Inside the Playbook: Tactical Signatures of Winning Teams in the NBA

Abstract

Basketball preparation has shifted from a physical and technical focus to a holistic approach that incorporates performance analysis, as traditional statistics offer only a limited understanding of team behavior. This study aimed to characterize NBA teams according to their performance in regular season and play-off games and to identify the play types that distinguish the best-performing teams in each phase. Data from five NBA seasons (2019–2024; 6400 games) were analyzed using play-type statistics obtained from the official league database. Two-step cluster analysis and one-way analyses of variance with Bonferroni correction were applied to identify group differences (p < 0.05). Three team clusters were identified in both the regular season and the play-offs. High-performing teams in the regular season were significantly more effective in isolation (p < 0.01) and spot-up (p = 0.03) situations and showed greater use of pick-and-roll ball-handler actions (p = 0.001). In the play-offs, differences were smaller and mainly involved low-performing teams, which were less effective in transition and spot-up plays (p < 0.05). Comparisons between the best regular season and play-off teams revealed significant differences in post-up, off-screen, and put-back efficiency (p < 0.05). Success depended primarily on execution efficiency rather than play-type frequency.

1. Introduction

Traditionally, basketball teams focused their preparation on physical and technical aspects of performance. In recent years, however, this approach has evolved toward a more holistic model that incorporates tactical, psychological, and analytical dimensions, with performance analysis emerging as a key component. Performance analysis in basketball is described as an essential tool for coaches and technical staff. This method allows collecting reliable information about the game, the competition, or both teams and players [1]. Normally, this performance analysis has been using the game-related statistics or box score stats [2]. Game-related statistics are very popular among coaches, players, and researchers and have been used to improve understanding of game performance in different contexts [1]. These game-related statistics have been used to analyze what happened in a game or competition, known as the classical static approach. This approach fails to characterize the structure of plays that teams use, simplifying it in a reductionist paradigm [3,4], analyzing the sport, therefore, as a series of discrete events [5].

Basketball research needs to evolve and move beyond a sole reliance on performance indicators [6], but to improve the understanding of how teams develop their plays during a game. In this sense, play-type statistics are the offensive and defensive actions played individually or as a team, planned in advance by technical staff [7]. In fact, technical staff of professional teams have stated that qualitative and descriptive information is preferred above game-related statistics [8]. Therefore, it is necessary to investigate additional performance indicators—such as (already investigated) catch and drives [9], cut and hand-off actions [10], isolations [10,11], post-up games [12], putbacks, and off-screens [13], or transitions [14]; to achieve a more comprehensive understanding of team playing styles. In this hand, play-type statistics (PTS) refer to offensive actions carried out through individual, dyadic, or collective interactions, as identified and classified by scouting staff [7]. Knowing tactical preferences and use of different teams allows technical staff to identify play trends, prepare game plan and tactics in defense and offense according to the opposite team’s weaknesses or own strengths to achieve competitive advantages [15,16].

Analysis of the EuroLeague 2017–2018 season identified fast breaks (78.2%), cuts (64.8%), pick-and-roll screens (61.5%), and transition plays and offensive rebounds (57.4%) as the most effective types of ball possessions [17]. In this same competition, in the 2020–2021 season, the best and worst teams were studied, finding that play-off teams show higher levels of isolation, pick-and-roll handling, post-up plays, and screen-off possessions compared to non-play-off teams [7]. On the other hand, when the NBA was studied, the best teams (play-off teams) used principally catch-and-shoot plays, pick-and-roll handlers, transition plays, and isolation plays [9]. The best teams in both competitions exhibit differences in playing styles: in Europe, teams use post-ups and off-screen plays, while in the NBA, catch-and-shoot and transition plays are more common. In the NBA, the best teams play a faster game based on individual abilities, while in the EuroLeague, the best teams play in a more structured manner, whether it is for post play or creating opportunities off-screen. One recent study analyzed six European leagues (Spain, Germany, Greece, Italy, France, and Adriatic leagues), including regular season and play-off games [18]. High-performance (HP) teams were characterized by a higher frequency of catch-and-shoot, post-up, and cutting actions, whereas low-performance (LP) teams demonstrated a greater reliance on pick-and-roll plays. Notably, teams classified as higher performing exhibited superior efficiency across the majority of the 25 play types examined, including pick-and-roll, isolation, transition, and off-screen actions, while no significant differences were identified between HP and LP with respect to hand-off situations.

To the best of our knowledge, research on play types has predominantly focused on European leagues. Accordingly, this study seeks to contribute to the scientific literature by providing insights that may support coaches in formulating strategies and anticipating potential outcomes when competing against teams that employ specific offensive play types. The interpretation of play types has proven useful for professional teams in optimizing strategies and practices, thereby enhancing overall team performance. For all the above, the aim of this study is twofold: (i) to characterize teams according to performance in regular season and play-off games and (ii) to identify play types that differentiate the best team in regular season and play-off games.

2. Materials and Methods

2.1. Sample

Data were collected from the NBA across five seasons, from 2019 to 2020 and 2023 to 2024. The dataset included Regular Season (RS) games (1230 games per season, except for the 2019–2020 “bubble” season with 1059 games) and Play-off (PO) games (2020: n = 83, 2021: n = 85, 2022: n = 87, 2023: n = 84, 2024: n = 82; total n = 421 = 6400 games).

For the purposes of the cluster analyses, the observational unit was the team-season. In the RS, all 30 teams were included in each of the five seasons, resulting in 150 team-season cases (30 teams × 5 seasons). In the PO, only teams that qualified for the post-season in each year were considered, yielding 80 team-season cases (16 teams × 5 seasons). Each season was treated as an independent competitive context; therefore, repeated appearances of the same franchise across seasons were modeled as separate observations.

2.2. Procedures

Data were extracted from official stats of the league (https://www.nba.com/stats (accessed on 31 March 2025)). The data is publicly accessible and may be used for educational or research purposes. The validity and reliability of this data have been proven before in several studies [16,19]. Data included situational variables such as season (from 2019/2020 to 2023/2024), type of competition (RS vs. PO), victories (in RS), or position (in PO). Also, play-type variables included absolute and percentage values of use and efficiency (points per possession) (see

Table 1. Description of play types.

Play Type	Operational Description
Isolation	A situation in which a player attacks one-on-one, usually from a static position, without direct tactical involvement from teammates to create an advantage.
Transition	Fast play after regaining possession of the ball, aiming to score before the opposing defense is set. Characterized by speed, verticality, and low structure.
Pick-and-Roll Ball Handler	The player with the ball uses a direct screen to create an advantage. Their decisions after the screen (drive, pass, or shot) are analyzed.
Pick-and-Roll Man	Player who sets the screen in the pick-and-roll and then rolls to the basket or pops out, looking to exploit mismatches created by the screen.
Post-Up	Action in which a player receives the ball near the basket, usually with their back to it, aiming to score through physical play or to attract defensive help.
Spot-Up	Shooting action after receiving the ball from a fixed position or with minimal movement, generally in open-space or spacing situations.
Hand-Off	Moving pass where the player with the ball hands it off directly to a teammate approaching, creating a dynamic screen-and-pass action and tactical advantage.
Cut	Off-ball movement toward the basket, typically to receive in an advantageous position. Includes direct cuts, backdoor cuts, or cuts off screens.
Off-Screen	Play in which the offensive player uses off-ball screens to open and receive the ball in a favorable position. Requires coordination and tactical reading.
Put-Backs	Immediate offensive action after grabbing an offensive rebound. Considered effective in moments of defensive disorganization.
Miscellaneous	Residual category grouping situations not classified under other play types, such as chaotic possessions, forced shots, or unsystematic opponent mistakes.

Adapted from Matulaitis & Bietkis [17] and Alonso-Pérez-Chao et al. [18].

).

2.3. Statistical Analysis

First, a descriptive analysis was conducted using means and standard deviations for all variables related to play types according to the type of competition. Subsequently, a two-step cluster analysis was performed to determine the number of clusters for each sample. Specifically, the two-step cluster analysis was applied to team-season observations in the RS and PO, using the number of victories obtained by each team in a given season as the clustering variable. This resulted in 150 team-season cases for the RS and 80 team-season cases for the PO. The purpose of the cluster analysis was descriptive, aiming to obtain a data-driven classification of teams into high-, intermediate-, and low-performance groups based on the number of victories. The two-step procedure was selected because it identifies optimal cut points using a log-likelihood distance measure and the Bayesian Information Criterion (BIC), thereby avoiding arbitrary thresholds or quantile-based divisions. This approach provides an objective and replicable grouping solution, which is appropriate when the goal is to classify teams according to their competitive success.

Each season was treated as an independent competition context, and therefore repeated seasons of the same team were analyzed as separate cases. We did not apply a specific statistical control for potential autocorrelation between seasons of the same team, as the primary aim of the cluster analysis was descriptive and classificatory (identifying high-, intermediate-, and low-performance groups based on wins). This point is considered in the interpretation of the results. This procedure enhances the robustness of the analyses compared to performing them individually [20]. Subsequently, a one-way ANOVA with Bonferroni correction (three groups of teams according to their level) was conducted. Finally, another one-way ANOVA was performed between the top teams from the RS and the top teams from the PO. Effect sizes were calculated using eta squared (η²). The interpretation of the values, according to [21], was as follows: very small (<0.01), small (0.01–0.059), medium (0.06–0.139), and large (≥0.14). The level of significance for all the comparisons was set at p < 0.05. The IBM SPSS statistical package version 170 24.0 for Windows (IBM Corp., Armonk, NY, USA) was used to analyze the data.

3. Results

The results of the two-step cluster analyses for both competitions are presented in Figure 1. The cluster analysis of the RS identified three distinct groups of teams based on the number of victories: Cluster 1 grouped 36.7% of the teams (cluster center = 50.35 victories; HP teams); Cluster 2 grouped 41.3% of the teams (cluster center = 37.7 victories; intermediate performance teams); and Cluster 3 grouped 22.0% of the teams (cluster center = 21.4 victories; LP teams). The cluster analysis of the PO also identified three groups of teams: Cluster 1 grouped 12.5% of the teams (cluster center = 14.8 victories); Cluster 2 grouped 36.2% of the teams (cluster center = 7.4 victories); and Cluster 3 grouped 51.2% of the teams (cluster center = 1.4 victories).

Subsequently, the descriptive results for each group of teams within each cluster are presented in

Table 2. Descriptive statistics for the teams in each cluster during the regular season and differences between subgroups.

Play Types	Points per Possession				Frequency (%)
	Cluster 1	Cluster 2	Cluster 3	p-Value (ES)	Cluster 1	Cluster 2	Cluster 3	p-Value (ES)
	Mean (SD)	Mean (SD)	Mean (SD)		Mean (SD)	Mean (SD)	Mean (SD)
Isolation	0.94 (0.07)	0.90 (0.07)	0.86 (0.07)	0.00 (0.14) a,b,c	7.19 (2.07)	6.88 (1.67)	5.87 (1.61)	0.00 (0.07) b,c
Transition	1.14 (0.04)	1.13 (0.04)	1.11 (0.04)	0.00 (0.11) b,c	16.49 (2.06)	15.84 (1.87)	16.07 (1.95)	0.19
Pick-and-roll ball handler	0.92 (0.05)	0.90 (0.05)	0.84 (0.06)	0.00 (0.26) b,c	16.02 (2.63)	17.77 (2.43)	17.37 (2.55)	0.00 (0.09) a,b
Pick-and-roll man	1.15 (0.07)	1.14 (0.07)	1.10 (0.07)	0.00 (0.06) b,c	5.83 (1.24)	5.81 (1.02)	5.76 (0.95)	0.96
Post-up	0.97 (0.07)	0.96 (0.07)	0.92 (0.08)	0.00 (0.06) b,c	4.82 (1.88)	4.08 (1.67)	3.92 (1.08)	0.01 (0.05) a,b
Spot-up	1.06 (0.05)	1.03 (0.04)	0.98 (0.05)	0.00 (0.31) a,b,c	23.31 (2.00)	23.53 (2.11)	24.20 (1.87)	0.13
Hand-off	0.94 (0.06)	0.93 (0.07)	0.90 (0.08)	0.00 (0.06) b,c	4.8 (1.46)	4.89 (1.27)	5.11 (1.26)	0.57
Cut	1.31 (0.06)	1.29 (0.06)	1.27 (0.06)	0.00 (0.09) b	7.02 (1.39)	6.84 (1.31)	6.95 (1.05)	0.74
Off-screen	0.99 (0.07)	0.97 (0.07)	0.95 (0.08)	0.08	3.83 (1.5)	4.00 (1.54)	3.88 (1.15)	0.79
Put-back	1.13 (0.06)	1.11 (0.06)	1.10 (0.06)	0.22	5.12 (0.74)	5.25 (0.82)	5.40 (0.075)	0.27
Miscellanea	0.56 (0.05)	0.55 (0.06)	0.52 (0.06)	0.01 (0.05) b,c	5.43 (0.54)	5.38 (0.65)	5.51 (0.55)	0.63

SD: standard deviation; ES: effect size (η²); ^a significant difference between Cluster 1 and Cluster 2; ^b significant differences between Cluster 1 and Cluster 3; ^c significant differences between Cluster 2 and Cluster 3; p-values highlighted in bold indicate statistical significance (p < 0.05).

. The table also displays the significant differences after the Bonferroni adjustment.

Among the significant variables, the Bonferroni post hoc test indicated which groups the differences were significant for each variable. Significant differences were found in the use of isolations between Cluster 1 and Cluster 3 (p < 0.01) and between Cluster 2 and Cluster 3 (p < 0.05); in pick-and-roll ball handler between Cluster 1 and Cluster 2 (p = 0.001) and between Cluster 1 and Cluster 3 (p < 0.01); and in post-up actions between Cluster 1 and Cluster 2 (p = 0.05) and between Cluster 1 and Cluster 3 (p < 0.05).

Table 3. Descriptive statistics for the teams in each cluster during the play-off and differences between subgroups.

Play Types	Points per Possession				Frequency (%)
	Cluster 1	Cluster 2	Cluster 3	p-Value (ES)	Cluster 1	Cluster 2	Cluster 3	p-Value (ES)
	Mean (SD)	Mean (SD)	Mean (SD)		Mean (SD)	Mean (SD)	Mean (SD)
Isolation	0.94 (0.06)	0.92 (0.12)	0.94 (0.18)	0.84	9.33 (2.60)	9.97 (3.87)	8.93 (3.56)	0.49
Transition	1.15 (0.07)	1.11 (0.10)	1.04 (0.15)	0.01 (0.10) b	15.14 (2.15)	15.60 (2.25)	14.78 (2.87)	0.42
Pick-and-roll ball handler	0.91 (0.09)	0.92 (0.10)	0.88 (0.14)	0.38	16.71 (4.03)	17.04 (3.81)	17.63 (4.60)	0.76
Pick-and-roll man	1.13 (0.07)	1.14 (0.12)	1.04 (0.23)	0.06	5.59 (0.89)	5.81 (1.42)	5.81 (1.84)	0.92
Post-up	0.90 (0.12)	0.99 (0.16)	0.90 (0.24)	0.18	4.71 (2.22)	4.68 (2.33)	4.87 (3.24)	0.95
Spot-up	1.10 (0.07)	1.03 (0.09)	1.00 (0.13)	0.02 (0.09) b	22.15 (3.60)	23.49 (3.92)	22.99 (4.52)	0.67
Hand-off	0.91 (0.06)	0.92 (0.22)	0.90 (0.24)	0.92	4.64 (2.72)	4.31 (2.18)	4.33 (1.93)	0.90
Cut	1.33 (0.10)	1.26 (0.14)	1.28 (0.16)	0.50	7.49 (1.78)	6.10 (1.67)	6.26 (1.68)	0.07
Off-screen	0.92 (0.14)	0.96 (0.18)	0.95 (0.25)	0.89	3.89 (1.68)	3.09 (1.46)	3.48 (1.54)	0.32
Put-back	1.04 (0.08)	1.06 (0.14)	1.06 (0.21)	0.94	4.76 (1.12)	4.62 (0.98)	5.41 (1.46)	0.03 (0.06) b
Miscellanea	0.59 (0.10)	0.62 (0.15)	0.51 (0.19)	0.02 (0.09) c	5.56 (0.87)	5.29 (0.89)	5.44 (0.97)	0.68

SD: standard deviation; ES: effect size (η²); ^a significant differences between Cluster 1 and Cluster 2; ^b significant differences between Cluster 1 and Cluster 3; ^c Significant differences between Cluster 2 and Cluster 3; p-values highlighted in bold indicate statistical significance (p < 0.05).

presents the results of the descriptive and inferential analyses for the teams that qualified for the PO within each cluster group. The differences are shown through Bonferroni post hoc comparisons along with effect sizes.

The results of the inferential analysis for the PO games revealed fewer differences among the three groups of teams, and these differences were primarily associated with the group of LP teams. These teams were less effective in transitions, spot-up situations, and other types of shots (miscellaneous). Regarding style of play, all teams that reached the PO displayed very similar patterns, except for putbacks, where the LP teams showed a higher percentage. This action depends on a missed previous shot and, therefore, is not strategically planned in the same way as other play types.

Finally,

Table 4. Descriptive statistics and statistically significant differences between the top teams in the regular season and the play-offs.

Play Types	Points per Possession			Frequency (%)
	RS	PO	p-Value (ES)	RS	PO	p-Value (ES)
	Mean (SD)	Mean (SD)		Mean (SD)	Mean (SD)
Isolation	0.94 (0.07)	0.94 (0.06)	0.96	7.19 (2.07)	9.33 (2.60)	0.00 (0.11)
Transition	1.14 (0.04)	1.15 (0.07)	0.60	16.49 (2.06)	15.14 (2.15)	0.06
Pic-and-roll ball handler	0.92 (0.05)	0.91 (0.09)	0.09	16.02 (2.63)	16.71 (4.03)	0.48
Pick-and-roll man	1.15 (0.07)	1.13 (0.07)	0.62	5.83 (1.24)	5.59 (0.89)	0.56
Post-up	0.97 (0.07)	0.90 (0.12)	0.03 (0.06)	4.82 (1.88)	4.71 (2.22)	0.86
Spot-up	1.06 (0.05)	1.10 (0.07)	0.02 (0.08)	23.31 (2.00)	22.15 (3.60)	0.14
Hand-off	0.94 (0.06)	0.91 (0.06)	0.14	4.8 (1.46)	4.64 (2.72)	0.77
Cut	1.31 (0.06)	1.33 (0.10)	0.54	7.02 (1.39)	7.49 (1.78)	0.34
Off-screen	0.99 (0.07)	0.92 (0.14)	0.01 (0.08)	3.83 (1.5)	3.89 (1.68)	0.90
Put-back	1.13 (0.06)	1.04 (0.08)	0.00 (0.23)	5.12 (0.74)	4.76 (1.12)	0.19
Miscellanea	0.56 (0.05)	0.59 (0.10)	0.10	5.43 (0.54)	5.56 (0.87)	0.54

RS: regular season; PO: play-offs; SD: standard deviation; ES: effect size (η²); p-values highlighted in bold indicate statistical significance (p < 0.05).

presents the differences between the best teams according to the type of competition, RS or PO, along with their respective effect sizes (η²).

The results indicated statistically significant differences, with medium effect sizes in the use of isolations and in the effectiveness of post-ups, spot-up shots, and off-ball screens, and a large effect size in putbacks. It is noteworthy that in the RS, the best teams were more effective in six play types, whereas in the PO, they were superior in three of these play types.

4. Discussion

The purpose of this study was to characterize and describe the most frequently used play styles by NBA teams, both in the RS and in the PO, according to the number of victories, as well as to compare the performance of the best teams in both phases of the competition. The results identified three groups of teams based on the number of victories in the RS and PO. The best teams in the RS were more effective in isolation and spot-up situations compared to the other teams, and they also made greater use of P&R ball handler and spot-up actions. In the PO, the differences were smaller and mainly involved the LP teams. Finally, the best teams in both the RS and PO differed in the effectiveness of post-up, spot-up, off-screen, and put-back actions, as well as in the use of isolations. The findings suggest that although teams in the RS and PO exhibit similar play frequencies, performance differences are primarily explained by the effectiveness of their actions.

Evidence from the NBA indicates that teams employ very similar play types regardless of their performance level (high, medium, or low). The primary distinction lies not in the selection of plays but in the efficiency with which they are executed, particularly in the case of LP teams when compared to their counterparts. From a technical standpoint, HP teams are characterized by greater effectiveness in finishing through isolation and spot-up situations. Findings from European leagues suggest that spot-up or catch-and-shoot situations represent key performance indicators that differentiate the most successful teams (Pérez-Chao et al., 2025) [18]. Within the Euro League, although isolation plays constitute the most frequently employed finishing actions, the catch-and-shoot has been identified as the most effective option [7]. In contrast, in the NBA, spot-up situations are among the most widely used across all teams; however, the evidence shows that the factor most strongly discriminating between winning and losing teams is their effectiveness in isolation plays [9]. In this study, due to NBA official rules, teams in defense are forbidden to do some defensive strategies. Specifically, no defensive player can stay in the restricted zone, “the paint”, during 3 s without actively guarding an opponent. In European basketball, FIBA rules allow defensive players to stay in the restricted zone for an unlimited time; that allows players to help partners and protect the rim. This fact hinders the fact that isolation plays ended successfully. In the NBA, defensive players can help teammates in isolation, but in a more active way, as a double team, they do not defend the rim due to regulations. Because of that, isolations and spot-ups are closely related. A spot-up’s action is very similar to a catch-and-shoot situation. A player receives a pass after a play and, in an open space and with minimum movement, ends in a shot [22]. HP teams in RS are more efficient in these two play types than the other teams.

The recent literature supports the idea that the best teams win more due to their better team dynamics and coordination [3,23] and effective tactics to create advantage [18]. Isolations and spot-up play types are closely related. In FIBA basketball, an isolation situation is typically followed by a sequence of defensive help actions. Successfully overcoming these defensive rotations requires precise coordination and spatial positioning of the offensive players to optimize scoring [22], enhancing intra-team offensive communication by systematically cultivating it during practice sessions and reinforcing it in competitive contexts [24]. In the NBA, the best players in isolation receive double teams, so it is easier to find the free shooter. Given the differences in NBA basketball, teams need to recruit skilled players, not only the best shooters but also the best players who can be a threat in iso situations. Successful teams do not rely only on designing effective tactical actions but on picking the player who can solve the situation. Moreover, the best players need less time and space to shoot three-pointers [25], so the best players do not need excellent plays to be effective. Keeping that in mind, LP teams show little difference from the other high- and intermediate-performance groups of teams in the style of play but show differences in 9 of 11 play types efficiency (less than 10% of plays). It has been stated that the best teams are more efficient due to better team coordination and stronger individual player abilities [3,18], though in decisive parts of the games or decisive games, the best players decide what and how to play ball possessions [1].

Another interesting trend is that the use of pick-and-roll is not as important as in previous studies [9], with the low- and medium-performance teams using the most pick-and-roll (ball handler and roll man) in RS and PO games [10]. Years ago, pick-and-roll action was the most used in basketball [26,27], but it is used more by LP teams [18] and with worse efficiency. Pick-and-roll actions are used to create advantages in space and time and help teams with less developed coordination to find simpler solutions [28].

In the PO, differences are smaller since only HP teams compete. The best teams only show differences in efficiency across three play types. However, there are greater differences between the best teams in the RS and those in the PO, especially in terms of efficiency. RS teams are more efficient in post-up, off-screen, and put-back situations, while PO teams are more efficient in spot-up situations. The POs are the decisive phase in the NBA, considered a period of high psychological and physiological demand for players and teams. Teams and players who are able to experience less competitive anxiety and reduce negative stress are more likely to succeed [29] and will be able to play better [30]. In the RS, there are 82 games, most of which do not have an immediate impact on the team’s final outcome. Therefore, winning a single game carries a different level of importance: while in the RS a team can lose three games in a row and still remain in contention, in the play-offs, losing two out of three, or three out of five games, means elimination [1]. Along these lines, it has been shown that in the PO, the effectiveness of defensive rebounding increases in relation to winning games [1] in the ACB league. This study also shows that the percentage of put-back usage decreases in the NBA PO. Teams are aware of the importance of offensive rebounding for winning games [31], and therefore, in the PO, teams pay much more attention to securing defensive rebounds and preventing opponents from having put-back opportunities.

Available literature stated that using this kind of data from only one season limits the generalizability and practical application of the findings [9]. In the present study, a large sample size is used (five seasons with regular season and play-off), bringing more robust findings. The use of advanced analytics (like machine learning) could give a more thorough understanding of the competition. The collaboration of academic researchers and technical and tactical staff in clubs and organizations could bridge the gap between science and practice. Also, studies have focused on offense, but the defense’s influence on the outcome must be studied.

This study presents several limitations that should be acknowledged. First, its observational design does not allow for controlling potential confounding factors such as roster composition, player injuries, coaching changes, or other contextual elements that may influence team performance across seasons. Second, the cluster solution was based on a single continuous variable (number of wins), which restricts the complexity of the model and may limit the nuance of the classification. Although this approach was aligned with the descriptive aim of the study, it does not capture multidimensional aspects of performance. Additionally, by treating each season as an independent competitive context, repeated observations of the same franchise may introduce some degree of autocorrelation that was not statistically modeled, and this should be considered when interpreting the findings. Furthermore, some seasons included in the analysis were affected by unique contextual circumstances, such as the 2019–2020 “bubble” environment and pandemic-related schedule adjustments, which may have influenced both play-type usage and scoring efficiency. Future research could integrate additional performance indicators, multilevel or mixed-effects approaches, and contextual variables to provide a more comprehensive understanding of tactical behavior in professional basketball.

5. Conclusions

The study and analysis of play types in the NBA is a useful tool for assessing teams and competition. Teams perform differently according to their level. In the NBA, the difference lies in the effectiveness of actions rather than in the way of playing, which means that teams have two options: (i) design training programs aimed at collective improvement to generate better options for players individually and (ii) individual improvement, where those advantages are exploited by each player.

From a practical perspective, coaches and performance analysts should focus on improving the quality and timing of execution in isolation and spot-up actions, as these are the play types that most clearly differentiate successful teams in the NBA. Training tasks should reproduce realistic decision-making contexts, emphasizing reading defensive help, passing under pressure, and creating space for catch-and-shoot situations. Individual development programs should prioritize players capable of generating and resolving one-on-one situations, not only through physical superiority but also through anticipation, spatial awareness, and efficiency in limited time and space. Furthermore, the design of team offensive systems should integrate principles that connect isolations with quick ball circulation to generate open spot-up opportunities, while defensive training should reinforce close-out and switching mechanisms to minimize such advantages. Incorporating detailed play-type analytics into scouting and daily training planning can help coaches identify inefficiencies in execution, adapt tactical emphasis throughout the season, and ultimately enhance team performance consistency in both the regular season and play-offs.