the strongest predictor of success. Shots aimed at the natural side of the kicker—especially when elevated—are both less likely to miss the target and harder to save, while low central shots, though safer against misses, are more frequently stopped by goalkeepers [6,19,20]. These findings illustrate the trade-off between risk and reward in shot selection.

Beyond biomechanics and psychology, penalty kicks have also been described as strategic interactions approximating a mixed-strategy Nash equilibrium, where kickers and goalkeepers adapt their behavior probabilistically to minimize predictability [21,22].

Despite this robust body of knowledge, research has overwhelmingly focused on men’s football. In contrast, the women’s game has undergone unprecedented professionalization and global growth, yet continues to receive limited scientific attention. Participation, investment, and media visibility have expanded rapidly, with tournaments such as the FIFA Women’s World Cup and UEFA Women’s Champions League drawing record audiences [23]. Within these competitions, penalties often play decisive roles, but peer-reviewed studies on penalty performance in women’s football remain scarce. Although some comparative investigations have examined gender differences in penalty situations [24,25,26], the research dedicated exclusively to women’s football is still limited. Evidence from other sports also suggests that female athletes may differ in technical execution, perceptual–motor strategies, and psychological responses to stress [6,11], indicating that direct generalizations from men’s football may overlook key gender-specific dynamics.

The English Women’s Super League and the Spanish Liga F provide ideal contexts for bridging this gap. Both competitions have established themselves among the most competitive domestic leagues globally, with professional infrastructures, high-quality video resources, and international exposure. Systematically analyzing penalties in these leagues will not only extend current knowledge but also provide applied insights for evidence-based coaching and performance optimization.

In summary, while men’s football has been widely studied in terms of penalty performance—including situational, technical, psychological, and strategic perspectives [7,8,14,22]—the women’s game remains a notable research void. Addressing this imbalance is crucial for advancing scientific understanding and enhancing practical applications. The present study therefore aims to analyze penalty kicks in the English Women’s Super League and the Spanish Liga F, focusing on technical, situational, and contextual factors associated with their success. By doing so, it provides among the first longitudinal analyses in women’s elite leagues and contributes to evidence-based strategies for improving penalty performance in women’s football.

2. Methods

2.1. Design

An observational methodology was applied to examine penalty executions in Spanish Liga F and the English Women’s Super League during the 2022/23, 2023/24, and 2024/25 seasons [27]. This methodological framework is particularly suitable for the study of naturally occurring behaviors in sport, as it allows researchers to systematically analyze actions in their authentic competitive context while maintaining ecological validity [27,28].

The approach involved systematic, non-participant observation of penalty kicks, which were recorded and subsequently coded using a predefined category system. In accordance with established observational methodology guidelines, the design was characterized as nomothetic, longitudinal, and unidimensional. The nomothetic dimension reflects the inclusion of multiple players and events; the longitudinal dimension corresponds to the three competitive seasons analyzed; and the unidimensional dimension denotes the focus on a single level of response (i.e., penalty execution). This classification ensures that the observational design meets the requirements of rigor, reproducibility, and comparability outlined for research in sport sciences [28].

2.2. Sample

The sample comprised penalty kicks taken in the domestic women’s leagues of Spain and England during the 2022/23, 2023/24, and 2024/25 seasons. In total, 243 penalties were identified, of which 240 were included in the analysis, while 3 were excluded due to insufficient video footage. The study was approved by the ethics committee of the Faculty of Education and Sport Science (University of Vigo, application 03-090425).

2.3. Instruments

For this research, an ad hoc observational tool was designed (see Table 1 and Figure 2 in Section 3). The instrument was structured as a system of mutually exclusive categories, comprising twelve criteria and fifty-five categories. Its initial framework was informed by previous studies on penalty kicks and kicker–goalkeeper interactions [7,8,9,29], and was subsequently refined through consultation with two specialists in football and observational methodology. Content validity was ensured by verifying consistency with the theoretical framework [27] and by achieving 95% consensus among experts on the accuracy and clarity of the criteria and their operational definitions.

Behavior coding was carried out using LINCE PLUS (version 2.1; Alberto Soto Fernández, INEFC, Lleida, Spain) [30], a software specifically developed for systematic observation. The platform allows researchers to configure interactive coding interfaces aligned with predefined categories, automatically generating sequential records of performance behaviors. This functionality facilitates a detailed analysis of behavioral regularities in competitive contexts. Its suitability for the present study is further supported by its widespread adoption in observational research across different sport settings [31,32,33,34].

Details of all criteria and operational definitions applied in the analysis are presented in the Section 3 (Table 1 and Figure 2).

2.4. Procedure

The shots analyzed were retrieved from publicly available and officially distributed sources, including YouTube and the official platforms of Liga F (https://www.youtube.com/@ligaf_oficial, accessed on 5 September 2025) and the Women’s Super League (https://www.youtube.com/@BarclaysWSL, accessed on 5 September 2025). All clips were compiled into a single video, chronologically ordered (Liga F followed by WSL), and edited with iMovie software (version 10.4.3; Apple Inc., Cupertino, CA, USA) to generate files optimized for observational analysis.

Observers received prior training, and reliability was assessed through intra- and inter-observer agreement testing using Cohen’s Kappa coefficient [35] in LINCE Plus. To avoid using the same material analyzed in the study, reliability testing was conducted on a parallel set of men’s penalty kicks equivalent in size to 25% of the women’s sample (i.e., 60 actions out of 240). Intra-observer reliability yielded a Kappa of 0.95 for the first observer and 0.98 for the second, while inter-observer testing on the same subset produced a Kappa of 0.96. All values indicated almost perfect agreement. After confirming reliability, the complete dataset of women’s penalties was coded by a single trained observer.

The coded records were then exported to Excel, including the sequential order of behaviors, their timing, and duration in frames, enabling subsequent transformations for both sequential and statistical analyses [8].

To enhance the transparency and reproducibility of the study, an algorithm flowchart summarizing the sequential stages of the observational process is presented in Figure 1. This diagram illustrates the methodological workflow, from video retrieval and instrument development to data analysis and interpretation.

2.5. Data Analysis

Descriptive and inferential statistical analyses were performed using IBM SPSS Statistics, version 25.0 (IBM-SPSS Inc., Chicago, IL, USA). A chi-square goodness-of-fit test was applied to examine whether the distribution of observations within each category significantly deviated from a uniform distribution, thereby revealing patterns of preference or dominance across variables. In addition, chi-square tests of independence were conducted to evaluate potential associations between penalty kick effectiveness (goal or error) and the categories of the remaining criteria, as well as to compare distributions between the two leagues (Liga F vs. WSL). For each chi-square test of independence, Cramer’s V was also calculated to estimate the effect size of the association, providing information on the strength of relationships independent of sample size. Values of <0.10, 0.10–0.29, 0.30–0.49, and ≥0.50 were interpreted as trivial, small, moderate, and large effects, respectively [33].

Furthermore, a binary logistic regression analysis was performed to examine the predictive contribution of contextual (e.g., stadium, match status, time interval, league) and technical (e.g., run-up pattern, goalkeeper action, shot placement, laterality) variables to penalty success (goal = 1; error = 0). Model fit was assessed using the omnibus chi-square test, the Hosmer–Lemeshow goodness-of-fit test, and Nagelkerke’s R², with classification accuracy reported as a measure of predictive performance. Statistical significance was set at p < 0.05.

3. Results

A total of 240 penalty kicks were analyzed across the three seasons. Preliminary comparisons across the three analyzed seasons (2022/23, 2023/24, and 2024/25) revealed no significant differences in any contextual or technical variable, either for the overall dataset or when leagues were analyzed separately. Consequently, results are presented jointly for all seasons in Table 1 (global sample) and Table 2 (by league).

Table 1 summarizes the distribution of penalties across all criteria and categories, together with success rates (SR) and chi-square results, while Figure 2 provides a visual reference for the spatial coding of shot direction. This overview establishes the baseline for subsequent interpretations of contextual, technical, and situational influences on penalty performance.

Table 1. Observational instrument, descriptive statistics, and results of chi-square goodness-of-fit (χ² GOF) and independence tests for each criterion. Independence tests were performed between binary penalty outcome (goal vs. error) and the remaining criteria.

Criteria	Description	n	%	SR	χ2 GOF	χ2 Independence
League	Liga F	156	65	80.1	χ2 =21.600 p < 0.001	χ2 = 0.024; p = 0.878; V = 0.01
	Women’s Super League	84	35	81
Stadium	The team that kicks plays as the home team	144	60	85.4	χ2 = 9.600 p = 0.002	χ2 = 5.715; p = 0.017 *; V = 0.15
	The team that kicks plays as the away team	96	40	72.9
Time	The penalty is kicked at 0–15 min	22	9.2	77.3	χ2 = 17.650 p = 0.003	χ2 = 3.797; p = 0.579; V = 0.13
	The penalty is kicked at 16–30 min	32	13.3	87.5
	The penalty is kicked at 31–45+ min	39	6.3	71.8
	The penalty is kicked at 46–60 min	52	21.7	80.8
	The penalty is kicked at 61–75 min	42	17.5	85.7
	The penalty is kicked at 76–90+ min	53	22.1	79.2
Result	The team that kicks the penalty is tying	104	43.3	84.6	χ2 = 11.425 p = 0.003	χ2 = 7.450; p = 0.024 *; V = 0.18
	The team that kicks the penalty is losing	63	26.3	85.7
	The team that kicks the penalty is winning	73	30.4	69.9
Scoreboard	The team that takes the penalty is tying the match.	104	43.3	84.6	χ2 = 201.233 p < 0.001	χ2 =12.663; p = 0.049 *; V = 0.23
	The team that kicks the penalty is losing by 1 goal.	41	17.1	85.4
	The team that kicks the penalty is losing by 2 goals.	15	6.3	86.7
	The team that kicks the penalty is losing by 3+ goals.	7	2.9	85.7
	The team that kicks the penalty is winning by 1 goal.	42	17.5	66.7
	The team that kicks the penalty is winning by 2 goals.	23	9.6	65.2
	The team that kicks the penalty is winning by 3+ goals.	8	3.3	100
Laterality	The kicking player is right—footed	188	78.3	80.9	χ2 = 77.067 p < 0.001	χ2 = 0.104; p = 0.747; V = 0.02
	The kicking player is left—footed	52	21.7	78.8
Run—up to the Kick	The kicker takes fewer than three steps before striking the ball	22	9.2	72.7	χ2 = 343.75 p < 0.001	χ2 = 5.334; p = 0.069; V = 0.15
	The kicker takes three or more steps before striking the ball	215	89.6	81.9
	The kicker clearly pauses during the run—up	3	1.3	33.3
Goalkeeper actions prior to the kick	Small sideways steps in the center of the goal without combining them with arm movements (legs only)	12	5	66.7	χ2 = 257.000 p < 0.001	χ2 = 4.879; p = 0.559; V = 0.14
	Exaggerated lateral movement to the kicker’s right side.	9	3.8	77.8
	Exaggerated lateral movement to the kicker’s left side.	9	3.8	100
	Goalkeeper remains stationary in the center of the goal without moving.	110	45.8	82.7
	Exaggerated movement of both arms and legs in the center of the goal (without displacement).	8	3.3	75
	Arm movements only in the center of the goal (without displacement).	33	13.8	81.8
	Small jumps in the center of the goal (without displacement).	59	24.6	76.3
Direction—goal (depending on the view of the kicker)	Left—Top	11	4,6	90.9	χ2 = 287.775 p < 0.001	χ2 =13.551; p = 0.094; V = 0.26
	Left—Medium height	28	11.7	57.1
	Left—Down	93	38.8	60.6
	Centre—Top	3	1.3	100
	Centre—Medium height	5	2.1	80
	Centre—Down	7	2.9	100
	Right—Top	12	5.0	83.3
	Right—Medium height	18	7.5	83.3
	Right—Down	63	26.3	84.1
Direction— laterality (depending on the view of the kicker)	Kicker—Top	15	6.3	93.3	χ2 = 284.700 p < 0.001	χ2 =11.793; p = 0.161; V = 0.25
	Kicker—Medium	29	12.6	62.1
	Kicker—Down	91	37.9	84.6
	Middle—Top	3	1.3	100
	Middle—Medium	5	2.1	80
	Middle—Down	7	2.9	100
	Far—Top	9	3.8	77.8
	Far—Medium	16	6.7	81.3
	Far—Down	65	27.1	76.9
Penalty Outcome (detailed)	The penalty ends in goal	193	80.4	-	χ2 = 243.975 p < 0.001	-
	The penalty is saved	37	15.4	-
	The penalty is missed	10	4.2	-
Penalty Outcome (binary)	Goal	193	80.4	-	χ2 = 88.817 p < 0.001	-
	Error	47	19.6	-

Note: SR = Success Rate; GOF = goodness-of-fit; χ² = value of the chi-square statistic; V = Cramér’s V (Effect Size is reported as Cramér’s V for chi-square tests; interpretation thresholds as specified in the Section 2); * Statistical significance (p < 0.05).

Table 1. Observational instrument, descriptive statistics, and results of chi-square goodness-of-fit (χ² GOF) and independence tests for each criterion. Independence tests were performed between binary penalty outcome (goal vs. error) and the remaining criteria.

Criteria	Description	n	%	SR	χ2 GOF	χ2 Independence
League	Liga F	156	65	80.1	χ2 =21.600 p < 0.001	χ2 = 0.024; p = 0.878; V = 0.01
	Women’s Super League	84	35	81
Stadium	The team that kicks plays as the home team	144	60	85.4	χ2 = 9.600 p = 0.002	χ2 = 5.715; p = 0.017 *; V = 0.15
	The team that kicks plays as the away team	96	40	72.9
Time	The penalty is kicked at 0–15 min	22	9.2	77.3	χ2 = 17.650 p = 0.003	χ2 = 3.797; p = 0.579; V = 0.13
	The penalty is kicked at 16–30 min	32	13.3	87.5
	The penalty is kicked at 31–45+ min	39	6.3	71.8
	The penalty is kicked at 46–60 min	52	21.7	80.8
	The penalty is kicked at 61–75 min	42	17.5	85.7
	The penalty is kicked at 76–90+ min	53	22.1	79.2
Result	The team that kicks the penalty is tying	104	43.3	84.6	χ2 = 11.425 p = 0.003	χ2 = 7.450; p = 0.024 *; V = 0.18
	The team that kicks the penalty is losing	63	26.3	85.7
	The team that kicks the penalty is winning	73	30.4	69.9
Scoreboard	The team that takes the penalty is tying the match.	104	43.3	84.6	χ2 = 201.233 p < 0.001	χ2 =12.663; p = 0.049 *; V = 0.23
	The team that kicks the penalty is losing by 1 goal.	41	17.1	85.4
	The team that kicks the penalty is losing by 2 goals.	15	6.3	86.7
	The team that kicks the penalty is losing by 3+ goals.	7	2.9	85.7
	The team that kicks the penalty is winning by 1 goal.	42	17.5	66.7
	The team that kicks the penalty is winning by 2 goals.	23	9.6	65.2
	The team that kicks the penalty is winning by 3+ goals.	8	3.3	100
Laterality	The kicking player is right—footed	188	78.3	80.9	χ2 = 77.067 p < 0.001	χ2 = 0.104; p = 0.747; V = 0.02
	The kicking player is left—footed	52	21.7	78.8
Run—up to the Kick	The kicker takes fewer than three steps before striking the ball	22	9.2	72.7	χ2 = 343.75 p < 0.001	χ2 = 5.334; p = 0.069; V = 0.15
	The kicker takes three or more steps before striking the ball	215	89.6	81.9
	The kicker clearly pauses during the run—up	3	1.3	33.3
Goalkeeper actions prior to the kick	Small sideways steps in the center of the goal without combining them with arm movements (legs only)	12	5	66.7	χ2 = 257.000 p < 0.001	χ2 = 4.879; p = 0.559; V = 0.14
	Exaggerated lateral movement to the kicker’s right side.	9	3.8	77.8
	Exaggerated lateral movement to the kicker’s left side.	9	3.8	100
	Goalkeeper remains stationary in the center of the goal without moving.	110	45.8	82.7
	Exaggerated movement of both arms and legs in the center of the goal (without displacement).	8	3.3	75
	Arm movements only in the center of the goal (without displacement).	33	13.8	81.8
	Small jumps in the center of the goal (without displacement).	59	24.6	76.3
Direction—goal (depending on the view of the kicker)	Left—Top	11	4,6	90.9	χ2 = 287.775 p < 0.001	χ2 =13.551; p = 0.094; V = 0.26
	Left—Medium height	28	11.7	57.1
	Left—Down	93	38.8	60.6
	Centre—Top	3	1.3	100
	Centre—Medium height	5	2.1	80
	Centre—Down	7	2.9	100
	Right—Top	12	5.0	83.3
	Right—Medium height	18	7.5	83.3
	Right—Down	63	26.3	84.1
Direction— laterality (depending on the view of the kicker)	Kicker—Top	15	6.3	93.3	χ2 = 284.700 p < 0.001	χ2 =11.793; p = 0.161; V = 0.25
	Kicker—Medium	29	12.6	62.1
	Kicker—Down	91	37.9	84.6
	Middle—Top	3	1.3	100
	Middle—Medium	5	2.1	80
	Middle—Down	7	2.9	100
	Far—Top	9	3.8	77.8
	Far—Medium	16	6.7	81.3
	Far—Down	65	27.1	76.9
Penalty Outcome (detailed)	The penalty ends in goal	193	80.4	-	χ2 = 243.975 p < 0.001	-
	The penalty is saved	37	15.4	-
	The penalty is missed	10	4.2	-
Penalty Outcome (binary)	Goal	193	80.4	-	χ2 = 88.817 p < 0.001	-
	Error	47	19.6	-

Note: SR = Success Rate; GOF = goodness-of-fit; χ² = value of the chi-square statistic; V = Cramér’s V (Effect Size is reported as Cramér’s V for chi-square tests; interpretation thresholds as specified in the Section 2); * Statistical significance (p < 0.05).

Figure 2. Goal areas according to direction-goal and direction-laterality.

Figure 2. Goal areas according to direction-goal and direction-laterality.

Table 2. Descriptive statistics and results of chi-square goodness-of-fit (χ² GOF) and independence tests for each criterion in Liga F and the Women’s Super League.

		LIGA F					WSL					Interleague
Criteria	Description	n	%	SR	χ2 GOF	χ2 Independence	n	%	SR	χ2 GOF	χ2 Independence	χ2 Independence
Stadium	Home team	90	57.7	85.6	χ2 = 3.692	χ2 = 3.935; p = 0.047 *; V = 0.16	54	64.3	85.2	χ2 = 6.857	χ2 = 1.757; p = 0.185; V = 0.14	χ2 = 0.989; p = 0.320; V = 0.06
	Away team	66	42.3	72.7	p = 0.055		30	35.7	73.3	p = 0.009
Time	0–15 min	14	9	78.6	χ2 = 10.385	χ2 = 3.053; p = 0.692; V = 0.14	8	9.5	75	χ2 = 10.429	χ2 = 2.049; p = 0.842; V = 0.16	χ2 = 3.086; p = 0.687; V = 0.11
	16–30 min	21	13.5	90.5	p = 0.065		11	13.1	81.8	p = 0.064
	31–45+ min	30	19.2	73.3			9	10.7	66.7
	46–60 min	32	20.5	81.3			20	23.8	80
	61–75 min	26	16.7	84.6			16	19.0	87.5
	76–90+ min	33	21.2	75.8			20	23.8	85
Result	Tying	68	43.6	83.8	χ2 = 7.423	χ2 = 3.230; p = 0.199; V = 0.14	36	42.9	86.1	χ2 = 4.571	χ2 = 4.797; p = 0.091; V = 0.24	χ2 = 0.661; p = 0.718; V = 0.05
	Losing	43	27.6	83.7	p = 0.024		20	23.8	90	p = 0.102
	Winning	45	28.8	71.1			28	33.3	67.9
Scoreboard	Tying	68	43.6	83.8	χ2 = 149.667	χ2 = 8.025; p = 0.236; V = 0.23	36	42.9	86.1	χ2 = 63.000	χ2 = 9.114; p = 0.167; V = 0.33	χ2 = 14.734; p = 0.022 *; V = 0.25
	Losing by 1 goal.	34	21.8	82.4	p < 0.001		7	8.3	100	p < 0.001
	Losing by 2 goals	6	2.8	100			9	10.7	77.8
	Losing by 3+ goals	3	1.9	66.7			4	4.8	100
	Winning by 1 goal	29	18.6	65.5			13	15.5	69.2
	Winning by 2 goals	11	7.1	72.7			12	14.3	58.3
	Winning by 3+ goals	5	3.2	100			3	3.6	100
Laterality	Right—footed	117	75	78.6	χ2 = 39.000	χ2 = 0.658; p = 0.417; V = 0.07	71	84.5	84.5	χ2 = 40.048	χ2 = 3.759; p = 0.053; V = 0.21	χ2 = 2.918; p = 0.088; V = 0.11
	Left—footed	39	25	84.6	p < 0.001		13	15.5	61.5	p < 0.001
Run—up to the Kick	Less three steps	16	10.3	62.5	χ2 = 210.038	χ2 = 8.066; p = 0.018 *; V = 0.23	6	7.1	100	χ2 = 61.714	χ2 = 1.520; p = 0.218; V = 0.14	χ2 = 2.347; p = 0.309; V = 0.10
	Three or more steps	137	87.8	83.2	p < 0.001		78	92.9	79.5	p < 0.001
	Pauses during the run—up	3	1.9	33.3			0	0	0
Goalkeeper actions prior to the kick	Side steps center (legs)	7	4.5	42.9	χ2 = 170.487	χ2 = 15.616; p = 0.016 *; V = 0.32	5	6.0	100	χ2 = 87.000	χ2 = 7.326; p = 0.292; V = 0.30	χ2 = 6.236; p = 0.397; V = 0.16
	Move right (exaggerated)	6	3.8	100	p < 0.001		3	3.6	33.3	p < 0.001
	Move left (exaggerated)	9	5.8	100			1	1.2	100
	Stationary center	74	47.4	85.1			35	41.7	77.1
	Arms & legs center	4	2.6	50			4	4.8	100
	Arms only center	23	14.7	82.6			10	11.9	80
	Jumps center	33	21.2	69.7			26	31.0	84.6
Direction—goal	Left—Top	6	3.8	83.3	χ2 = 144.462	χ2 = 9.999; p = 0.265; V = 0.29	5	6.0	100	χ2 = 131.619	χ2 = 6.103; p = 0.528; V = 0.27	χ2 = 10.171; p = 0.253; V = 0.21
	Left—Medium height	21	13.5	57.1	p < 0.001		7	8.3	57.1	p < 0.001
	Left—Down	52	33.3	82.7			41	48.8	78
	Centre—Top	3	1.9	100			0	0	0
	Centre—Medium height	4	2.6	75			1	1.2	100
	Centre—Down	5	3.2	100			2	2.4	100
	Right—Top	10	6.4	80			2	2.4	100
	Right—Medium height	14	9.0	78.6			4	4.8	100
	Right—Down	41	26.3	85.4			22	26.2	81.8
Direction— laterality	Kicker—Top	10	6.4	90	χ2 = 149.423	χ2 = 8.721; p = 0.366; V = 0.27	5	6.0	100	χ2 = 118.667	χ2 = 6.664; p = 0.465; V = 0.28	χ2 = 6.189; p = 0.626; V = 0.16
	Kicker—Medium	22	4.1	63.6	p < 0.001		7	8.3	57.1	p < 0.001
	Kicker—Down	53	34	86.8			38	45.2	81.6
	Middle—Top	3	1.9	100			0	0	0
	Middle—Medium	4	2.6	75			1	1.2	100
	Middle—Down	5	3.2	100			2	2.4	100
	Far—Top	7	4.5	71.4			2	2.4	100
	Far—Medium	11	7.1	72.7			5	6.0	100
	Far—Down	41	26.3	78			24	28.6	75
Penalty Outcome (detailed)	The penalty ends in goal	125	80.1	—	χ2 = 158.808	—	68	81.0	—	χ2 = 86.000	—	χ2 = 3.786; p = 0.151; V = 0.13
	The penalty is saved	27	17.3	—	p < 0.001		10	11.9	—	p < 0.001
	The penalty is missed	4	2.6	—			6	7.1	—
Penalty Outcome (binary)	Goal	125	80.1	—	χ2 = 56.641	—	68	81.0	—	χ2 = 32.190	—	χ2 = 0.024; p = 0.878; V = 0.01
	Error	31	19.9	—	p < 0.001		16	19.0	—	p < 0.001

Note: WSL = Women’s Super League; SR = Success Rate; GOF = goodness-of-fit; χ² = value of the chi-square statistic; V = Cramér’s V (Effect Size is reported as Cramér’s V for chi-square tests; interpretation thresholds as specified in the Section 2); * Statistical significance (p < 0.05).

Table 2. Descriptive statistics and results of chi-square goodness-of-fit (χ² GOF) and independence tests for each criterion in Liga F and the Women’s Super League.

		LIGA F					WSL					Interleague
Criteria	Description	n	%	SR	χ2 GOF	χ2 Independence	n	%	SR	χ2 GOF	χ2 Independence	χ2 Independence
Stadium	Home team	90	57.7	85.6	χ2 = 3.692	χ2 = 3.935; p = 0.047 *; V = 0.16	54	64.3	85.2	χ2 = 6.857	χ2 = 1.757; p = 0.185; V = 0.14	χ2 = 0.989; p = 0.320; V = 0.06
	Away team	66	42.3	72.7	p = 0.055		30	35.7	73.3	p = 0.009
Time	0–15 min	14	9	78.6	χ2 = 10.385	χ2 = 3.053; p = 0.692; V = 0.14	8	9.5	75	χ2 = 10.429	χ2 = 2.049; p = 0.842; V = 0.16	χ2 = 3.086; p = 0.687; V = 0.11
	16–30 min	21	13.5	90.5	p = 0.065		11	13.1	81.8	p = 0.064
	31–45+ min	30	19.2	73.3			9	10.7	66.7
	46–60 min	32	20.5	81.3			20	23.8	80
	61–75 min	26	16.7	84.6			16	19.0	87.5
	76–90+ min	33	21.2	75.8			20	23.8	85
Result	Tying	68	43.6	83.8	χ2 = 7.423	χ2 = 3.230; p = 0.199; V = 0.14	36	42.9	86.1	χ2 = 4.571	χ2 = 4.797; p = 0.091; V = 0.24	χ2 = 0.661; p = 0.718; V = 0.05
	Losing	43	27.6	83.7	p = 0.024		20	23.8	90	p = 0.102
	Winning	45	28.8	71.1			28	33.3	67.9
Scoreboard	Tying	68	43.6	83.8	χ2 = 149.667	χ2 = 8.025; p = 0.236; V = 0.23	36	42.9	86.1	χ2 = 63.000	χ2 = 9.114; p = 0.167; V = 0.33	χ2 = 14.734; p = 0.022 *; V = 0.25
	Losing by 1 goal.	34	21.8	82.4	p < 0.001		7	8.3	100	p < 0.001
	Losing by 2 goals	6	2.8	100			9	10.7	77.8
	Losing by 3+ goals	3	1.9	66.7			4	4.8	100
	Winning by 1 goal	29	18.6	65.5			13	15.5	69.2
	Winning by 2 goals	11	7.1	72.7			12	14.3	58.3
	Winning by 3+ goals	5	3.2	100			3	3.6	100
Laterality	Right—footed	117	75	78.6	χ2 = 39.000	χ2 = 0.658; p = 0.417; V = 0.07	71	84.5	84.5	χ2 = 40.048	χ2 = 3.759; p = 0.053; V = 0.21	χ2 = 2.918; p = 0.088; V = 0.11
	Left—footed	39	25	84.6	p < 0.001		13	15.5	61.5	p < 0.001
Run—up to the Kick	Less three steps	16	10.3	62.5	χ2 = 210.038	χ2 = 8.066; p = 0.018 *; V = 0.23	6	7.1	100	χ2 = 61.714	χ2 = 1.520; p = 0.218; V = 0.14	χ2 = 2.347; p = 0.309; V = 0.10
	Three or more steps	137	87.8	83.2	p < 0.001		78	92.9	79.5	p < 0.001
	Pauses during the run—up	3	1.9	33.3			0	0	0
Goalkeeper actions prior to the kick	Side steps center (legs)	7	4.5	42.9	χ2 = 170.487	χ2 = 15.616; p = 0.016 *; V = 0.32	5	6.0	100	χ2 = 87.000	χ2 = 7.326; p = 0.292; V = 0.30	χ2 = 6.236; p = 0.397; V = 0.16
	Move right (exaggerated)	6	3.8	100	p < 0.001		3	3.6	33.3	p < 0.001
	Move left (exaggerated)	9	5.8	100			1	1.2	100
	Stationary center	74	47.4	85.1			35	41.7	77.1
	Arms & legs center	4	2.6	50			4	4.8	100
	Arms only center	23	14.7	82.6			10	11.9	80
	Jumps center	33	21.2	69.7			26	31.0	84.6
Direction—goal	Left—Top	6	3.8	83.3	χ2 = 144.462	χ2 = 9.999; p = 0.265; V = 0.29	5	6.0	100	χ2 = 131.619	χ2 = 6.103; p = 0.528; V = 0.27	χ2 = 10.171; p = 0.253; V = 0.21
	Left—Medium height	21	13.5	57.1	p < 0.001		7	8.3	57.1	p < 0.001
	Left—Down	52	33.3	82.7			41	48.8	78
	Centre—Top	3	1.9	100			0	0	0
	Centre—Medium height	4	2.6	75			1	1.2	100
	Centre—Down	5	3.2	100			2	2.4	100
	Right—Top	10	6.4	80			2	2.4	100
	Right—Medium height	14	9.0	78.6			4	4.8	100
	Right—Down	41	26.3	85.4			22	26.2	81.8
Direction— laterality	Kicker—Top	10	6.4	90	χ2 = 149.423	χ2 = 8.721; p = 0.366; V = 0.27	5	6.0	100	χ2 = 118.667	χ2 = 6.664; p = 0.465; V = 0.28	χ2 = 6.189; p = 0.626; V = 0.16
	Kicker—Medium	22	4.1	63.6	p < 0.001		7	8.3	57.1	p < 0.001
	Kicker—Down	53	34	86.8			38	45.2	81.6
	Middle—Top	3	1.9	100			0	0	0
	Middle—Medium	4	2.6	75			1	1.2	100
	Middle—Down	5	3.2	100			2	2.4	100
	Far—Top	7	4.5	71.4			2	2.4	100
	Far—Medium	11	7.1	72.7			5	6.0	100
	Far—Down	41	26.3	78			24	28.6	75
Penalty Outcome (detailed)	The penalty ends in goal	125	80.1	—	χ2 = 158.808	—	68	81.0	—	χ2 = 86.000	—	χ2 = 3.786; p = 0.151; V = 0.13
	The penalty is saved	27	17.3	—	p < 0.001		10	11.9	—	p < 0.001
	The penalty is missed	4	2.6	—			6	7.1	—
Penalty Outcome (binary)	Goal	125	80.1	—	χ2 = 56.641	—	68	81.0	—	χ2 = 32.190	—	χ2 = 0.024; p = 0.878; V = 0.01
	Error	31	19.9	—	p < 0.001		16	19.0	—	p < 0.001

Note: WSL = Women’s Super League; SR = Success Rate; GOF = goodness-of-fit; χ² = value of the chi-square statistic; V = Cramér’s V (Effect Size is reported as Cramér’s V for chi-square tests; interpretation thresholds as specified in the Section 2); * Statistical significance (p < 0.05).

Overall effectiveness was 80.4%, with 15.4% saved and 4.2% missed. No significant differences in success rates were found between Liga F (80.1%) and the Women’s Super League (81%) (χ² = 0.024, p = 0.878; V = 0.01, trivial effect). However, the distribution of penalty attempts across leagues deviated significantly from uniformity (χ² = 21.600, p < 0.001), indicating that a greater number of penalties occurred in Liga F than in the WSL.

Contextual variables showed clearer effects. Penalties taken by home teams were more successful than those by away teams (85.4% vs. 72.9%; χ² = 5.715, p = 0.017; V = 0.15, small effect). Timing within the match also influenced outcomes, with a significant deviation from uniformity (χ² = 17.650, p = 0.003). Conversion was lowest in the final 15 min of the first half (71.8%), followed by the opening 15 min (77.3%) and the final stages of the match (79.2%). The highest rate was observed between minutes 16–30 (87.5%). However, no significant association between timing and outcome was detected (χ² = 3.797, p = 0.579; V = 0.13, small effect).

Match status was another decisive factor. Penalties executed when the team was losing (85.7%) or tied (84.6%) were more successful than when leading (69.9%), with significant associations in both the result (χ² = 7.450, p = 0.024; V = 0.18, small effect) and scoreboard variables (χ² = 12.663, p = 0.049; V = 0.23, small effect). In particular, penalties taken while leading by one or two goals dropped to 66.7% and 65.2%, respectively, compared with success rates above 85% when losing.

Player-related variables showed limited impact. Right-footed (78.3% of attempts) and left-footed kickers (21.7%) displayed similar effectiveness (80.9% vs. 78.8%; χ² = 0.104, p = 0.747; V = 0.02, trivial effect). Run-up patterns revealed reduced success when a clear pause occurred (33.3%), compared with shorter (<3 steps, 72.7%) or longer (≥3 steps, 81.9%) approaches (χ² = 5.334, p = 0.069; V = 0.15, small effect).

Goalkeeper actions prior to the kick showed no significant association with outcome (χ² = 4.879, p = 0.559; V = 0.14, small effect). The highest effectiveness occurred when goalkeepers moved laterally to the kicker’s left (100%), while stationary positioning (82.7%) and small jumps in place (76.3%) were more common.

Shot placement showed the clearest performance pattern. Central and top-corner attempts yielded conversion rates above 80%, whereas shots aimed at the lower left zone achieved only 60.6% (χ² = 13.551, p = 0.094; V = 0.26, small effect). This trend was corroborated by the predictive model, where shot placement emerged as a significant predictor of success. A similar pattern appeared in the laterality-based analysis, with top and central zones exceeding 93% effectiveness compared with 62.1% at medium height (χ² = 11.793, p = 0.161; V = 0.25, small effect).

Table 2 presents the results separated by league, enabling direct comparison between Liga F and the Women’s Super League. This breakdown highlights contextual and technical variations in penalty execution and complements the overall findings of Table 1. The chi-square goodness-of-fit test examined whether the distribution of categories within each league deviated from uniformity, while the chi-square independence test was used to compare distributions between the two leagues for each criterion.

For clarity,

Table 3. Summary of significant associations between contextual and technical variables and penalty success.

Criterion	League	χ2 (Independence)	p	Cramer’s V	Interpretation
Stadium (home vs. away)	Liga F	3.935	0.047	0.16	Small
Result (winning/tying/losing)	Both	7.450	0.024	0.18	Small
Scoreboard (goal difference)	Both	12.663	0.049	0.23	Small
Run-up length (<3 vs. ≥3 steps)	Liga F	8.066	0.018	0.23	Small
Goalkeeper actions before the kick	Liga F	15.616	0.016	0.32	Moderate

Note: Only significant (p ≤ 0.05) associations are reported. χ² = chi-square statistic; V = Cramer’s V; interpretation thresholds according to Cohen [36]: trivial < 0.10; small 0.10–0.29; moderate 0.30–0.49; large ≥ 0.50.

summarizes the main significant associations (p ≤ 0.05) between contextual and technical variables and penalty success across leagues.

Although overall conversion rates were nearly identical in Liga F (80.1%) and the WSL (81.0%), several contextual and technical differences emerged.

Home advantage was more evident in Liga F, where home teams achieved 85.6% success compared with 72.7% for away teams (χ² = 3.935, p = 0.047; V = 0.16, small effect). In the WSL, the disparity was smaller and not significant (χ² = 1.757, p = 0.185; V = 0.14, small effect). The interleague comparison confirmed no significant difference (χ² = 0.989, p = 0.320; V = 0.06, trivial effect).

Temporal distributions were comparable across competitions. In both leagues, penalties occurred unevenly throughout the match (Liga F: χ² = 10.385, p = 0.065; WSL: χ² = 10.429, p = 0.064), but no significant associations with outcome were found (Liga F: V = 0.14; WSL: V = 0.16; interleague: V = 0.11, all small effects).

Match status showed league-dependent differences. In Liga F, penalties taken while winning were less effective (65.5–72.7%) than those executed when drawing or losing (≥82%), with a small effect (V = 0.14). In the WSL, the effect was stronger (p = 0.091; V = 0.24, small effect).

A similar pattern appeared in the scoreboard variable. Both leagues showed higher conversion rates when trailing or level than when leading. The association reached moderate magnitudes (Liga F: V = 0.23; WSL: V = 0.33), and the interleague comparison confirmed a significant difference (χ² = 14.734, p = 0.022; V = 0.25, small effect).

Regarding laterality, Liga F players were mostly right-footed (75%) and achieved 78.6% success, while left-footed kickers (25%) reached 84.6%. In the WSL, right-footed kickers were more effective (84.5% vs. 61.5%), but the overall effects remained small (Liga F: V = 0.07; WSL: V = 0.21; interleague: V = 0.11).

Run-up patterns also differed between leagues. In Liga F, longer approaches (≥3 steps) produced higher success (83.2%) than shorter ones (62.5%), a significant association (χ² = 8.066, p = 0.018; V = 0.23, small effect). In the WSL, the relationship was not significant (p = 0.218; V = 0.14, small effect).

Goalkeeper actions before the kick varied between leagues. In Liga F, small side steps without arm movements were linked to reduced success (42.9%), resulting in a significant association (χ² = 15.616, p = 0.016; V = 0.32, moderate effect). In the WSL, the same pattern yielded V = 0.30 (moderate effect) but without statistical significance.

Shot direction showed small-to-moderate effect sizes without statistical significance (Liga F: χ² = 9.999, p = 0.265; V = 0.29; WSL: χ² = 6.103, p = 0.528; V = 0.27). Comparable results were found for the laterality-based analysis (Liga F: V = 0.27; WSL: V = 0.28; interleague: V = 0.16).

Finally, no significant differences were found in detailed or binary penalty outcomes across leagues (χ² = 3.786, p = 0.151; V = 0.13, small effect; and χ² = 0.024, p = 0.878; V = 0.01, trivial effect, respectively).

Overall, the magnitude of league-specific effects ranged between small and moderate (V = 0.10–0.33), confirming consistent overall success rates but minor contextual and technical variations between competitions.

To complement the descriptive and associative analyses, a binary logistic regression model was conducted to examine the predictive contribution of contextual and technical variables to penalty success.

The model was statistically significant (χ²(26) = 48.47, p = 0.005) and showed a good fit (Hosmer–Lemeshow test: χ²(8) = 6.03, p = 0.64), explaining 29.1% of the variance in outcomes (Nagelkerke R² = 0.29).

Overall, 81.3% of the attempts were correctly classified. Stadium location (p = 0.002), match status (p = 0.005–0.009), run-up pattern (p = 0.042–0.006), goalkeeper action (p < 0.001), and shot placement (p < 0.001) emerged as significant predictors of penalty success, whereas laterality and league showed no significant effect.

These findings confirm that a small set of contextual and technical factors can reliably predict penalty effectiveness.

Figure 3 shows penalty effectiveness by shot placement for all penalties (n = 240) and for shots on target (n = 230).

In the direction-goal analysis (upper panels), top and central zones yielded the highest conversion rates, often reaching 100%. In contrast, medium-height shots were the least effective, particularly on the left side (57.1%). Downward zones were the most frequently targeted and displayed intermediate success (80.6–84.1%), with slightly higher values when only on-target attempts were considered (e.g., left-down: 85.2% vs. 80.6%).

The direction-laterality analysis (lower panels) revealed a similar pattern. Top and central zones again achieved the greatest effectiveness (≥93%), while medium placements showed the lowest reliability (62.1–80%). Downward zones, although the most common, improved in success rates when restricted to shots on target.

Figure 4 displays penalty effectiveness by shooting area, separated by league and distinguishing between all penalties (Liga F: n = 156; WSL: n = 84) and only shots on target (Liga F: n = 152; WSL: n = 78).

In Liga F (upper panels), the highest conversion rates were recorded in the top and central zones (often 100%), while medium-height shots on the left side showed the lowest effectiveness (57.1%). Downward shots were the most common, with effectiveness ranging from 82.7% (left-down) to 85.4% (right-down), which increased slightly when only on-target attempts were considered (84.3% and 85.4%, respectively).

In the WSL (lower panels), a comparable pattern was observed: top and central zones reached maximum effectiveness (100%), whereas left-medium height produced the weakest results (57.1%). Downward shots were again the most frequent, especially to the left (47.4% of attempts), with success improving from 78% to 86.5% when restricted to on-target attempts. Right-down shots also maintained high effectiveness (81.8–90%).

Overall, both leagues followed the same trend: penalties directed to the top and central areas were most effective, medium-height placements were least reliable, and downward zones—though the most frequently targeted—showed moderate-to-high success, particularly when limited to shots on target.

4. Discussion

This study provides novel evidence on penalty performance in elite women’s football, analyzing 240 attempts from Liga F and the Women’s Super League. The findings revealed an overall effectiveness of 80.4%, which lies at the upper end of values reported in previous literature. Contextual factors such as stadium location and match status significantly influenced outcomes, while technical aspects like shot placement emerged as decisive. In contrast, variables including laterality and goalkeeper actions showed limited impact. Taken together, these results extend current knowledge by confirming well-established trends in men’s football and highlighting league-specific dynamics in the women’s game.

4.1. Overall Effectiveness

The overall penalty effectiveness observed in this study was 80.4%, which lies at the high end of ranges most frequently reported in prior studies. Most studies conducted in elite men’s football have described success rates between 75% and 80% [4,6,7,8,17,37], while a smaller number have reported markedly lower values, close to 67% [9]. Taken together, these findings confirm that elite women’s football is aligned with, and in some cases exceeds, benchmarks established in previous samples.

In the specific case of women’s football, lower effectiveness has been previously described, with a success rate of 70.1–72.99% [24,26], notably below the value obtained in the present study. This difference may reflect recent developments in the women’s game, including tactical refinement, enhanced goalkeeper analysis, improved psychological preparation of penalty takers, and the growing professionalization of elite female competitions.

Beyond overall success, the present data also showed that 15.4% of penalties were saved by the goalkeeper and 4.2% were missed off target. These values are very close to those described in several studies that reported save percentages around 14% and missed penalties between 3% and 6% [4,6,7,19]. By contrast, other research has documented higher rates of saves, between 17% and 19%, and considerably more missed attempts, up to 13% [9]. This comparison indicates that the present distribution reflects not only a relatively high overall effectiveness but also a comparatively lower proportion of unsuccessful attempts than some previously reported samples.

4.2. Stadium Factor

The present analysis revealed higher effectiveness when penalties were taken at home (85.6%) compared with away matches (72.7%). Similar tendencies have been reported in previous research, where home advantage has often been observed across competitions, including women’s football [6,9,25,26,37]. However, other studies have not confirmed this pattern, suggesting that penalty success may not always be significantly influenced by stadium location [4,7,8,38].

These discrepancies highlight that the influence of the home environment may depend on contextual and methodological factors. Psychological support from the crowd, familiarity with the playing environment, and reduced travel demands are among the explanations commonly proposed for higher success at home. In any case, the present findings add to the body of evidence pointing toward a potential home advantage in penalty situations, while also underlining the need for further research in female contexts.

4.3. Match Timing

The timing of the penalty during the match significantly affected conversion rates (i.e., success rates), with the lowest effectiveness observed in the final minutes of the first half (71.8%), followed by the opening minutes (77.3%) and the final stage of the match (79.2%). In contrast, the highest success was recorded between minutes 16 and 30 (87.5%). Comparable tendencies have been documented in some studies which likewise found reduced effectiveness around halftime [7], while others pointed to alternative vulnerable periods, such as the final minutes of the match [8] or the opening minutes [4].

This heterogeneity suggests that the influence of time on penalty performance may be shaped by multiple contextual factors, including physiological fatigue, psychological pressure linked to match dynamics, and the situational weight of the moment. The present results support the idea that certain periods pose greater challenges for penalty takers, although the exact timing of vulnerability differs across studies and competitions [4,7,8].

4.4. Match Status

Penalty effectiveness was strongly influenced by the scoreline at the moment of execution. In the present study, success rates were highest when the team was drawing (84.6%) or losing (85.7%), and clearly lower when the team was leading (69.9%). Consistent results have been reported in other studies that also identified lower success when teams were leading [8]. However, other studies identified alternative vulnerable contexts, such as tied situations [7] or when the team was behind [4,6]. In contrast, some evidence suggests greater success when the kicking team was leading [26,37].

Taken together, these discrepancies indicate that match status exerts a significant but not uniform influence on penalty performance. The variability across studies may be related to sample composition, competitive level, or psychological factors linked to pressure and perceived risk. The current results support the idea that penalties executed while leading may be subject to specific psychological dynamics—such as reduced concentration or overconfidence—that lower effectiveness compared to situations where the team is drawing or losing [4,6,7,8,26,37].

4.5. Laterality

The analysis of laterality showed that right-footed players achieved slightly higher effectiveness (80.9%) than left-footed players (78.8%). Similar results have been described in several investigations, where right-footed kickers generally achieved higher success rates overall [4,6,7,8]. Nonetheless, other studies have reported the opposite tendency, identifying greater effectiveness among left-footed players [9,25]. League-specific differences should also be noted: in Liga F, left-footed players outperformed right-footed ones, whereas in the WSL the opposite pattern was observed, with right-footed players more effective.

These contrasting findings suggest that laterality is not a determinant factor by itself but may interact with other contextual and technical variables, such as the goalkeeper’s anticipation strategies, the preferred shooting zones of right- vs. left-footed players, or the distribution of dominant feet in the sample under study. The present study therefore contributes to the ongoing debate by reinforcing the general trend of higher success among right-footed players, while acknowledging that exceptions to this pattern have also been documented [4,6,7,8,9].

4.6. Shot Placement (Goal Direction)

The present results confirmed that penalties directed toward the upper and central areas of the goal achieved the highest effectiveness, frequently reaching values close to or equal to 100%. In contrast, medium-height shots, particularly to the left side, showed the lowest success rates (57.1%). These tendencies are consistent with previous research which highlighted the advantage of aiming for upper and central zones and pointed to medium-height attempts as the least effective [3,7,8,9].

This pattern may be explained by biomechanical and perceptual factors. Upper and central placements are more difficult for goalkeepers to reach due to the required vertical and lateral displacement, whereas medium-height zones are closer to the goalkeeper’s natural range of motion, making them easier to intercept. The present findings reinforce the strategic importance of accurate placement in maximizing penalty effectiveness, particularly in elite contexts where goalkeeper anticipation and preparation are highly developed [3,7,8,9]. In addition, it has been suggested that, beyond targeting the upper central zone, the strike should also be executed with high power to increase ball velocity, thereby reducing the goalkeeper’s reaction time and further enhancing the probability of success [39].

4.7. Laterality-Related Placement

When analyzed from the perspective of laterality-related areas, the lowest effectiveness was observed in the kicker-medium zones, while higher success was recorded in the kicker-down zones compared to far-down placements. Similar tendencies have been documented in some studies which also identified medium areas as particularly vulnerable for penalty takers [9]. However, other research has highlighted different zones as the least effective, such as kicker-down [8] or center-down [7].

These inconsistencies suggest that spatial categorization based on laterality may be sensitive to methodological approaches, competition level, and sample characteristics. Nonetheless, the present results align with the broader consensus that medium placements—whether vertical or lateral—are less favorable, while top and central areas remain the most effective for maximizing penalty success [7,8,9].

4.8. Predictive Interpretation

Beyond descriptive and associative analyses, integrating a predictive model provided additional insight into how contextual and technical factors jointly determine penalty success. In line with the associative findings, the predictive analysis showed that a small set of contextual and technical variables jointly explained a substantial proportion of penalty success. The logistic regression model correctly classified 81.3% of attempts (Nagelkerke R² = 0.29), with stadium location, match status, run-up pattern, goalkeeper action, and shot placement emerging as the strongest predictors. These results confirm the importance of situational and executional factors previously reported in men’s football and highlight their relevance within the women’s game, offering a more integrative understanding of penalty performance.

4.9. Practical Applications

The results of this study provide several practical implications for coaches and practitioners in elite women’s football. First, the identification of higher effectiveness at home suggests that psychological and environmental factors should be leveraged when preparing players for away matches, where penalty performance tends to decline. Second, the lower success rates observed in specific time intervals, particularly around halftime, indicate the need for targeted mental and physical routines to maintain concentration and execution quality throughout the match. Third, the reduced effectiveness of penalties taken while leading highlights the importance of psychological training to counteract potential overconfidence or relaxation in favorable scoreline contexts.

From a technical standpoint, the clear advantage of aiming at upper and central goal zones reinforces the need to prioritize accuracy and shot placement in training, while avoiding medium-height areas that consistently yield lower success rates. Additionally, the small differences observed between right-footed and left-footed players, together with the influence of run-up patterns and goalkeeper behaviors, emphasize the value of individualized preparation based on player profiles. Collectively, these applications can support evidence-based training strategies aimed at optimizing penalty performance in elite female competitions.

4.10. Limitations and Future Perspectives

Although the present study provides valuable evidence on penalty performance in elite women’s football, certain limitations should be acknowledged. First, the analysis was based exclusively on penalties from two domestic leagues, which may limit the generalizability of the findings to other competitions or international tournaments. Second, contextual factors such as player fatigue, match importance, or psychological characteristics of kickers and goalkeepers were not directly measured, despite their potential influence on performance. Third, the observational methodology, while rigorous and reliable, is inherently constrained by the availability and quality of video footage, resulting in the exclusion of a small number of attempts. An additional limitation concerns the difficulty of accessing complete match footage from earlier seasons, as video coverage was limited and not always systematically available. This limitation has been mitigated in recent years thanks to increased media dissemination and the publication of official highlights for each match.

Moreover, the statistical approach was extended by incorporating a binary logistic regression model to predict penalty success based on contextual and technical variables, thereby strengthening the inferential interpretation of the results. Nevertheless, future studies could further expand this perspective by applying multivariate or pattern-detection techniques—such as clustering or T-pattern analyses—to identify distinct penalty execution profiles and relate them to success rates once larger datasets are available. Finally, the retrospective and observational nature of the design prevents causal interpretations, and future research would benefit from longitudinal approaches to examine potential changes in penalty effectiveness across multiple seasons and competitions.

5. Conclusions

This study provides new evidence on penalty performance in elite women’s football, based on data from the Spanish Liga F and the English Women’s Super League. The overall success rate (80.4%) was consistent with benchmarks reported in men’s football, underscoring that penalties are a decisive yet complex event shaped by contextual and technical factors.

Key findings highlight that success is more likely when penalties are executed while drawing or losing, whereas attempts taken when leading carry a higher risk of failure. Home advantage and specific temporal intervals, particularly around halftime, also influenced performance. Technically, shot placement was confirmed as the most decisive factor, with upper and central zones associated with near-perfect conversion.

In contrast, variables such as laterality and goalkeeper actions showed limited influence overall, though league-specific patterns suggest potential differences in tactical preparation and execution styles between Spain and England.

These insights not only extend current knowledge but also offer applied implications for training and match preparation in elite women’s football. Coaches should prioritize accuracy and placement in upper and central zones, develop psychological strategies to counteract overconfidence when leading, and reinforce concentration during critical match periods.

Future studies should expand to other competitions and incorporate psychological and physiological measures to better understand the multifactorial nature of penalty performance in women’s football.