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Article

Notational Analysis of Men’s Singles Pickleball: Game Patterns and Competitive Strategies

by
Iván Prieto-Lage
1,*,
Xoana Reguera-López-de-la-Osa
2,*,
Abel Juncal-López
1,
Antonio José Silva-Pinto
1,
Juan Carlos Argibay-González
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. 2024, 14(19), 8724; https://doi.org/10.3390/app14198724
Submission received: 1 September 2024 / Revised: 24 September 2024 / Accepted: 25 September 2024 / Published: 27 September 2024
(This article belongs to the Special Issue Human Performance and Health in Sport and Exercise)
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Abstract

:
Background: Pickleball is an exponentially growing sport with a lack of notation-based studies. Consequently, this research aimed to conduct a match analysis in men’s singles to enhance the understanding of the game and optimize training practices. Methods: Using observational methodology, a total of 1145 points were analyzed from the semifinal and final rounds of five Professional Pickleball Association Tour tournaments. Data were recorded with LINCE PLUS V.2.1.0 software using the OI-PICKLEBALL-S23 observational instrument. Descriptive statistical analyses were conducted with IBM-SPSS version 25.0, and gameplay patterns were detected using Theme 6.0 Edu. Statistical significance was set at p < 0.05. Results: The data indicated that service faults at the start of the game were minimal (2.4%). The server won fewer points than the returner in the overall set of analyzed points (46.6%). Most points were concluded in short rallies (1–4 shots; 43%) or medium-length rallies (5–8 shots; 44%), with the final shot predominantly occurring from striking zone 2, the area closest to the non-volley line (50.7%). Ground strokes (55.1%) and volleys (38.4%) were the most common final shots. Conclusions: The insights gained from this study can benefit high-performance players and coaches and provide a foundation for future notation-based research in pickleball.

1. Introduction

Pickleball, a relatively new sport in the realm of racket sports, has experienced exponential growth since its creation in 1965 [1]. Conceived on Bainbridge Island, Washington, by Joel Pritchard, Bill Bell, and Barney McCallum, this sport merges elements of tennis, badminton, and table tennis. Initially designed as a recreational family activity, pickleball has significantly evolved, becoming a competitive sport with a growing global community [2]. Its popularity has particularly surged in the United States, where millions of people play it both recreationally and competitively [3]. This expansion has led the sport to other countries, consolidating its presence in various regions around the world. Its growth is partly attributed to the sport’s dynamics, including its ease of technical learning due to the dimensions of the playing area and equipment, its strong social nature, affordable access for all, less demanding technical requirements compared to other sports, as well as physical demands that can be adapted to different ages and fitness levels [4].
Despite this rapid expansion and growing global acceptance, scientific research on pickleball has been limited to date. Most of the available publications have primarily focused on aspects related to health and leisure, especially highlighting the positive impact of the sport on older adults [5]. These studies have emphasized the physical and mental benefits associated with regular pickleball practice, such as improvements in coordination, balance, agility, and psychological well-being, particularly in older individuals [1,6,7]. Topics related to injuries [8,9] and the inclusion of pickleball as an alternative sport in physical education classes [10,11] have also been addressed. However, while these findings are valuable, they have left a significant gap in terms of notational analysis and competitive performance optimization, which are critical areas for the development of advanced training and gameplay strategies.
In contrast, sports like tennis and padel have been the subject of a wide range of notational studies that have allowed for a deep understanding of their game dynamics [12,13,14,15,16,17]. These studies have provided key data on service effectiveness, rally length, finishing zones, and other critical aspects of the game, which have enabled coaches and players to continuously improve their strategic approaches [18,19]. Even other racket sports, such as table tennis and badminton, though to a lesser extent, have been the subject of detailed analyses that have contributed to the evolution of their respective training and game tactics [20,21,22,23]. In the case of tennis, for example, it has been extensively documented how court surface influences rally dynamics and service success probability, guiding the development of specific strategies for each type of court [24,25].
Despite the similarities pickleball shares with these sports, the lack of exhaustive notational analysis has so far limited the understanding of its specific characteristics and the ability to systematically enhance performance. This highlights the need for research focused on unraveling the specific dynamics of pickleball, both in singles and doubles, to establish training strategies that optimize performance in competitive contexts.
This study aims to fill this research gap by conducting a detailed notational analysis of this sport, with the objective of generating precise and relevant data to guide high-performance training. Through this investigation, the goal is to establish a solid knowledge base on key game variables, such as rally length, common ending points, and shot directions that lead to winners or unforced errors. Additionally, the most characteristic playing patterns for winning points are identified, distinguishing between the server and the returner. This will enable a better understanding of game dynamics and help optimize training strategies. These data will provide a framework for continuous improvement in player preparation, something that has been challenging in the field of pickleball due to the scarcity of research.
Therefore, this study not only seeks to fill this knowledge gap but also aims to lay the groundwork for future research that continues to develop the competitive potential of pickleball. For all these reasons, the objective of this research is to analyze singles play in men’s pickleball, identifying patterns of effectiveness based on rally type and the role of the server/returner. This study aims to offer a deeper understanding of the game, provide useful insights for optimizing training, and set a precedent for future investigations.

2. Methods

2.1. Design

This observational study aims to analyze the structure of play in men’s singles pickleball. To achieve this, the observational methodology was utilized [26].
The observational design [27] employed is nomothetic, as it encompasses all points contested in the semifinals and finals during the 2023 season across five tournaments of the Professional Pickleball Association (PPA) Pro Tour of Pickleball (https://www.ppatour.com/ (accessed on 15 September 2023)) Additionally, the design is longitudinal (covering an entire season) and unidimensional (as the analysis does not account for the concurrency of behaviors).

2.2. Sample

The unit of analysis in this study was the points played in the observed pickleball matches. Specifically, five individual tournaments were analyzed (Las Vegas, Cincinnati, Kansas, Seattle, and Denver), resulting in the observation of 15 matches and a total sample of 1145 points. The selected tournaments represent the highest competitive level in this sport, bringing together only professional players from various countries around the world. The participants in this study were the players who reached at least the semifinal round in one of the five analyzed pickleball tournaments. Since this is an observational study conducted in a natural setting, using publicly available videos and not involving any form of experimentation, informed consent from the competitors was not required [28]. The study was approved by the Ethics Committee of the Faculty of Education and Sport Sciences (University of Vigo, application 07-280722).

2.3. Instruments

The observation instrument used was the OI-PICKLEBALL-S23 (observational instrument for analyzing pickleball during the 2023 season), a system of categories designed ad hoc to consider the various playing possibilities in pickleball. This instrument is based on tools previously developed for similar objectives in the field of tennis [18,19]. After designing and testing the observation instrument, its construct validity was assessed through its alignment with the theoretical framework [29] and through consultation with three experts in racket and/or paddle sports and observational methodology. The experts showed an agreement level with the instrument exceeding 95%.
The OI-PICKLEBALL-S23 consists of seven criteria that form a system of categories (see Table 1 and Figure 1) which meets the conditions of exhaustiveness and mutual exclusivity. Data recording was conducted using LINCE PLUS software, version 2.1.0 [30].

2.4. Procedure

Data collection was carried out by searching for, downloading, and viewing all the semifinals and finals of the five tournaments selected for the study. The videos, recorded in 1080p resolution (1920 × 1080), were analyzed on 27-inch monitors. Before conducting the data quality tests, which were performed by two experts in pickleball and observational methodology, specific training on the use of the observation instrument was provided. This training involved familiarization with the observation instrument and the LINCE PLUS recording software, version 2.1.0, through nine two-hour sessions over three weeks, using videos of men’s pickleball matches from the 2022 season. The two expert observers are university professors with experience teaching in a research master’s program, where they deliver a module on observational methodology in sports science. Both have numerous scientific publications on racket and paddle sports. Additionally, one of them is certified as a pickleball coach.
To ensure rigor in the data recording process [31], data quality was monitored by calculating intra-observer and inter-observer agreement using the Kappa coefficient [32], with the LINCE PLUS software, version 2.1.0. Both agreements were calculated on points that were not part of the final sample (n = 200; 1/10 of the final sample). The intra-observer Kappa was 0.96 for the first observer and 0.99 for the second, while the inter-observer Kappa was 0.98 (see Table 2). After the data quality tests, the second observer analyzed all the points in the study sample. Once all points were recorded, an Excel file was generated with the sequence of actions that occurred in each analyzed point. The versatility of this Excel file allowed for automatic transfer of the information to a file compatible with IBM-SPSS version 25 and THEME version 6 Edu, the software used for the various statistical analyses in the study.

2.5. Data Analysis

All descriptive statistical analyses were conducted using the Statistical Package for the Social Sciences version 25.0 (IBM-SPSS Inc., Chicago, IL, USA). Statistical significance was assumed for p < 0.05.
A descriptive analysis of the study variables was performed. The χ2 test was used to assess differences within the categories of each employed criterion (χ² goodness-of-fit test). Additionally, this same test was applied, using crosstabs, to identify differences between the point-winning criterion and the method of winning the point with respect to the other analyzed variables (χ2 test of independence). Furthermore, the effect size was calculated using Cramér’s V to assess the strength of the associations observed, with the following interpretation: 0.00–0.10: very weak, 0.10–0.20: weak, 0.20–0.30: moderate, 0.30–0.40: relatively strong, 0.40–0.50: strong, and 0.50 or more: very strong. An analysis of adjusted residuals was also conducted to highlight significant deviations from the expected frequencies, providing further insight into the relationships between variables.
To identify playing patterns in pickleball, we utilized THEME 6 Edu software [33], a specialized statistical analysis tool designed for detecting temporal patterns in sequential data. Widely used in fields such as psychology, ethology, and sports analysis, THEME excels in identifying T-patterns—recurring temporal and/or sequential patterns that may not be immediately apparent. Its ability to analyze large data sets and detect patterns that do not follow rigid sequences makes it particularly valuable for studying complex or dynamic behaviors. T-pattern detection identifies recurrent patterns of behavioral events within a temporal sequence, based on statistical probabilities [34]. While THEME’s primary strength lies in detecting temporal patterns, it also facilitates the identification of sequential structures through its order parameter function, adding depth to the analysis of behavioral and tactical dynamics. The following search criteria were applied: (a) the presence of at least three T-patterns in the observed sequence set; (b) a 90% redundancy reduction adjustment for similar T-pattern occurrences; and (c) a significance level of 0.005.

3. Results

Descriptive Analysis

Table 3 presents a descriptive analysis of the study, including the χ² goodness-of-fit test.
Significant statistical differences were found in the χ2 test for each of the criteria analyzed.
In general terms, the start of the point predominantly occurred without a service fault (nearly 98%) and was followed by a medium-length rally (44%), although short rallies were also common (43%). It was more common for the returner to win the point (53%). Points mostly ended due to an unforced error (58%), with the server making the error more frequently (32.9%). In more than half of the points played (51%), the final shot was executed from striking zone 2. More than half of the final shots were hit out or into the net (58%). Winners were typically directed to finishing zone 4 (11%) or finishing zone 6 (9%). The most common final shot was a forehand (34%), followed by a backhand (22%), although numerous volleys were also observed (18% forehand and 21% backhand).
On the other hand, significant differences (χ2 = 38.200; p = 0.000) were observed when comparing the data concerning rally length based on the point winner (whether the server or the receiver wins the point). The effect size test indicated that the relationship was weak (V = 0.183). As shown in Figure 2a, the analysis of adjusted residuals reveals distinct patterns in point winning across different rally lengths. In short rallies (SH), servers won significantly more points than expected, with an adjusted residual of 6.2, indicating that this type of rally favors the server. Conversely, in medium rallies (MD), receivers gained more points than expected, reflected by an adjusted residual of 4.9, highlighting a significant advantage for them. In long rallies (LN), although receivers also won more points than anticipated, with an adjusted residual of 1.9, these differences did not reach statistical significance.
When crossing the variable point ending with rally length (Figure 2b), statistically significant differences are again observed (χ2 = 106.774; p = 0.000). The effect size test indicated that the relationship was moderate (V = 0.216). In short rallies (SH), it was observed that servers predominantly won due to unforced errors by the opponent (SWUE), with an adjusted residual of 8.8. Overall, unforced errors (SWUE and RWUE) were more common than winners (SWW and RWW). In medium rallies (MD), the receiver won a greater number of points, primarily due to unforced errors (RWUE, with an adjusted residual of 0.8), but especially through winners (RWW), which had an adjusted residual of 5.6, showing a notable increase compared to short rallies. It is noteworthy that the server’s unforced error (SWUE), which was the most recorded value in short rallies, became the least frequent in medium rallies, while the receiver’s winners (RWW) increased. In long rallies (LN), a trend similar to that of medium rallies was maintained, though with a decrease in the number of recorded points.
When analyzing the data between winner/point ending and striking zones (Figure 2c,d), statistically significant differences were observed (χ2 = 31.696; p = 0.000 and χ2 = 437.636; p = 0.000, respectively). The effect size test indicated that the relationship was weak (V = 0.166) in the first case (none of the adjusted residuals exceeded the critical value of ±1.96) and relatively strong in the second (V = 0.357). Points won from striking zone 2, both on serve and return, were particularly noteworthy. From this mid-court zone, returners frequently won points with winners, while servers often benefitted from their opponents’ unforced errors. In striking zones 3 (baseline area) and 4 (beyond the baseline), most points won by the returner resulted from unforced errors, whereas the server achieved numerous winners.
The analysis of the adjusted residuals reveals that striking zone 2 (SZ2) is crucial in scoring points during the match, particularly highlighting the winners from the receiver, which presented an adjusted residual of 14.8, as well as the errors from the receiver, with a residual of 4.9. This suggests that the receiver has a notable advantage in controlling the game in this zone. In striking zone 3 (SZ3), the server’s errors were significantly higher than expected, with an adjusted residual of 8.4, while the winners from the receiver were less frequent, showing a residual of −7.4. This indicates that, although SZ3 favors the server, this advantage is primarily due to errors from the receiver rather than winning shots (6.0). On the other hand, in striking zone 4 (SZ4), both the server’s errors (residual of 5.8) and the receiver’s errors (residual of 3.0) exceeded expectations, while the winners from the receiver were significantly lower than expected, with an adjusted residual of −9.0.
Table 4 presents an analysis of play patterns that illustrates how points were concluded, taking into account the type of rally, the zone from which the final shot was executed, and the point ending.
The analysis reveals that, in short rallies, points were predominantly resolved with shots from striking zone 2 or striking zone 4. From striking zone 2, where the receiver was positioned in most cases, 67.1% of the points resulted in unforced errors and 33.7% resulted in winners by that player. When the final shot came from striking zone 4, unforced errors were predominant for both the server and the receiver. In cases where a winner was recorded, it generally belonged to the server.
In medium-length rallies, the final shot was most frequently executed from striking zone 2 (67%). The majority of points were won by the receiver (57.4%), often through winners (40.7%, representing 71% of the points won). The server also achieved a significant number of winners (21.7%, representing 51% of the points won). From striking zone 4, points were predominantly won by the receiver due to unforced errors from the opponent (75.9%).
Table 5 shows the T-pattern analysis carried out for this research. The data include only first-serve points, excluding those with service faults, and is organized by rally length and the player who wins the point.
To facilitate the understanding of a T-pattern, let us take the fifth pattern presented in the Table 5 as an example: (FS (SH SW)) (SWW FH)). This T-pattern indicates that, after executing a successful first serve (FS), the server tends to win the point quickly (SW) in short rallies (SH), finishing with a winner (SWW) through a forehand shot (FH). This pattern, which spans 5 categories, occurred 80 times, representing 28.2% of the points won in short rallies by the server, which accounts for 17% of all short rally points and 7% of all points played. The recurrence of this pattern suggests that the server is very effective in short-duration points and typically finishes them offensively with their forehand.
In short rallies, which accounted for 42% of the total points recorded, the server won a significantly higher percentage of points compared to the returner (60.4% versus 39.6%). Most of these points were finished from striking zone 4, representing nearly half of the cases, with a clear predominance of forehand shots (38.1%), often followed by an unforced error (14% from the returner and 13% from the server). Additionally, a considerable number of points concluded with a shot from striking zone 2 (29.8%).
Up to 14% of all points in this type of rally were won by the server due to an unforced error from the returner following a forehand shot from striking zone 4, representing 23.6% of the points won by the server. Nearly 70% of the points won by the server resulted from unforced errors by the returner, with most of the winners being achieved through a forehand shot.
On the other hand, approximately 5% of all points in this type of rally were decided by a forehand volley winner from the returner, accounting for 33% of the points won by this player. Additionally, more than half of the points won by the returner (52.1%) were the result of unforced errors from the opponent via a forehand shot.
In medium-length rallies, which accounted for 45.1% of the points analyzed, the returner won 61.5% of these points. Notably, 65.6% of these rallies ended with a shot from striking zone 2, while nearly 20.8% concluded with a shot from striking zone 3. A forehand volley was the decisive shot in 26.5% of the points, representing 40.5% of all points that concluded from striking zone 2. The number of points concluded with a forehand and backhand in this type of rally were relatively similar, accounting for 24.4% and 26.5% of the points, respectively.
Among the points won by the server, 58.2% were achieved through winners, which comprised 22.4% of the total points. In contrast, the returner primarily won points due to unforced errors, accounting for 66.5% of their points won and 33.5% of the total points. The majority of the server’s winners were executed from striking zone 2 (35.5%), with finishing zone 4 being a common area for finishing shots. The most frequent winning pattern for the server involved a shot from striking zone 2 directed toward finishing zone 4, often concluding with a forehand volley, which represented 6.2% of all points won by the server.
For the returner, it is notable that 43.5% of their points were won through winners from striking zone 2, with 14.2% of these points concluding with a forehand volley. These points were hit toward finishing zones 3, 4, and 6 in similar proportions. Points won due to unforced errors were predominantly preceded by an opponent’s shot from striking zone 3 (20.3% of all points won), often involving a backhand stroke.
Long rallies were the least frequent, accounting for only 13% of the analyzed points. Of these, 60.7% were won by the returning player. Approximately half of the points ended with a volley, with 23.4% being forehand volleys and 24.8% backhand volleys. The server won 64.9% of their points through winners, most of which were generated from striking zone 2. Regarding the returning player, the points they won were almost equally divided between unforced errors from the opponent and their own winners. Striking zone 2 stood out as the main zone from which to hit winners, with volleys predominating as the final shot. The majority of winners were directed toward finishing zone 3.

4. Discussion

The objective of this study was to conduct a notational analysis of pickleball to generate accurate and relevant data that can contribute to optimizing high-performance training in this sport. The research provided significant findings on various key variables, such as rally duration, the most common final shot zones, and ball trajectories associated with winners or forced errors. Additionally, detailed statistics were gathered on the probability of winning a point, distinguishing between the server and the receiver.
Previous research in other racquet and paddle sports has demonstrated that the serve is a fundamental component of the game. When comparing pickleball to sports such as tennis or padel, it has been observed that the ball is put into play with the first serve at a high percentage, close to 97%, far exceeding the values recorded in tennis [35,36] and more similar to those in padel [14,17]. This percentage is more akin to sports like badminton, where, as in pickleball, there is no option for a second serve. However, it has been found that the serve in pickleball is significantly less effective compared to other racquet and paddle sports [17,37], particularly in relation to tennis [18]. In fact, among the more than 1100 points observed, only one ace was recorded. Despite the fact that players serve underhand in this sport, training with various spins and trajectories could enhance the chances of winning points. This variability in serves could be an area to optimize during practice, as it would allow players to develop more effective strategies and better adapt to different game situations.
Regarding rally length, the study data showed that the proportion of points ending in short rallies (between one and four shots) and medium rallies (between five and eight shots) was quite similar, at approximately 43% in both cases. Long rallies, those with nine or more shots, were relatively infrequent. When compared to other sports, it could be said that the structure of play is more similar to padel, where rallies of fewer than 12 shots (including short, medium, and long rallies in this study) are common and occur at a similar rate [17,38,39], in contrast to tennis, where short rallies clearly predominate, especially on fast surfaces such as hard courts and grass [18,35,40,41]. Given the rarity of long rallies in men’s singles pickleball, players are encouraged to quickly adapt to situations and play proactively, fostering a more offensive and aggressive style.
In terms of court striking zones from where the final shot is executed, pickleball showed greater similarity to padel than to tennis. Previous research in padel has revealed that up to 40% of final shots are made from the middle zone of the court, with similar percentages from the zone near the net [17,42]. In contrast, in tennis, final shots from the baseline dominate across all surfaces, highlighting a significant difference in point dynamics between these sports [18]. These data suggest that, in this sport, as in padel, mastering both the net and forehand and backhand volleys is crucial [43]. This contrasts with tennis, where these shots are less frequent due to the predominance of baseline rallies through groundstrokes [18,44]. It is important to note that in pickleball there is a zone close to the net that, for regulatory reasons, cannot be used as a zone from which the final shot is executed. This reinforces the idea that controlling the area near the net is fundamental, similar to padel. A clear difference between padel and pickleball in this respect is that in the latter, the smash is hardly used, whereas in padel, it is a decisive stroke [17,42]. In any case, the high percentage of unforced errors committed by the receiver near the net during short rallies suggests that the tactic of advancing toward the net may not be as effective as previously considered. This aspect requires further exhaustive analysis in future research.
According to the data obtained from the study, the receiving player won slightly more than half of the points played (54%). This finding contrasts markedly with padel and, especially, with tennis, where the serve tends to clearly favor the server. Previous studies have shown that, in padel, the probability of winning a point on serve is approximately 60% [17,45], while in tennis, it ranges between 65% and 70%, depending on the surface [18,46]. The rule in pickleball that requires the ball to bounce before being returned on the first shot by the server after the serve could explain this difference. Therefore, coaches have a significant opportunity to improve performance by focusing on optimizing the serve and the first return following the serve, to increase the chances of winning the point.
Regarding point endings, it was observed that nearly 60% of the points ended with an unforced error, predominantly committed by the server. These values are more similar to those in tennis [18,47] than in padel, where recent research indicates that approximately 40% of points are decided by unforced errors [17]. Although in tennis the data are also close to the findings of this study, in that sport, unforced errors are more common from the receiving player.
Regarding the stroke used to finish points in this study, it was observed that forehands and backhands predominated, along with a significant number of volleys, both forehand and backhand. These characteristics show clear similarities to padel, where, in addition to these shots, finishes with smashes are also common [48]. However, in pickleball, the smash was less frequent. In contrast, tennis presents a different profile, with a lower proportion of volleys as final shots, since points in this sport are typically resolved with shots from the baseline. Thus, while pickleball and padel share a more dynamic and offensive approach to point endings, tennis is characterized by baseline play, with a predominance of forehand and backhand strokes. The data reveal that in men’s singles pickleball, simply mastering groundstrokes from the back of the court is not enough to win matches, nor is being skilled only at volleying near the net. Both areas of play are essential, and success requires a well-rounded game that integrates proficiency from both positions.
The sequential analysis of the study has revealed patterns of play and point-winning chances that a descriptive analysis could have overlooked or misinterpreted. This type of analysis, recently implemented in other racquet sports [13,17], has proven useful. For example, although the receiver generally wins more points overall, this trend does not hold in short rallies, where the server wins a greater proportion of points. This indicates that the serve can be a decisive factor in point development, especially when resolved in the first few shots. Although it may seem that the receiver has the initiative in the point, similar to the dynamic in volleyball, this theory is not entirely accurate, since if the server manages to shorten the rally, the possibility of success tilts in their favor. As expected, certain zones of the court are more favorable for winning points, but the study reveals that it is crucial to combine this information with the type of rally to obtain precise performance data. This integrated approach allows coaches to optimize match tactics, adapting the game not only based on the striking zone but also according to the dynamics of the rallies.

4.1. Practical Implications

The data presented in the research provide a more detailed understanding of the internal dynamics of the game and are crucial for designing specific tasks to optimize training and improve performance. For instance, although it is believed that being close to the net facilitates scoring points in this sport, it has been observed that the returner frequently makes errors in the area near the no-volley zone during short rallies. This suggests that coaches should focus on improving this aspect of the game.
While servers win more points in short rallies, the difference is relatively small. This indicates that there is still room for improvement in this area, as it has not been conclusively demonstrated that serving significantly benefits the returner.
Focusing on striking zones 2 and 4, which are critical for finishing points, is also essential. Since a significant number of points are resolved with shots from near the no-volley zone, it is important for players to practice precise shot execution in these areas. Additionally, considering that striking zone 4 is crucial for servers, training should include exercises to explore how to capitalize on these zones for both offensive and defensive plays.
The analysis highlights the importance of both forehand and backhand volleys and groundstrokes. Therefore, players should engage in exercises that emphasize these shots equally, ensuring they can execute them effectively in various situations, especially during short and medium-length rallies.
Moreover, given the significant role of unforced errors in determining point outcomes, training should include practices aimed at reducing these errors, particularly in striking zone 2. Players need to work on maintaining control and composure in this critical area to minimize the likelihood of making unforced errors.
Finally, it is important to tailor training sessions to the duration of rallies. For short rallies, the focus should be on aggressive play and quick strategies to finish points. In medium and long rallies, the emphasis should shift to consistency, patience, and strategic shot placement to outmaneuver the opponent.

4.2. Limitations and Future Perspectives

For this study, data from the semifinals and finals of five tournaments from the 2023 professional pickleball circuit were collected. Despite the number of points recorded and analyzed, the results obtained might have varied if points from earlier rounds or additional tournaments had been included. Competitive stress associated with final rounds or even physical fatigue may have influenced the resulting data.
In this study, player rankings, the state of the scoreboard, and player handedness were not considered. These factors could be examined in future research.
For future studies, it is suggested to conduct a temporal analysis of the effort-pause time between points and to compare results with the female category or different doubles categories. Given that pickleball is an expanding sport, it would be beneficial to compare these findings with future seasons to observe the evolution of the game and the changes that may occur.

5. Conclusions

The research provides significant findings regarding successful patterns of rallies/play in pickleball. Although this sport shares some similarities with padel and tennis, it has distinctive characteristics that require specific and differentiated training compared to other racket and paddle sports. In general, most rallies in men’s singles end within eight shots. Returners win more points than servers, although in shorter rallies the advantage tends to favor the server. Most points conclude near the no-volley zone, indicating that players actively seek to approach the net due to the competitive advantage this position offers. The most frequently used finishing shots are the forehand and backhand, but when the point ends near the net, forehand and backhand volleys prevail, while the use of smashes and drop shots is minimal.
These findings can contribute to a better understanding of the game, helping athletes improve their decision-making during competition. Additionally, they provide valuable information for designing specific drills and training sessions, especially considering the lack of previous studies on this sport.

Author Contributions

Conceptualization, I.P.-L., A.J.S.-P., A.J.-L., X.R.-L.-d.-l.-O. and A.G.-S.; methodology, I.P.-L., J.C.A.-G., X.R.-L.-d.-l.-O. and A.G.-S.; software, I.P.-L., A.J.-L. and A.G.-S.; validation, J.C.A.-G., A.J.-L. and X.R.-L.-d.-l.-O.; formal analysis, I.P.-L. and A.G.-S.; investigation, I.P.-L., J.C.A.-G., A.J.S.-P., A.J.-L., X.R.-L.-d.-l.-O. and A.G.-S.; resources, I.P.-L., A.J.S.-P., J.C.A.-G., A.J.-L. and X.R.-L.-d.-l.-O.; data curation, I.P.-L., A.J.S.-P., J.C.A.-G. and A.G.-S.; writing—original draft, I.P.-L., A.J.-L., A.J.S.-P. and A.G.-S.; writing—review and editing, I.P.-L., X.R.-L.-d.-l.-O. and A.G.-S.; visualization, A.J.S.-P., J.C.A.-G. and A.J.-L.; supervision, I.P.-L., A.J.S.-P. and A.G.-S.; project administration, I.P.-L., J.C.A.-G., X.R.-L.-d.-l.-O. and A.G.-S.; funding acquisition, 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 (accessed on 20 June 2024)), 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.

Institutional Review Board Statement

The study was approved by the ethics committee of the Faculty of Education and Sport Science (University of Vigo, application 08-280722).

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

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 14 08724 g001
Figure 1. Court striking zones and finishing zones.
Figure 1. Court striking zones and finishing zones.
Applsci 14 08724 g001
Applsci 14 08724 g002
Figure 2. Relationship between research criteria (winner and service (a); point ending and rally length (b); winner and strike zone (c); point ending and strike zone (d). Note: SH: short rally, MD: medium rally, LN: long rally; SW: server wins, RW: receiver wins; SWW: server wins with a winner or a forced error, SWUE: server wins with an unforced error by the opponent, RWW: receiver wins with a winner or a forced error, RWUE: receiver wins with an unforced error by the opponent; SZ1: non-volley zone, SZ2: mid-court striking zone; SZ3: back court striking zone, including the baseline, SZ4: deep court striking zone, behind the baseline, SZ: service zone.
Figure 2. Relationship between research criteria (winner and service (a); point ending and rally length (b); winner and strike zone (c); point ending and strike zone (d). Note: SH: short rally, MD: medium rally, LN: long rally; SW: server wins, RW: receiver wins; SWW: server wins with a winner or a forced error, SWUE: server wins with an unforced error by the opponent, RWW: receiver wins with a winner or a forced error, RWUE: receiver wins with an unforced error by the opponent; SZ1: non-volley zone, SZ2: mid-court striking zone; SZ3: back court striking zone, including the baseline, SZ4: deep court striking zone, behind the baseline, SZ: service zone.
Applsci 14 08724 g002
Table 1. Observational instrument OI-PICKLEBALL-S23.
Table 1. Observational instrument OI-PICKLEBALL-S23.
CriteriaCodeDescription
ServiceFSFirst service
SFService fault
Rally length (the serve stroke is counted)SHShort rally (1–4 shots).
MDMedium rally (5–8 shots).
LNLong rally (9+ shots).
Strike zone
(see Figure 1)		SZ1Non-volley zone
SZ2Mid-court zone
SZ3Back court zone, including the baseline
SZ4Deep court zone, behind the baseline
SZService zone
Finish zone
(see Figure 1)		FZ1Left front zone
FZ2Right front zone
FZ3Left mid-court zone
FZ4Right mid-court zone
FZ5Left back court zone
FZ6Right back court zone
NTNet shot
OUTShot out
WinnerSWThe point is won by the server
RWThe point is won by the returner
Point endingSWWServer wins with a winner or a forced error
SWUEServer wins with an unforced error by the opponent
RWWReceiver wins with a winner or a forced error
RWUEReceiver wins with an unforced error by the opponent
Final strokeACEDirect serve
FHForehand
BHBackhand
FHVForehand volley
BHVBackhand volley
SMSmash
LBLob
DSDrop shot
SCChange of service due to an error in the service
OTOther type of stroke
Table 2. Degree of reliability of the study.
Table 2. Degree of reliability of the study.
CriteriaIntra-Kappa
Obs1-Obs1		Intra-Kappa
Obs2-Obs2		Inter-Kappa
Obs1-Obs2
Service111
Rally length0.980.990.98
Strike zone0.960.970.96
Finish zone0.970.990.98
Winner0.990.990.97
Point ending0.970.970.96
Final stroke0.960.990.96
Mean reliability0.980.990.97
Table 3. Descriptive analysis of the investigation.
Table 3. Descriptive analysis of the investigation.
CriteriaCoden%χ2 TestCriteriaCoden%χ2 Test
ServiceFS111897.6χ2 = 1039.547WinnerRW61153.4χ2 = 5.178
SF272.4p < 0.001SW53446.6p < 0.023
Rally lengthLN14512.7χ2 = 220.215Point endingRWUE37732.9χ2 = 44.921
MD50444.0p < 0.001RWW23420.4p < 0.001
SH49643.3 SWUE29125.4
Strike zoneSZ1121.0χ2 = 944.716SWW24321.2
SZ258150.7p < 0.001Final strokeACE10.1χ2 = 1608.860
SZ321819.0 BH24821.7p < 0.001
SZ430426.6 BHV20317.7
SZ302.6 DS70.6
Finish zoneNT31627.6χ2 = 739.583FH39434.4
OUT34830.4p < 0.001FHV23720.7
FZ1272.4 LB60.5
FZ2201.7 OT40.3
FZ31099.5 SC292.5
FZ412611.0 SM161.4
FZ5938.1
FZ61069.3
Table 4. Descriptive analysis of play patterns.
Table 4. Descriptive analysis of play patterns.
Play Pattern SZ2n%Play Pattern SZ3n%Play Pattern SZ4n%
SH464100
SH-SZ214030.2SH-SZ39620.7SH-SZ422849.1
SH-SZ2-SW9668.6SH-SZ3-SW4647.9SH-SZ4-SW14061.4
SH-SZ2-SW-SWW21.4SH-SZ3-SW-SWW3940.6SH-SZ4-SW-SWW5122.4
SH-SZ2-SW-SWUE9467.1SH-SZ3-SW-SWUE77.3SH-SZ4-SW-SWUE8939
SH-SZ2-RW4431.4SH-SZ3-RW5254.2SH-SZ4-RW8838.6
SH-SZ2-RW-RWW4330.7SH-SZ3-RW-RWW00SH-SZ4-RW-RWW73.1
SH-SZ2-RW-RWUE10.7SH-SZ3-RW-RWUE5254.2SH-SZ4-RW-RWUE8135.5
MD494100
MD-SZ233167MD-SZ310521MD-SZ45812
MD-SZ2-SW14142.6MD-SZ3-SW3836.2MD-SZ4-SW1322.4
MD-SZ2-SW-SWW6920.8MD-SZ3-SW-SWW3634.3MD-SZ4-SW-SWW712.1
MD-SZ2-SW-SWUE7221.7MD-SZ3-SW-SWUE21.9MD-SZ4-SW-SWUE610.3
MD-SZ2-RW19057.4MD-SZ3-RW6763.8MD-SZ4-RW4577.6
MD-SZ2-RW-RWW13540.7MD-SZ3-RW-RWW43.8MD-SZ4-RW-RWW11.7
MD-SZ2-RW-RWUE5516.6MD-SZ3-RW-RWUE6360MD-SZ4-RW-RWUE4475.9
Note: In this analysis, shots ending from striking zone 1 and long rallies have been excluded due to their low frequency, in order to simplify the data analysis. Note 2: SH: short rally, MD: medium rally; SW: server wins, RW: receiver wins; SWW: server wins with a winner or a forced error, SWUE: server wins with an unforced error by the opponent, RWW: receiver wins with a winner or a forced error, RWUE: receiver wins with an unforced error by the opponent; SZ2: mid-court striking zone; SZ3: back court striking zone, including the baseline, SZ4: deep court striking zone, behind the baseline.
Table 5. T-pattern analysis.
Table 5. T-pattern analysis.
SearchMax. T-PatternLO%
FS1118100%
FS-SH 469 42%(FS (SH SZ4))322848.6
(FS (SH SZ2))314029.8
((FS SH) (SZ4 FH))417938.1
(((FS SH) (SZ2 SW)) SWUE)59420
FS-SH-SW28360.4%((FS (SH SW)) (SWW FH))58028.2
((FS (SH NT)) (SW SWUE))58530
((FS (SH OUT)) (SW SWUE))510537.1
((((FS SH) (SZ2 SW)) SWUE) BHV)64917.3
(FS ((SH SZ4) (SW (SWUE FH))))66723.6
((FS SH) ((SZ4 OUT) (SW (SWUE FH))))74415.5
FS-SH-RW18639.6%((FS (SH RW)) (RWUE FH))59752.1
(FS ((SH NT) (RW (RWUE FH))))65127.4
(FS ((SH OUT) (RW (RWUE FH))))64624.7
(FS ((SH SZ4) (RW (RWUE FH))))66132.7
((((FS SH) (SZ2 RW)) RWW) FHV)62312.4
((FS SH) ((SZ4 NT) (RW (RWUE FH))))73418.2
FS-MD50445.1%((FS MD) (SZ2 FHV))413426.5
(FS (MD SZ2))333165.6
(FS (MD SZ3))310520.8
(FS (MD BH))313025.8
(FS (MD FH))312324.4
FS-MD-SW19438.5%(FS (MD (SW SWW)))411358.2
((FS MD) (SZ2 SW))414127.9
((FS MD) (SZ2 (SW SWW)))56935.5
((FS MD) (SZ3 (SW SWW)))53618.5
((FS MD) (FZ4 (SW SWW)))53819.6
((FS MD) (SZ2 (SW SWUE)))57237.1
(((FS MD) (SZ2 FZ4)) (SW SWW))62613.4
(FS ((MD SZ2) (SW (SWW FHV))))63115.9
((FS MD) ((SZ2 FZ4) (SW (SWW FHV))))7126.2
FS-MD-RW31061.5%(FS (MD (RW RWUE)))416954.5
((FS MD) (SZ2 RW))419061.3
((FS MD) (SZ2 (RW RWW)))513543.5
(FS ((MD SZ2) (RW (RWW FHV))))67223.2
(((FS MD) (SZ2 FZ4)) (RW RWW))63812.2
(((FS MD) (SZ2 FZ3)) (RW RWW))63110
(((FS MD) (SZ2 FZ6)) (RW RWW))63110
((FS MD) (SZ3 (RW RWUE)))56320.3
((FS (MD RW)) (RWUE BH))57524.1
((FS (MD RW)) (RWUE FH))56119.7
FS-LN14513%(FS (LN SZ2))311075.9
(FS (LN (SZ2 BHV)))43624.8
(FS (LN (SZ2 FHV)))43423.4
FS-LN-SW5739.3%((FS LN) (SZ2 SW))44985.9
(FS (LN (SW SWW)))43764.9
((FS LN) (SZ2 (SW SWW)))53154.3
(((FS LN) (SZ2 SW)) (SWW FHV))61424.5
FS-LN-RW8860.7%(FS (LN (RW RWUE)))44551.1
((FS LN) (SZ2 RW))46169.3
((FS LN) (SZ2 (RW RWW)))54228.9
((FS LN) (SZ2 (RW RWW)))54247.7
(((FS LN) (SZ2 FZ3)) (RW RWW))61415.9
(((FS LN) (SZ2 RW)) (RWW BHV))61618.1
(((FS LN) (SZ2 RW)) (RWW FHV))61314.7
Note: Max refers to the maximum possible frequency of points with this sequence; L indicates the pattern length; O represents the occurrence of that sequence. Note 2: FS: first service; SH: short rally, MD: medium rally, LN: long rally; SW: server wins, RW: receiver wins; SWW: server wins with a winner or a forced error, SWUE: server wins with an unforced error by the opponent, RWW: receiver wins with a winner or a forced error, RWUE: receiver wins with an unforced error by the opponent; SZ2: mid-court striking zone; SZ3: back court striking zone, including the baseline, SZ4: deep court striking zone, behind the baseline; FZ3: finishing zone left mid-court zone, FZ4: finishing zone right mid-court zone, FZ6: finishing zone right back court zone; FH: forehand, BH: backhand, FHV: forehand volley, BHV: backhand volley; OUT: shot out, NT: net shot.
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Prieto-Lage, I.; Reguera-López-de-la-Osa, X.; Juncal-López, A.; Silva-Pinto, A.J.; Argibay-González, J.C.; Gutiérrez-Santiago, A. Notational Analysis of Men’s Singles Pickleball: Game Patterns and Competitive Strategies. Appl. Sci. 2024, 14, 8724. https://doi.org/10.3390/app14198724

AMA Style

Prieto-Lage I, Reguera-López-de-la-Osa X, Juncal-López A, Silva-Pinto AJ, Argibay-González JC, Gutiérrez-Santiago A. Notational Analysis of Men’s Singles Pickleball: Game Patterns and Competitive Strategies. Applied Sciences. 2024; 14(19):8724. https://doi.org/10.3390/app14198724

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Prieto-Lage, Iván, Xoana Reguera-López-de-la-Osa, Abel Juncal-López, Antonio José Silva-Pinto, Juan Carlos Argibay-González, and Alfonso Gutiérrez-Santiago. 2024. "Notational Analysis of Men’s Singles Pickleball: Game Patterns and Competitive Strategies" Applied Sciences 14, no. 19: 8724. https://doi.org/10.3390/app14198724

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