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Article

Analysis and Successful Patterns in One-Possession Games During the Last Minute in the Women’s EuroLeague

by
Christopher Vázquez-Estévez
1,
Iván Prieto-Lage
1,*,
Xoana Reguera-López-de-la-Osa
2,*,
Manuel Rodríguez-Crespo
1,
Jesús Antonio Gutiérrez-Santiago
1 and
Alfonso Gutiérrez-Santiago
1
1
Observational Research Group, Faculty of Education and Sport, University of Vigo, 36005 Pontevedra, Spain
2
Education, Physical Activity and Health Research Group (Gies10-DE3), Galicia Sur Health Research, Institute (IIS Galicia Sur), SERGAS-UVIGO, 36208 Vigo, Spain
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2025, 15(9), 5046; https://doi.org/10.3390/app15095046
Submission received: 27 January 2025 / Revised: 1 March 2025 / Accepted: 29 April 2025 / Published: 1 May 2025
(This article belongs to the Special Issue Advances in Sports Science and Movement Analysis)
Browse Figures
Versions Notes

Abstract

:
Despite the growing popularity of women’s basketball in recent years, scientific literature on the subject remains significantly less extensive compared to its male counterpart. The main objective of this research was to analyze successful offensive actions and patterns during critical moments in the Women’s EuroLeague. The sample consisted of 377 technical–tactical actions corresponding to plays with score differences of three points or less (one-possession games) in the final minute and overtime periods of the Women’s EuroLeague during the 2021/22 and 2022/23 seasons. This study was based on an observational design, utilizing the LINCE PLUS software together with a customized observation tool. Descriptive statistics and chi-square (χ2) tests were carried out using SPSS version 25, while T-Pattern detection was performed through Theme 5 software. A threshold for statistical significance was established at p < 0.05. The findings indicated that home teams achieved a higher percentage of successful plays compared to visiting teams. Most successful patterns occurred during the final phase of possession (8”–0”), regardless of game location or team result. Additionally, layups, plays involving shots after on-ball screen, and actions following personal fouls demonstrated the highest success rates. The practical implications discussed in this research provide valuable insights for coaches to optimize offensive strategies during high-pressure moments in elite women’s basketball.

1. Introduction

Despite the increasing popularity of women’s basketball in recent years [1], media coverage remains significantly lower compared to men’s basketball [2]. This disparity is also reflected in the scientific literature, where research focused on women’s basketball is still in the minority [3]. Topics investigated tend to mirror those of men’s basketball, despite the fact that differences in both game structure [4,5,6] and physical attributes [7] are significant. However, studies have highlighted that psychological factors such as choking—a phenomenon characterized by a decline in performance during high-pressure moments [8]—show no significant differences between genders [9].
Given these research gaps, understanding game dynamics in high-pressure moments is crucial, as among critical game scenarios that strongly impact the outcome, few are as decisive as having a close score with less than one minute remaining in regulation [10]. Studies have demonstrated that final possessions play a decisive role in determining outcomes in elite basketball [11]. In competitions such as the NBA, the most closely contested games are often decided in the final minute [12]. Having top offensive players on the court in a close game’s final moments is crucial, but ensuring they have enough energy for key rebounds, steals, and defensive plays may be even more important [13]. However, there is limited research focusing exclusively on the analysis of the last minute of games [10,14,15]. Among the few available studies, a decrease in player performance under pressure has been observed, particularly during free-throw shooting [10]. Beyond the temporal constraints, game-related variables such as score differential play a crucial role. In games where the margin is 1, 2, or 3 points, a single possession can drastically alter the outcome—a scenario commonly referred to as a “one-possession game” [16].
The study of game dynamics in women’s basketball has primarily focused on the analysis of game-related statistics in various contexts, but no one has examined the final moments of the game. During the final possessions, changes in game dynamics occur that influence player substitutions, tactical systems, personal fouls, and the number of points scored per unit of time [14]. In this way, studies have observed how in international tournaments, winning teams demonstrated significantly more points in the paint, points off turnovers, and second-chance points compared to losing teams during the 2017 Women’s EuroBasket [17].
Additionally, field goal percentage, defensive rebounds, steals, and turnovers combined to predict a 91.1% likelihood of winning during Olympic cycles spanning from 2004 to 2016 [18]. An analysis of regional variations in women’s basketball during four continental championships in 2017 showed that Asian and European competitions shared similar profiles, characterized by fewer possessions and turnovers alongside a higher frequency of successful field goals and assists. By contrast, African championships exhibited a greater number of possessions, free throws, and turnovers, whereas American tournaments were marked by lower success rates in field goals and assists but higher figures in steals and turnovers [19].
Studies have also highlighted the differences between winning and losing teams. For example, in closely contested games, winning teams achieved higher success rates in free throws, three-point field goals, defensive rebounds, and assists in the Spanish Women’s League [20]. Similarly, winning teams in the Turkish Women’s Basketball Super League playoffs demonstrated superior performance by executing more steals, committing fewer turnovers, improving field goal percentages (two- and three-point shots), and increasing both offensive and defensive rebounds [21]. Player performance has also been analyzed based on nationality. For example, foreign players showed better performance in most indicators, particularly in the percentage of minutes played, successful two-point field goals, free throws, and assists, depending on the team’s level and the player’s position [22]. Finally, in women’s basketball, performance indicators are also influenced by situational factors such as the stage of the league or the match status [23].
However, the main limitation of game-related statistics is that they are not stable properties of individual players or teams, as performance can vary significantly from one game to another [24]. Moreover, many studies analyze games statically, using performance indicators obtained from box scores after the game has concluded [25]. This approach prevents the detailed analysis of game elements such as offensive and defensive strategies, types of shots, and scoring dynamics [26]. In response, other studies have adopted dynamic methodologies to analyze performance indicators during games, focusing on each play in real time, as done in observational methodology [27]. For example, research on women’s basketball teams found that fastbreak actions had an average duration of 4.42 s, involved 1.22 passes and 2.13 players per play, and resulted in a scoring success rate of 66.3% [28]. Similarly, studies on the Spanish Women’s League identified off-ball movements, ball circulation, and individual plays (both interior and perimeter) as the most effective actions for scoring, particularly during fastbreak situations with a clear advantage [29].
Considering the gap in the literature regarding the lack of studies analyzing game dynamics during the final minute in women’s basketball—when players may be more susceptible to distractions and, consequently, impaired decision-making [30]—the objective of this study was to examine the effectiveness and identify successful play patterns executed during the final minute of regulation and overtime periods in the 2021/22 and 2022/23 Women’s EuroLeague seasons, specifically in situations where the score difference was three points or less, representing a one-possession game.

2. Methods

2.1. Design

The investigation examined the effectiveness and successful patterns of plays during critical moments in the 2021/22 and 2022/23 Women’s EuroLeague. To achieve this, this study applied an observational methodology [31], which effectively studies different sports disciplines by allowing the analysis of game actions in their natural context and dynamics [27]. Basketball research frequently utilizes this approach [15].
The observational design [32] adopted a nomothetic approach, examining the offensive actions of all participating teams. It followed a follow-up structure, covering 48 matches from the Women’s EuroLeague during the 2021/22 and 2022/23 seasons, and maintained a unidimensional focus, as each moment in the observation instrument involved a single response level. Additionally, this study implemented a non-participant-observation process, ensuring no direct interaction with the subjects.

2.2. Sample

The analysis focused on offensive technical–tactical actions taking place during the final minute of regulation time and throughout overtime periods in both the regular season and playoff stages of the 2021 and 2022 Women’s EuroLeague. The sample included only plays where the score difference in the final moments was equal to or less than 3 points (N = 377).

2.3. Instruments

To ensure a comprehensive analysis, this study developed a specific observational instrument that incorporated fifteen criteria and seventy-two categories (Table 1 and Table 2, and Figure 1—see results section). This category system ensured exhaustiveness and mutual exclusivity, with all criteria derived from various studies in the scientific literature [11,23]. The researchers recorded the data using LINCE PLUS software version 2.1 [33].

2.4. Procedure

In this study, the researchers sourced the analyzed videos from the official YouTube channel of the International Basketball Federation (FIBA) (accessed on 31 January 2024). The matches from the first round of the 2021/2022 Women’s EuroLeague were unavailable for download, so this study excluded them from the sample.
Once the available videos were gathered, the researchers isolated the final minute of the fourth quarter along with any overtime periods from each game. These segments were then compiled into a single file and arranged chronologically according to the match dates. Filmora software (version 10.1.20.15) facilitated the editing and creation of this file.
Expert observers, after undergoing targeted training in the use of the instruments, were tasked with recognizing and documenting the offensive technical–tactical behaviors. To assess the consistency of the data collection process [34], this study evaluated the accuracy of the recorded data by calculating Cohen’s Kappa coefficient [35] using the LINCE software.
For this analysis, the researchers selected 100 plays (one-fourth of the total sample).
The intra-observer reliability assessment produced Kappa coefficients of 0.92 and 0.93 for observer 1 and observer 2, respectively. Regarding inter-observer reliability, the analysis showed a Kappa value of 0.90. To ensure consistency and precision in data collection, any discrepancies identified were discussed and resolved jointly by the observers. Following this agreement process, both observers collaboratively carried out a final review of the entire dataset. Although either observer was qualified to perform this independently, a joint review was preferred to enhance the rigor and efficiency of the observation procedure.
Finally, this study compiled all the recorded actions into an Excel file, which provided flexibility for various transformations and analyses in the subsequent research phases.

2.5. Data Analysis

Statistical analyses were performed using IBM SPSS software (version 25.0, IBM-SPSS Inc., Chicago, IL, USA). Descriptive statistics were calculated for each variable, with results expressed through frequencies and percentages. A chi-square (χ2) goodness-of-fit test was conducted to detect differences across the categories of each criterion. Furthermore, chi-square (χ2) tests of independence were applied to explore the associations between all the criteria and the variables “basket” and “game location”. A significance level of p < 0.05 was established for all the statistical tests.
The T-Pattern analysis was conducted using Theme software (version 5.0) [36], with the significance level established at 0.005. This method allowed for the identification of the most prominent successful offensive patterns according to game location, team result, and possession. The Theme software proved instrumental in uncovering hidden structures and subtle behavioral sequences in basketball [37].

3. Results

3.1. General Descriptive Analysis

Table 1 and Table 2 provide a summary of the descriptive analysis and the results of the χ2 goodness-of-fit test applied to each criterion. The findings indicated the presence of significant differences in multiple study variables.
Furthermore, we conducted a χ2 test of independence to analyze the relationships between the various criteria in this study and those deemed most relevant by the researchers (basket and game location). Using basket as the reference criterion, the results revealed significant differences. In contrast, taking game location as the reference variable revealed significant differences in relation to team result, score difference, and ending player.
As illustrated in Figure 2, although the number of plays completed via fastbreaks was lower than those completed through set offense, the success rate was notably higher for fastbreaks compared to set offense (77.8% vs. 42.6%, respectively). Additionally, the analysis showed that as possession time progressed, the success rate decreased, dropping from 50% during the initial phase of possession (24”–17”) to 40.7% in the final phase (8”–0”). Regarding the type of finish, high success rates were recorded for plays ending after a screen (55.6%), with a layup (68.3%), or following a personal foul (67.4%). Finally, the center position achieved the highest success rate among all positions, with 56.3%.
Figure 3 shows that in 66.4% of cases, home teams started their plays while leading on the scoreboard, whereas away teams began their plays while trailing in 67.5% of cases. Additionally, away teams predominantly used 0 to 2 passes during their plays, while home teams executed a greater number of plays requiring 3 to 5 or more passes.
It was also observed that away teams most often completed their plays during the mid-possession phase (16”–9”, 53.2%), whereas home teams primarily concluded their plays in the final 8 s of possession (52.5%). Lastly, home teams achieved a higher success rate compared to visiting teams (54.5% vs. 45.5%, respectively).

3.2. Analysis of Successful Patterns on Offense

Table 3 displays the results of the T-Pattern analysis conducted on the 167 successful offensive actions identified. In relation to game location, a greater percentage of successful plays was recorded for home teams (54.5%) compared to away teams (44.5%).
Regarding the score situation at the start of each possession, home teams achieved their highest success rate when the game was tied (37.4%), followed by situations when they were ahead (35.2%), and finally when trailing (27.4%). In contrast, visiting teams recorded the majority of their successful actions when starting behind on the scoreboard (69.7%), with lower success rates when leading (18.4%) or tied (11.9%).
Additionally, the analysis showed that, regardless of whether the team was playing at home or away, or the result at the beginning of the possession, the majority of successful actions were executed in the final segment of the possession (8”–0”).
The main offensive patterns identified are detailed below, categorized according to game location, initial team result, and remaining possession time.

4. Discussion

Through an in-depth examination of closely contested elite women’s basketball games, this study uncovers important findings about offensive strategies during decisive phases, specifically the final minute and overtime periods of the 2021/22 and 2022/23 Women’s EuroLeague seasons. Focusing on high-stakes moments where the point difference was three or fewer, the analysis reveals critical factors shaping team performance under conditions of extreme physical and emotional pressure, thus deepening the understanding of the game in such demanding contexts.
First, the findings revealed that home teams achieved a greater number of successful actions compared to away teams. Moreover, at the start of plays, home teams were more likely to be leading, while away teams often began their plays trailing on the scoreboard. This phenomenon aligns with the “home advantage phenomenon” [38], which attributes home court benefits to factors such as crowd support, familiarity with facilities, and the absence of travel-related fatigue [39]. Previous studies on home advantage in women’s basketball have reported victory rates of 61.4% in the Greek Women’s Basketball League [40]. Additionally, home teams exhibited higher success rates in two-point field goals, offensive rebound plays, and steals, consistent with prior research in women’s basketball [40,41]. Conversely, away teams showed higher success rates in three-point shooting (60%), particularly from the central and right perimeter zones. These results are consistent with findings from another study analyzing game dynamics during the final quarters of NBA games [42]. This pattern may be attributed to home teams employing man-to-man defenses more frequently, which increases pressure on offensive players and forces visiting teams to attempt long-distance shots [43]. Despite these observations, home advantage appears to have a greater impact in men’s basketball than in women’s basketball [44]. This discrepancy may be due to smaller crowds in women’s competitions [45] or the influence of testosterone on territorial behavior, fostering a stronger sense of community and team belonging [46]. Nevertheless, home court advantage can be influenced by factors beyond familiarity with the environment and fan support, including referee bias and travel fatigue experienced by visiting teams [47]. Research suggests that the likelihood of a foul being called against the visiting team is approximately 7% higher compared to the home team under similar circumstances. Furthermore, when the home team is leading, the probability of receiving the next foul call increases by approximately 6.3 percentage points compared to when they are trailing [48]. Similarly, in the WNBA, it has been observed that home games with reported crowd densities above 50% received significantly fewer personal fouls on average than those with crowd densities below 50%, suggesting that larger crowds may be linked to referee bias [49]. However, more recent studies indicate that referee bias appears to be largely non-existent in the NBA during crucial game situations [50]. Referees’ decisions are likely influenced by their stress levels, which fluctuate throughout games; specifically, research shows that stress levels increase as the score differential decreases and game time winds down [51].
Another factor that may contribute to home teams’ superior performance is the impact of travel fatigue on visiting teams. Prolonged intermittent stress resulting from a travel-heavy season has been shown to cause significant changes in key physiological markers among female basketball players [52]. In professional leagues such as the NBA, studies have reported that air travel across three time zones increases susceptibility to travel fatigue, raises injury risk, and negatively impacts game performance [53]. Long-distance travel impairs athlete recovery due to factors such as the physical toll of travel and disruptions to circadian rhythm, leading to diminished player performance and a higher probability of injuries [54]. Nonetheless, it is believed that around two-thirds of this advantage is built during the first quarter and gradually declines as the game progresses [55]. In the final minute and during overtime, the influence of home advantage lessens as visiting teams adjust to challenges like hostile crowds and unfamiliar environments [56,57].
Regarding offensive strategies, although the number of fastbreak plays was limited during the final minute, their success rate (77.8%) was significantly higher than that of set plays (42.6%). This aligns with previous studies reporting fastbreak effectiveness rates of 73% [58] and 66.3% in the Spanish Women’s League [28]. Additionally, in critical game moments, fastbreaks have consistently demonstrated high success rates, with 67.2% effectiveness in the final two minutes of closely contested men’s EuroLeague games [59]. Other authors have likewise emphasized the importance of prioritizing fastbreaks during the final minutes of NBA games, highlighting their notably high success rates [11]. The limited number of fast breaks in the final minute may be explained by glycogen depletion, muscle damage, or reduced action potential due to accumulated physical exertion earlier in the game [60]. These factors hinder players’ ability to sustain high-intensity performance and are reflected in lower blood lactate concentrations and heart rates near the end of games [61]. Fatigue can negatively impact various performance parameters. Previous studies suggest that fatigue alters certain kinematic variables in female basketball players, modifying their shooting mechanics. However, elite female players can compensate for fatigue by readjusting their neuromuscular system, maintaining shooting efficiency under fatigue conditions [62]. Specifically, moderate-to-high fatigue impacts shooting technique over longer distances, particularly by reducing wrist height and consequently lowering the release height [63]. Additionally, fatigue significantly reduces passing accuracy and ball speed compared to non-fatigue conditions, further affecting offensive execution in critical moments [62].
Mental fatigue can also play a crucial role during this phase of the game, affecting both technical performance (e.g., free throws, three-point shooting) and cognitive performance (e.g., decision-making), ultimately preventing athletes from performing at their peak and potentially compromising game outcomes [64].
In terms of number of passes used and possession, home teams demonstrated a tendency toward longer possessions with more passes compared to away teams. As previously noted, home teams were more likely to start plays with a lead, making it logical to employ longer possessions with more passes to protect their advantage and control the game clock [59]. This trend has been corroborated in a recent study analyzing critical moments in the men’s and women’s EuroBasket tournaments [65]. Regarding passing efficiency, success rates were highest with 2 to 4 passes per possession, aligning with an average of 3.66 passes during positional plays in the men’s EuroLeague playoffs [66]. Conversely, longer possessions tended to result in lower scoring percentages, with fastbreaks showing the highest success rates. These findings mirror those in men’s teams, where effectiveness increased with possessions lasting between 0 and 20 s during the final five minutes of the game [23].
With respect to the type of defense, in 98.1% of cases, teams employed man-to-man defense, which aligns with previous research [67]. Studies indicate that during critical game moments, switching on Pick-and-Roll (PnR) plays is the most prevalent defensive strategy, demonstrating a notable success rate [68,69]. Aside from being the quickest way to defend a PnR action, switching also imposes lower physical demands on players. Specifically, it reduces Player Load and perceived exertion (RPE), since the switch condition allows defenders to cover less distance and maintain their defensive position more efficiently compared to other defensive strategies [70].
Finally, successful patterns revealed that, for leading teams, the majority of successful actions took place during the final phase of possession (8–0 s remaining), regardless of game location, aligning with observations made during critical moments of the men’s and women’s EuroBaskets [65]. Additionally, leading teams often completed successful actions via free throws drawn from personal fouls by opponents. This finding is identical to what was observed in a study conducted in the Men’s EuroLeague [15]. The reason may be explained by the logical strategy of trailing teams committing fouls to stop the clock and force leading teams to score from the free-throw line during close games [71]. Similar findings were reported in the Philippine collegiate league, where winning teams benefited from more fouls and extended their lead through free throws [72]. In the NBA, free throws accounted for 38.56% of points scored in the final 60–24 s and up to 94.02% of points scored with less than 4 s remaining [73].

4.1. Practical Applications

The findings of this study offer several practical applications for basketball coaches, particularly in preparing their teams for critical situations. It is essential to optimize time management during decisive possessions by designing specific plays that maximize the use of the final 8 s, as this phase accounts for most successful actions. Game location is also a key factor; for home games, it is recommended to implement more elaborate plays involving multiple passes, whereas for away games, training should focus on fastbreak strategies.
In scenarios where the score is close, coaches should prepare specific plays for tied situations, prioritizing quick and effective options such as layups or finishes near the basket. Conversely, if the team is trailing, it is fundamental to adopt aggressive strategies to quickly reduce the point deficit, emphasizing high-probability shots. Plays that involve screens and result in layups or situations that draw personal fouls should be a priority in training sessions due to their high success rates. Furthermore, assigning clear roles to players based on their strengths is vital, with an emphasis on maximizing the participation of centers in key moments, as they have demonstrated the highest success rates.
Simulating one-possession scenarios during the final minute in practice sessions can help players become accustomed to the pressure associated with such contexts.

4.2. Limitations and Future Perspectives

The results of this study should be interpreted with caution, as the analysis focused exclusively on games from the Women’s EuroLeague during the 2021/22 and 2022/23 seasons, which may limit the generalization of the findings to other competitions, categories, or contexts. Additionally, comparisons with results from other studies were challenging, as this research did not base its criteria on game-related statistics.
In the future, it would be valuable to expand this type of research to other competitions, categories, and contexts, including national leagues, men’s competitions, or youth categories.
Variables such as bench depth, player rotation, and game pace were not assessed in this study, but it is well known that the depth of player rotation significantly impacts on-court outcomes [13], potentially influencing the final moments of close games, which are characterized by a slower pace and greater score unpredictability [74]. Other factors, such as team ranking or timeout usage, could influence the research outcomes. Therefore, incorporating these variables in future studies could provide complementary insights into the findings obtained.

5. Conclusions

The findings indicate that actions performed in these contexts are influenced by factors such as game location, team result at the start of the play, and the possession phase during which the attack is developed. Regarding location, home teams demonstrated a higher number of successful actions compared to away teams. Additionally, it was found that most successful patterns occurred during the final phase of possession (8”–0”), regardless of location or team result. On the other hand, plays involving fewer passes were predominant among visiting teams, while home teams used more elaborate patterns with a higher number of passes. Likewise, finishing types such as layups, plays following screens, and actions after personal fouls proved to be the most effective. These findings provide valuable insights for coaching staff, who can use this knowledge to design more effective strategies in critical situations.

Author Contributions

Conceptualization, I.P.-L., C.V.-E., M.R.-C., X.R.-L.-d.-l.-O. and A.G.-S.; methodology, I.P.-L., C.V.-E., X.R.-L.-d.-l.-O. and A.G.-S.; software, I.P.-L., M.R.-C. and A.G.-S.; validation, J.A.G.-S., M.R.-C. and X.R.-L.-d.-l.-O.; formal analysis, I.P.-L. and A.G.-S.; investigation, I.P.-L., J.A.G.-S., C.V.-E., M.R.-C., X.R.-L.-d.-l.-O. and A.G.-S.; resources, I.P.-L., C.V.-E., J.A.G.-S., M.R.-C. and X.R.-L.-d.-l.-O.; data curation, I.P.-L., C.V.-E., J.A.G.-S. and A.G.-S.; writing—original draft, I.P.-L., M.R.-C., C.V.-E. and A.G.-S.; writing—review and editing, I.P.-L., X.R.-L.-d.-l.-O. and A.G.-S.; visualization, C.V.-E., J.A.G.-S. and M.R.-C.; supervision, I.P.-L., C.V.-E. and A.G.-S.; project administration, I.P.-L., J.A.G.-S., X.R.-L.-d.-l.-O. and A.G.-S.; funding acquisition, C.V.-E., I.P.-L. and A.G.-S. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Ministerio de Cultura y Deporte (https://www.culturaydeporte.gob.es/portada.html), Consejo Superior de Deportes (https://www.csd.gob.es/es (accessed on 20 June 2024)), and European Union (https://european-union.europa.eu/index_es (accessed on 20 June 2024)) under Project “Integración entre datos observacionales y datos provenientes de sensores externos: Evolución del software LINCE PLUS y desarrollo de la aplicación móvil para la optimización del deporte y la actividad física beneficiosa para la salud (2023)” EXP_74847 to A.G.-S. and I.P.-L. This research was funded by the Universidade de Vigo through a predoctoral fellowship awarded to C.V.-E. (Axudas Predoutorais para a formación de Doutoras/es 2022, Universidade de Vigo. P.P. 00VI 131H 6410211).

Institutional Review Board Statement

The study was approved by the ethics committee of the Faculty of Education and Sport Science (University of Vigo, application 06-280722).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are openly available in FigShare at doi https://doi.org/10.6084/m9.figshare.28902521. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Acknowledgments

This publication was made possible thanks to the research stays during the years 2023 and 2024 at the Instituto Politécnico de Viana do Castelo (IPVC)—Escola Superior de Desporto e Lazer.

Conflicts of Interest

The authors declare no conflicts of interest.

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Applsci 15 05046 g001
Figure 1. Ending court zone.
Figure 1. Ending court zone.
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Figure 2. Relationship between the “Basket” criteria and “Type of Offense”, “Possession”, “Type of Finish”, and “Ending Player” criteria.
Figure 2. Relationship between the “Basket” criteria and “Type of Offense”, “Possession”, “Type of Finish”, and “Ending Player” criteria.
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Figure 3. Relationship between the “Game Location” criteria and “Team Result”, “Number of Passes Used”, “Possession”, and “Basket” criteria.
Figure 3. Relationship between the “Game Location” criteria and “Team Result”, “Number of Passes Used”, “Possession”, and “Basket” criteria.
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Table 1. Observational instrument and descriptive analysis of this study (first part).
Table 1. Observational instrument and descriptive analysis of this study (first part).
CriterionCodeN=%χ2
Game Location. Describe where the team is playingHome19150.7χ2 = 0.066
Away18649.3p = 0.797
Team Result. Score of the match at the moment of the play.Winning11630.8χ2 = 14.966
Tying10126.8p < 0.001
Losing16042.4
Time. Moment of the match when the play occurs.Last minute23061.0χ2 = 18.273
Overtime14739.0p < 0.001
Score difference. The play begins with a score difference of 0 points, 1 point, 2 points, or 3 pointsD09926.5χ2 = 108.398
D19625.5p < 0.001
D211530.5
D36617.5
Start location. The play starts in the backcourt, with a sideline out of bounds, with a baseline out of bounds, or with the ball in play.Backcourt16844.6χ2 = 158.459
Sideline out of bounds5715.1p < 0.001
Baseline out of bounds154.0
Ball in play13736.3
Type of offenseFastbreak184.8χ2 = 308.438
Set offense35995.2p < 0.001
Type of defenseMan-to-man37098.1χ2 = 712.589
Zone31.1p < 0.001
Combine defense40.8
Passes. Number of passes used during the play.P08823.3χ2 = 60.809
P18021.2p < 0.001
P28221.8
P37118.8
P4328.5
P5+246.4
Screens. The play may involve no screens, an on-ball screen, an off-ball screen, a combination of both on-ball and off-ball screens, or a handoff.No screen17345.9χ2 = 235.613
On-ball screen10828.6p < 0.001
Off-ball screen359.3
On–off-ball screen5815.4
Handoff screen30.8
Possession. Time interval in seconds remaining to finish the possession8—022158.6χ2 = 142.159
16—912432.9p < 0.001
24—17328.5
Table 2. Observational instrument and descriptive analysis of this study (second part).
Table 2. Observational instrument and descriptive analysis of this study (second part).
CriterionCodeN%χ2
Type of finish. The play ends with…Catch-and-shoot7319.4χ2 = 135.106
Shot after an on-ball screen184.8p < 0.001
Dribble shot6717.8
Layup4110.9
Floater133.4
Offensive rebound143.7
The play does not result in a finish5915.6
With a foul9224.4
Finish outcome. The play ends with…2-point basket7319.4χ2 = 135.106
3-point basket205.3p < 0.001
Basket after an offensive rebound10.3
Foul leading to 2 free throws6717.8
A foul, 2 free throws, and a made basket82.1
Missed 2-point basket7419.6
Missed 3-point basket349.0
Missed free throws20.5
Turnover102.7
A block61.6
A steal318.2
An offensive foul102.7
A 24 s shot clock violation112.9
Another reason266.9
Ending court zone. The play ends in…Right interior zone256.6χ2 = 86.050
Central interior zone184.8p < 0.001
Left interior zone349.0
Right mid-range zone287.4
Central mid-range zone8622.8
Left mid-range zone369.5
Right perimeter zone4211.1
Central perimeter zone4411.7
Left perimeter zone4010.6
Backcourt246.4
Ending Player. The play is finished by…Guard18649.3χ2 = 59.241
Forward12733.7p < 0.001
Center6417.0
BasketYes16744.3χ2 = 4.905
No21055.7p = 0.027
Table 3. T-Pattern analysis of the 167 successful moves observed.
Table 3. T-Pattern analysis of the 167 successful moves observed.
Sequence of PatternsMaxMost Representative T-PatternO%R%T
HOME 91(((home (on-ball screen 16–9 s of possession))((shoot after on-ball screen 2-point basket)(central mid-range zone guard))) yes)33.31.8
(((home 2 passes used)(no screen catch and shoot))(3-point basket (guard yes)))33.31.8
HOMEWIN 32((home ((winning 0 passes used)(no screen foul)))((2 free throws backcourt)(guard yes)))39.41.8
HOMEWIN24–174((home ((winning no screen)(24–17 s of possession foul)))(2 free throws (guard yes)))3751.8
HOMEWIN16–910(home (winning ((no screen 16–9 s of possession)(catch and shoot yes))))3301.8
((home ((winning no screen)(16–9 s of possession foul)))(2 free throws (left perimeter zone yes)))3301.8
HOMEWIN8–018(home (winning ((2 passes used 8–0 s of possession)(layup yes))))316.71.8
HOMELOS 25((home ((losing on-ball screen)(shoot after on-ball screen 2-point basket)))(central mid-range zone (guard yes)))3121.8
((home ((losing catch and shoot)(2-point basket center))) yes)4162.4
HOMELOS16–99((home ((losing no screen)(16–9 s of possession 2-point basket)))(center yes))333.31.8
(home (losing ((1 pass used 16–9 s of possession)(2-point basket yes))))333.31.8
((home ((losing on-ball screen)(16–9 s of possession guard))) yes)333.31.8
HOMELOS8–016((((home losing)(8–0 s of possession catch and shoot))(3-point basket guard)) yes)318.81.8
((home ((losing 8–0 s of possession)(catch and shoot 2-point basket)))(center yes))318.81.8
HOMETIE 34(((home tying)(0 passes used no screen))((layup 2-point basket)(forward yes)))38.11.8
(home ((tying (on-ball screen shoot after on-ball screen))(2-point basket(guard yes))))38.11.8
HOMETIE24–176((home tying)(no screen ((24–17 s of possession 2-point basket)(forward yes))))3501.8
(home (tying ((no screen 24–17 s of possession)(left interior zone yes))))466.72.4
HOMETIE16–913((home ((tying 2 passes used)(16–9 s of possession 2-point basket)))(central mid-range zone (guard yes)))323.11.8
((home (tying 16–9 s of possession))((layup 2-point basket)(guard yes)))323.11.8
HOMETIE8–015(((home (tying 4 passes used))(8–0 s of possession central mid-range zone)) yes)3201.8
(home (tying ((off-ball screen 8–0 s of possession)(forward yes))))3201.8
AWAY 76(away (((0 passes used (no screen 16–9 s of possession))(foul (2 free throws forward))) yes))341.8
AWAYWIN 14((away winning)((no screen ((foul 2 free throws)(backcourt forward))) yes))321.41.8
AWAYWIN16–96((away ((winning no screen)(16–9 s of possession foul)))(left perimeter zone yes))3501.8
AWAYWIN8–08((away winning)(1 pass used ((8–0 s of possession foul)(2 free throws yes))))337.51.8
AWAYLOS 53(((away losing)(on-ball screen shoot after dribbling))((2-point basket left interior zone)(center yes)))35.71.8
AWAYLOS16–922(((away losing)(on-ball screen 16–9 s of possession))((2-point basket left interior zone)(center yes)))313.61.8
((away ((losing 3 passes used)(on-ball screen 16–9 s of possession)))(shoot after dribbling (2-point basket yes)))313.61.8
((away losing)(no screen ((16–9 s of possession 3-point basket)(forward yes))))418.82.4
AWAYLOS8–029((away ((losing on-ball screen)(8–0 s of possession 2-point basket)))(left interior zone (center yes)))310.31.8
(((away losing)(on-ball screen_off-ball screen 8–0 s of possession))(catch and shoot(2-point basket yes)))310.31.8
(((away losing)((8–0 s of possession 2-point basket)(central mid-range zone guard))) yes)310.31.8
AWAYTIE 9(away ((tying (layup 2-point basket))(left interior zone (guard yes))))333.31.8
AWAYTIE8–05((away ((tying no screen)(8–0 s of possession 2-point basket))) yes)3601.8
Note: Max indicates the highest possible frequency of actions following this sequence; O = observed occurrences; %R = percentage relative to the maximum number of actions detected using the defined search sequence; and %T = percentage relative to the total number of actions recorded.
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Vázquez-Estévez, C.; Prieto-Lage, I.; Reguera-López-de-la-Osa, X.; Rodríguez-Crespo, M.; Gutiérrez-Santiago, J.A.; Gutiérrez-Santiago, A. Analysis and Successful Patterns in One-Possession Games During the Last Minute in the Women’s EuroLeague. Appl. Sci. 2025, 15, 5046. https://doi.org/10.3390/app15095046

AMA Style

Vázquez-Estévez C, Prieto-Lage I, Reguera-López-de-la-Osa X, Rodríguez-Crespo M, Gutiérrez-Santiago JA, Gutiérrez-Santiago A. Analysis and Successful Patterns in One-Possession Games During the Last Minute in the Women’s EuroLeague. Applied Sciences. 2025; 15(9):5046. https://doi.org/10.3390/app15095046

Chicago/Turabian Style

Vázquez-Estévez, Christopher, Iván Prieto-Lage, Xoana Reguera-López-de-la-Osa, Manuel Rodríguez-Crespo, Jesús Antonio Gutiérrez-Santiago, and Alfonso Gutiérrez-Santiago. 2025. "Analysis and Successful Patterns in One-Possession Games During the Last Minute in the Women’s EuroLeague" Applied Sciences 15, no. 9: 5046. https://doi.org/10.3390/app15095046

APA Style

Vázquez-Estévez, C., Prieto-Lage, I., Reguera-López-de-la-Osa, X., Rodríguez-Crespo, M., Gutiérrez-Santiago, J. A., & Gutiérrez-Santiago, A. (2025). Analysis and Successful Patterns in One-Possession Games During the Last Minute in the Women’s EuroLeague. Applied Sciences, 15(9), 5046. https://doi.org/10.3390/app15095046

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