Offensive Modalities and Their Influence on Basketball Efficiency Between Winning and Losing Teams

LITHUANIAN SPORTS UNIVERSITY

INTERNATIONAL BASKETBALL COACHING AND MANAGEMENT STUDY PROGRAMME

JULIUS DEMENIUS

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OFFENSIVE MODALITIES AND THEIR INFLUENCE ON BASKETBALL EFFICIENCY BETWEEN WINNING AND LOSING TEAMS

FINAL MASTER‘S THESIS

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2 TABLE OF CONTENTS

ABSTRACT ...... 4 SANTRAUKA...... 5 INTRODUCTION ...... 7 1. LITERATURE REVIEW ...... 8 1.1 Summary of statistical approach in basketball ...... 8 1.2 Introduction to offensive modalities ...... 9 1.3 Summary of basketball offensive modalities...... 10 1.4 Statistical efficiency measures in basketball ...... 15 2. METHODOLOGY AND ORGANIZATION ...... 17 2.1 Research object and aim ...... 17 2.2 Research strategy, logic and nature ...... 17 2.3 Research sample, variables, tool and statistical analysis methods ...... 18 3. RESULTS ...... 20 3.1 Usage ratio of basketball offensive modalities ...... 21 3.2 Efficiency of basketball offensive modalities ...... 22 3.3 Offensive modalities usage ratio between winning and losing teams ...... 23 3.4 Offensive modalities efficiency between winning and losing teams ...... 25 4. CONSIDERATIONS...... 27 CONCLUSIONS ...... 31 RECOMMENDATIONS...... 32 REFERENCES ...... 33 ANNEXES...... 38 Annex A ...... 39 Annex B ...... 52

3 ABSTRACT OFFENSIVE MODALITIES AND THEIR INFLUENCE ON BASKETBALL EFFICIENCY BETWEEN WINNING AND LOSING TEAMS

Keywords: basketball, statistical analysis, finishing actions. Research problem question: Which offensive modalities should be most important in team’s offensive structure regarding their quantity and quality? Aim of the study: To determine which offensive modalities have most influence between winning and losing teams on NBA playoffs. Hypothesis 1: There will be significant differences between winning and losing teams regarding offensive modalities usage ratio. Hypothesis 2: There will be differences between winning and losing teams regarding offensive modalities efficiency. Methodology: 1. Scientific literature review 2. Statistical data collection 3. Statistical analysis using SPSS Statistics and Microsoft Excel software

Offensive modalities for this research were chosen from the provided “Synergy Sports Technology” software database: isolation, transition, post-up, spot-up, cut, hand-off, off-screen offense, pick and roll ball handler; roll man, putback, miscellaneous. WIN% and LOSS% were used to determine winning and losing teams on NBA playoffs. Three most used modalities were found to be: spot-up, pick and roll ball handler, and transition, while most efficient ones were cut and putbacks. Results show that significant differences were found in isolation, transition, and cuts finishing action for winning teams and pick and roll ball handlers as well as roll man for defeated teams. Efficiency differences for winning teams were found in off-screen offense, putbacks, cuts and miscellaneous modalities. NBA teams follow analytical strategy trends and tend to rely on long-range shooting, shots to close to the basket and easy baskets. Both type teams play in similar offensive structure, but winners were able to create more fast-break opportunities due to their defense and rebounding. Off the ball movement, making tough shots, offensive rebounding and putbacks, cuts had the biggest margin between winning and losing teams in terms of efficiency.

4 SANTRAUKA PUOLIMOS CHARAKTERISTIKOS IR JŲ ĮTAKA KREPŠINIO EFEKTYVUMUI TARP LAIMINČIŲ IR PRALAIMINČIŲ KOMANDŲ

Raktiniai žodžiai: krepšinis, statistinė analizė, efektyvumas. Probleminis klausimas: Kurie puolimo charakteristikos veiksmai turėtų būti svarbiausi komandų puolimo struktūroje atsižvelgiant į jų atakų kiekį bei kokybę? Darbo tikslas: Nustatyti, kurios puolimo charakteristikos turi daugiausiai įtakos tarp laiminčių ir pralaiminčių komandų. Hipotezė 1: Puolimo charakteristikų dažnumas krepšinio rungtynėse skirsis tarp laiminčių ir pralaiminčių komandų. Hipotezė 2: Puolimo charakteristikų efektyvumas krepšinio rungtynėse skirsis tarp laiminčių ir pralaiminčių komandų. Tyrimo metodai: 1. Mokslinės literatūros apžvalga; 2. Statistinių duomenų rinkimas; 3. Statistinė analizė. Atlikto tyrimo duomenys buvo apdoroti SPSS Statistics ir Microsoft Excel programomis

Šiam tyrimui atlikti visos puolimo charakteristikos buvo pasirinktos iš “Synergy Sports Technology” duomenų bazės: žaidimas 1 prieš 1, greitas perėjimas iš gynybos į puolimą, žaidimas nugara, metimas iš stovimos padėties, kirtimas po krepšiu, kamuolio perdavimas iš rankų į rankas, žaidimas naudojantis užtvaromis, 2 prieš 2 kai ataką užbaigia gynėjas, 2 prieš 2 kai ataka užbaigiam po perdavimo po krepšiu, metimo pataisymas, kiti. Laiminčios ir pralaiminčios komandos buvo nustatytos pagal WIN% ir LOSS% formules. Rezultatai parodė, kad reikšmingi laiminčių komandų skirtumai buvo rasti žaidžiant 1 prieš 1, naudojant kirtimus po krepšiu bei greitų atakų kiekyje. Pralaiminčios komandos dažniau žaidė 2 prieš 2 kai ataką užbaigia gynėjas bei po perdavimo po krepšiu. Laiminčios komandos buvo efektyvesnės užbaigiant atakas pasinaudojant užtvaromis toliau nuo kamuolio, pataisant netaiklų metimą, atliekant kirtimus po krepšiu bei kiti kategorijose. Tyrimas parodė, kad NBA komandos modeliuoja savo puolimo sistemas pagal naujausias krepšinio tendencijas, kai dažniausiai atakos užbaigiamos metimu į krepšį nuo trijų taškų zonos ir iš stovimos padėties bei metimais iš baudos aikštelės ribų. Laiminčios ir pralaiminčios komandos žaidžia panašiu stiliumi, tačiau laiminčios komandos geba susikurti galimybes pelnyti lengus taškus

5 greito puolimo metu dėl savo gynybos bei sėkmingai atkovoto kamuolio. Sėkmingas judėjimas be kamuolio, puolime atkovotas kamuolys, sunkūs metimai bei prakirtimai po krepšiu naudojantis varžovų gynybos klaidomis buvo vieni iš faktorių, kuriuose laiminčios komandos rodė didesnį efektyvumą.

6 INTRODUCTION Topic relevancy: Analytics in basketball is a new and evolving set of tools that is being used more commonly in the industry, which should also provide a competitive advantage for decision- makers by using statistical analysis (Alamar, 2013). In recent years, basketball is highly analyzed, which provides coaches valuable data of efficiency, player or team performance during a game or whole season (Mikołajec, Maszczyk, Zając 2013). However, Goldsberry (2012) suggests statistical analysis cannot be effective without spatial analysis and decision-makers should combine both. While the game of basketball is changing rapidly coaches need to keep up with new trends and create offensive strategies for efficient scoring to gain competitive advantage. Zukolo, Dizdar, Selmanović & Vidranski (2019) in collective research about the role of finishing actions in the final result of basketball match distinguished 11 possession finishing actions and used it for prediction of team performance: isolation, post-up, spot-up, cuts, handoff, pick and pop, off-screen offense, pick & roll, putbacks, free throws, other actions. Bazanov, Võhandu, Haljand (2006) in their work analyzed various offensive modalities and their efficiencies at different level leagues and Zukolo et al., (2019), determining differences of finishing actions ratio and efficiency between winning and losing teams in European championship on 2013, while Selmanović, Škegro & Milanović (2015) focused their study on offensive modalities usage frequencies and comparison between European and American professional leagues. Research Problem: Basketball teams are leaning towards an analytical approach and similar offensive structure, but it is important to find and evaluate the quantity and quality of all offensive modalities and how they affect the team's performance. Oliver (2004) introduced "Four-factors", where shooting, turnovers, offensive rebounds and free throws are considered as main categories that have the biggest impact between winning and losing teams. Most of the studies are based on traditional box score or "Four-factors" statistical measures and data, but there is still a lack of wider studies of finishing actions frequency and efficiency and their influence between winning and losing teams on the highest basketball level – National Basketball Association (NBA) playoffs. Aim of the study: To determine offensive modalities and calculate their efficiency amongst winning and losing teams Value of research: Offensive modalities influence on team winning would be beneficial for basketball organization’s decision-makers and coaches to build successful, efficient and winning teams. Structure of thesis: 1. Scientific literature review 2. Empirical research methods, review of results and discussion 3. Suggested article to “Baltic Journal of Health & Science” 7 1. LITERATURE REVIEW 1.1 Summary of statistical approach in basketball

For a regular fan, it might seem that game of basketball is mostly a free-flowing team sport as 10 guys on the court has the goal to put the ball in the basket. While in some cases basketball seems like a chaotic game, but professionals are playing in a structure built by coaches whose goal to create a system and use offensive modalities most efficiently for their players and team to be able to win as many games as possible. Game statistics are a tool used worldwide and is one of the best tools to evaluate team's or player's performance (Junior, 2004). As Nikolaidis, (2013) stated basketball in the US and mostly in major league NBA (National Basketball Association) is highly analyzed by the statistics, various data measurements and quantitative techniques. Alamar (2013) described analytics as collected and structured data sets forwarded to decision-makers. However, Goldsberry (2012) suggests statistical analysis cannot be effective without spatial analysis and decision-makers should combine both. As basketball in recent years implemented bigger approach to statistical analysis and mathematics-based strategies to put players to be able to efficiently score or create opportunities for others it is important to understand and calculate a statistical ratio of different offensive modalities as well as their efficiency influencing end game result between winning and losing teams. In a professional level of basketball there is several responsible personnel involved in the decision making by choosing, signing players and putting a team together which would fit in today's basketball. Nikolaidis (2013) in his paper mentioned that there can be a lot of money and resources saved if managers would take time and adopt those new comprehensive metrics for decision making. Alamar (2013) mark two goals of sports analytics in his book:

1) Strong sports analytics program would save time for all responsible decision-makers on a team analyzing player's or team's performance; 2) To provide insight analyzing player's or team's that without statistical measurements would not be possible;

By following these statements and analysis a clear goal of sports analytics is to measure what an eye cannot see and save time as well as collect accurate data for assessment of performance by a player or team. There is a difference between NBA league and European leagues culture as in United States team managers and executives are mostly responsible for building a team together and most important puzzle pieces are superstar players, though in Europe coaches has a significantly more important role

8 and is actively involved in team personnel decision making. Coaches also carries a responsibility to create a strategy that would fit his chosen players. As basketball analytics is still a relatively new thing in the industry and highest-level coaches usually considered as old school, they mostly rely on their eye-test capabilities and intuition to make decisions during games and not creating strategy before that, but Nikolaidis (2015) raises a question if it is still appropriate and effective in today's era.

1.2 Introduction to offensive modalities

Performance analysis of sport takes into account team or player performance metrics of calculated variables and provides evaluation (O'Donoghue, 2005) and prepares for competition (Puente, Coso, Salinero & Abián-Vicén, 2015). O'Donoghue (2009) also states that such analysis has been used more often in recent years NBA (National Basketball Association) now provides a number of various data for fans using “Synergy Sports Technology” software database, which cuts every single play on the court to different video clips and turns it to statistical data (Oliver, 2013). There is various statistical data on every possible player's or team's action on the court, but for coaches it is important to create a strategy of plays and actions on the court which would involve players movements, set plays and building habits for their strategy to work. Most of the time coaches used to check traditional box score statistics, but as technology improved tremendously and now teams have access to different information and data sources they can look to the game in a more detailed way. Zukolo, Dizdar, Selmanović & Vidranski (2019) in collective research about the role of finishing actions in the final result of basketball match distinguished 11 possession finishing actions and used it for prediction of team performance:

• Isolation – finishing action with a shot or penetration after play type 1:1 • Post up – finishing action with the player's back to the basket • Spot up – penetration or shot after a pass to a player who is not strictly guarded or is open • Cuts – inside cut or outside cut and finishing action with a shot or penetration after a pass • Handoff – the player hands out the ball to another player who uses the passer's screen to make a shot or penetrate to the basket • Pick and Pop - a player who sets the screen then pops to the perimeter and receives the ball for a shot • Off-screen offense – off-ball screen creates enough space for open shot or penetration • Pick n Roll – Screen on a player with the ball with blocker cutting to the basket 9

• Putbacks – player secures an offensive rebound, then immediately scores a basket • Free throws (FT) • Other actions – quick-lost ball and other actions that cannot be classified into either of the above-mentioned finishing actions

“Synergy Sports Technology” analysis software and database provides Play Types as offensive modalities, which differ from Zukolo et al., (2019) listed modalities. Pick and Roll modality is divided into ball handler and roll man types. Transition modality is added, which is described as play type at Christmann, Akamphuber, Müllenbach & Güllich (2018) work: "Transition - Opening in the backcourt, finish within 7 s and create a shot opportunity before opponent's half-court defense is set". “Synergy Sports Technology” software has free-throw modality under Miscellaneous type, which also covers half-court shots, fouls committed and other non-categorized play types.

1.3 Summary of basketball offensive modalities

Most of the listed offensive modalities above have been analyzed in scientific literature separately as well as collectively and have their input to a basketball game as a possession finishing play type. Isolation according to Christmann et al., (2018): "The ball handler attempts to vanquish his counterpart defender in a 1-on-1 attack; teammates move away from the ball handler to draw their assigned defenders away and provide him maximum space for the 1-on-1 attack". According to Trninić, S., et al (2010), basketball is both individual and collective game which consists of players individual and team-related actions on the floor. Playing isolation basketball requires to have individually strong players on the team with specific skills to complete such play type successfully and efficiently. It is up to team coaches to find players most suitable for a specific role (Dežman, Trninić & Dizdar, 2001). Today NBA league has become guard dominant league. Players use their speed and quickness to create opportunities for scoring. However, Christmann et al., (2018) research about effectiveness on some play types at NBA league concluded that "1 on 1" situations in team's offense were less effective than set team plays because of lack of cooperation actions, frequent 2PT and 3PT shots, highly contested shots, low shooting percentage as well as a low number of PPP (Points per Possession). Post up – inside the perimeter play type. Usually offensive player is playing back to the basket against a defender. Ibáñez, McRobert, Toro & Vélez (2016) investigated: "how game conditions (i.e.,

10 ball possession duration, reception attitude, pass zone, pass distance; reception zone, reception distance, player position, defensive pressure against the receiver and defensive help) and situational variables (i.e., team ranking, game period, game location and match status) impacted on ball possession effectiveness when using inside pass". Received results showed that almost 20% of ball possessions included inside passes and was 1.4 to 2.0 times more effective compared to those that did not include this action. Inside pass effectiveness was also researched by Courel, Suárez, Ortega, Piñar & Cárdenas (2013), who found out that on 2012 Euroleague Playoff games 16.7% of passes were made to the post. Passing from the center position was more effective in winning teams (Zhang, et al., 2019), therefore specific location was not provided. Possessions which included inside pass were more effective and generated a bigger amount of points. Also (Zhang et al., 2019) found that winning team centers generated more two-point field goals from the paint area. Spot up – situations when an offensive player is standing still and waits to receive a pass from a teammate and attack immediately without taking any dribbles. As today's basketball has changed tremendously and now includes more outside perimeter shots and playing smaller line-ups. It is important to analyze and understand which shots are of high quality to make the best decision in a particular game situation (Skinner, 2012). Caporale and Collier (2015), stated that while the talent on basketball teams are on a different level and some aspects cannot be fully controlled, but coaches should then concentrate on controlling the shot selection of their team. Chang et al., (2014) at MIT Sloan Sports Analytics Conference presented a research about shot quality at NBA league and were evaluating whether shots were taken off the dribble or without a dribble and found out that spot-up shots were significantly more effective rather than shots off the dribble. One of the questions that authors raised was that spot-up shots were more effective because defenders were further away and the offensive player had more time for a shot rather than taking them off the dribble. Nikolaidis (2015) found that investigated team of research won a game when at least 45% of perimeter shots were made. However, according to Goldman & Rao (2013) teams should concentrate on taking long-range perimeter shots at the beginning of the game until fatigue can be a factor. One of the hypotheses that authors raised was that spot-up shots were more effective because defenders were further away and the offensive player had more time for a shot rather than taking them off the dribble. Cuts – a player is using the various change of direction moves without a basket to get free from his defender and make a quick move close to the basket to receive a wide-open pass to score. Based on Tallir, Lenoir, Valcke & Musch (2007) game performing coding instrument table cutting action leads for better passing, possession scoring opportunities. Zukolo et al., (2019), in the study about the role of finishing actions, received results that cut action when 2 or 3 players are involved in the most efficient way to score a basket. Furthermore, isolation, post up, spot up and cutting 11 situations accounted for 58% of offense structure in 2013 European championship at Slovenia out of 100 possessions of each scoring action, which also supports Selmanović, Škegro & Milanović (2015) findings who recorded between 57% and 63% of finishing actions with the same structure in Euroleague and NBA. Also, by Zukolo et al., (2019) results showed that cut action was more frequently used and were more efficient by winning rather than losing teams. Following existing researches, it is clear that coaches must involve cutting to the basket action to their offense and this can be reached by utilizing more player and ball movement in their offense running sets or implementing movement based system. Transition – situations, when an offensive team can finish a possession before the opponent's teams, have their defense set (Christmann et al., 2018). By Trninić et al., (1995) offensive and defensive effectiveness depends on the ability to go from one action to another as fast as possible. One of the most efficient playing types in team's offense as early transition offense creates easy opportunities to score a basketball, therefore according to Bazanov, Võhandu & Haljand (2006), research of Tallinn University men's basketball team which plays Division One of the Estonian basketball league showed that fast break is the most efficient play type of studies modalities. Also, the author's research showed that winning teams were able to use transition offense more frequently and efficiently rather than losing teams. Results of Evangelos, Alexandros & Nikolaos (2017) showed significant differences between winners and losers in the ratio of primal fast break possessions. Also, winners were able to make more two-point baskets than defeated teams. Sporiš, Šango, Vučetić & Mašina (2017) distinguished that for a successful transition action influenced by a ball-dominant point guard play, one on one defense which leads to steals and defensive rebounding as well as the ability to challenge or block shots in the paint. Handoff – action on the floor when a pass is made from hand to hand. Usually a screener makes a pass to the guard, then this action is continued with a pick & roll play. According to Vaquera, García-Tormo, Gómez Ruano & Morante (2016) ball screens requires cooperation between two players for the action to be successful. Based on Zukolo et al., (2019) research of 30 random games of the European Basketball championship 39% of hand-off actions succeeded, but it also showed that losing teams were using this action more often than winning teams, even though winning teams had a better efficiency. Fernandez (2009), findings state that passing the ball in almost the same spot by using hand-offs or regular passes, but without swinging the ball completely helped the defense to be well situated and had a direct impact on the offensive team's shooting percentage. While there might seem that hand-off action does not have a significant influence on team offensive efficiency, but it is worth keeping in mind that hand-off action usually goes together and is a variation of set up to pick and roll action, which mentioned researches did not include into consideration. 12

Off-screen offense – play type which includes offensive player movement without a ball using teammates set screens to get wide open or receive a pass for finishing (Lamas, 2011). By Selmanović et.al (2015) American and European basketball differs in their frequently used offensive modalities where ball screens are more used in American basketball for pick and pop, cut actions. However, even though Zukolo (2019), found similar findings in the most used offensive modalities to Selmanović et.al (2015), but off-ball screens were used just 2% of the time for the specific sample of 30 randomly selected games of 2013 European Championship at Slovenia, therefore according to Zukolo et al., (2019) research off-ball screens were one of the most efficient offensive modality with 44% efficiency. Bazanov et al., (2006) findings showed results that off-ball screen efficiency differs due to the number of off screens used during possession as with two ball screens scoring had 44% efficiency, while using one screen had 56% and four screens had 50% efficiency. Even though off- ball screens showed a solid efficiency on the team's offense, according to Zukolo et al., (2019) results, defeated teams were the ones who used off-ball screens more. While off-ball screens are used in different capacities on different basketball leagues, regions and levels team coaches should look for the optimal percentage of off-ball screens in their offenses and their efficiency. Pick n Roll (ball handler and roll man) – Screen set on the ball handler's assigned defender, screener rolls to the rim or rolls away. Ball screens in general are one of the most used play type actions on offense for basketball teams (Gómez et al., 2015). According to Wang, Liu & Moffit (2009) pick and roll action is most commonly used through different levels of basketball: amateur to professional. By Hucinksi and Tymanski (2006), most of the possessions in the team's offenses consisted of pick and roll action regardless of the offensive system. On Marmarinos (2016) research a total of 12,376 pick and roll plays were analyzed. It was found that pick and roll actions with two passes after set screen generated the biggest number of PPP – 1.27 while ball handlers shot were generating the least amount 0.81PPP. Furthermore, the most efficient (59.74%) pick and roll ending is a direct pass to the roller, where a shot with two passes after pick and roll has a 44.35% efficiency, spot-up situation after one pass – 42,68%, pop up situation – 37,07% and ball-handler shot – 36%. By looking at these results coaches can structure their offense and use pick and roll actions for the most efficient finishes. Pick and roll offensive modality can also be used not only for finishing of possession but also to initiate offense and create space to operate. Lamas (2015), found that pick and roll commonly used for offense space creation (33% of the time) and also to initiate offense as it produced the highest probability leading to space creation on the court.

While pick and roll is a valuable tool in the team's offense for scoring it is also important to note that coaches can use this action to also initiate the offense, move the ball and create space to operate. Putbacks – scoring a basket after an offensive rebound. For a successful putback to happen an offensive rebound is needed first. Oliver (2014) stated that offensive rebound has more value than a defensive one, due to the possibilities to score an immediate basket. The more offensive rebounds a team can take, the more opportunities they have to score and reduce the opponent's chances to score (Hofler & Payne, 1997). Özmen (2016) stated that in the Euroleague team who grabs at least 1 offensive rebound more than opponents have a 6,3% higher chance to win a game. Malarranha, Figueira, Leite & Sampaio (2013) also found that offensive rebounding together with effective field goal percentages were the main factors deciding the result of the game. Zukolo et al., (2019) in his research found that putbacks can be included to list of most effective modalities. Therefore, it is worth mentioning that putbacks do not happen that often and teams have less control over offensive rebounding as a ball can bounce to various directions after a missed shot. Miscellaneous - possession ends in turnover, fouls received, free throws, half-court shot or other endings that cannot be classified. “Synergy Sports Technology” software provides few statistical actions that can be counted: free throw (FT), turnover (TOV), shooting foul (SF), their ratio as well as efficiency percentage from miscellaneous modalities on the floor. Miscellaneous offensive possession endings should not be fully neglected as free throws, turnovers are two of "four factors" that Oliver (2014) introduced as main characteristics which influence team success. Özmen (2016) stated that making at least 1 turnover more than opponent decreases winning possibility by 13 % in the regular season and 17% in playoffs. Lower number of turnovers are also correlated with technical skills of players, but also team maturity (García, Ibáñez, De Santos, Leite & Sampaio, 2013). Gómez, Lorenzo, Sampaio, Ibáñez & Ortega (2008) in their findings included free throws to the possession ending which has a positive effect on the team's offense. Sampaio, Janeira, Ibáñez & Lorenzo (2003) found that winning teams had better FT percentage than losing teams. While having a good free throw percentage matters, Kreivytė & Čižauskas (2010) stated that the quantity of FT attempted during the game had more influence between winning and losing teams. To add more Özmen (2016) found that the number of fouls made have an influence on team success as one more foul attempted than an opponent decreases a possibility to win in 5,1% on regular season and 5,8% in playoffs. While offensive possession on basketball has a number of possible and obvious endings, it is important to include turnovers and free throws to the calculations as they have a significant influence between winning and losing teams.

1.4 Statistical efficiency measures in basketball

Performance indicators are used by coaches to calculate and evaluate individuals or team performance (Hughes and Bartlett, 2002). Bartlett (2001) also stated that coaches are paying bigger attention to how various indicators, measurements can help to improve their athlete's performance. Teams who followed a set strategy found to be more effective in reaching a goal (DeShon, Kozlowski, Schmidt, Milner, & Wiechmann, 2004). During recent years a statistical approach to basketball is seen on a wider spectrum and talked about more. Gómez et al., (2008) stated that game-related statistics are useful for coaches when they want to compare the team's performance against opponents, but full season length should be considered as game schedule, opponents strategy, game speed components might influence calculated metrics. Efficiency by success percentage. Various statistical measurements can be used for counting player's, or team's efficiency on a basketball court. In the Bazanov et al., (2006) research on basketball offensive trends and calculated their efficiency by the success percentage, for example Team A attempted 75% of their offense with fast breaks and their efficiency was 40%. Similar measurements were used at Zukulo et al., (2019) for calculating the role of finishing actions at basketball games, counting their efficiency and influence on winning and losing teams. Points per Possession. One of the popular mainstream measures for counting player's or team's efficiency on offense and defense is PPP (Points per Possession), where possession is described as action when one team gains control of the ball fully and ends where another team receives the ball: 1) made field goal or free throws; 2) defensive rebounds; 3) turnovers (Kubatko, Oliver, Pelton & Rosenbaum, 2007). Oliver (2004) in his book "Basketball on Paper" states that teams during basketball games should have approximately a similar number of possessions per game. As of today, the basketball industry has tools and formulas for counting specific game-related statistic measures (Alamar, 2013; Nikolaidis, 2015; O'Donoghue, 2009). Points per possession can be found on scientific literature (Marmarinos, Apostolidis, Bolatoglou, Kostopoulos & Apostolidis, 2016; Bazanov et al., 2006) as well as in more public basketball statistics databases: "Synergy Sports Technology", www.nba.com and www.basketball-reference.com websites, which are one of the biggest ones available. By using this metric, it is possible to make averages on how much a team can generate PPP on offense and defense. Formula for counting ball possessions by Oliver (2004):

Ball possessions = (field-goals attempted) – (offensive rebounds) + (turnover) + 0.4 x (free throws attempted)

Formula for counting Points per Possession:

PPP = (points scored) / (ball possessions);

Offensive Rating. Oliver (2004) in his book “Basketball on Paper” provided an offensive rating formula for teams:

Offensive rating = (points produced) / (ball possessions) x 100;

In other words, an offensive rating is calculated by points scored divided by ball possessions and multiplied by 100 as offensive rating is counter per 100 ball possessions. Metric is widely used by analysts on TV and broadcast as well as at scientific studies. Oliver (2004), in his book used offensive rating for comparing different NBA teams in separate "eras", their performance and differences against other teams. Rubio, Gómez, Cañadas & Ibáñez (2015) used OffRtg metric to analyze and check the reliability of ecological dynamics to describe the dynamics of basketball contest as the interaction of two teams along time. Ibáñez, García, Feu, Lorenzo & Sampaio (2009), used OffRtg to identify changes in game-related statistics that discriminate against the basketball team's winning and losing when playing consecutive games. Conte et al., (2018) used offensive rating as one of the metrics to evaluate game-related statistics and tactical profile in NCAA's men'1 division between winning and losing teams. As metric is used for teams it is also can be dedicated to evaluating individual athlete's performances. Prieto (2017), analyzed offensive and defensive rating differences between rookies and 2nd-year players of the NBA. Metric is also valuable as a performance indicator to decide on the most effective five-man unit on the floor as coaches can then have a better understanding of which players fit together to provide the biggest efficiency and scoring capabilities as well as lead to more victories. Efficiency success percentage, points per possession and offensive rating metrics are considered as efficiency measurements on the basketball court, but should be used differently and provide researchers, fans, analysts, coaches and players variant view to numbers. All of the measurements can calculate either team's or player's efficiency regarding game-related statistics, impacting winning or losing as well as coaches can find the most effective player combination on the court. 16

2. METHODOLOGY AND ORGANIZATION

2.1 Research object and aim

Teams on the highest level of basketball tend to have various offensive strategies that depend on the coaching tactics and players on the team. Offensive modalities usage frequencies and their particular efficiencies might provide a general idea of what tendencies teams have in their style of play and in which of the team is most successful. However, the frequency and effective use of certain modalities may be a discriminating factor between winning and losing teams. A statistical analysis of available past data was used to determine basketball offensive trends and modalities influencing the result of a game. Aim of the research: To identify selected offensive modalities usage ratio and efficiency amongst winning and losing team on NBA playoffs.

2.2 Research strategy, logic and nature

While the result of a basketball game cannot be fully decided by analyzing statistical measures of offensive modalities, the use of past data can provide more information on the tendencies, differences between winning and losing teams. To determine offensive modalities usage ratio and efficiencies on the result of a basketball game, a collection, analysis and calculation of such data was done: 1. Scientific literature review and understanding of offensive modalities and available efficiency metrics. 2. Team types were determined by winning and losing percentage formula (WIN%; LOSS%):

WIN% = (games won/ total games) *100 LOSS% = (games loss/ total games) *100

Winning team is considered as 50% or more games won, losing team – less than 50% of games won. 3. Offensive modalities for this research were chosen from the provided “Synergy Sports Technology” software database (listed in a sample of variables). Firstly, the usage ratio data from each game of winning and losing teams were collected. Secondly, data of scoring

effectiveness were collected from each team offensive modalities efficiency table, where averages of all games played in 2018-2019 were listed. 4. Data analysis and comparison for offensive modalities usage ratio and efficiency of all teams as well as scoring productivity between winning and losing teams was done using Microsoft Excel. Statistical descriptive analysis for comparison between winning and losing teams of modalities usage ratio was done by SPSS Statistics analysis software.

Nature of research: This research is based on quantitative analysis involving statistical data processing of offensive, modalities frequencies, complemented by efficiency data provided by “Synergy Sport Technology” database.

2.3 Research sample, variables, tool and statistical analysis methods

Research Sample: A sample of 82 total games during the 2018-2019 NBA playoffs were taken. During NBA playoffs 16 teams from Western (8) and Eastern conferences (8) are playing for four wins in the series starting from conferences quarterfinal going to a semi-final, conference final and NBA final. Series can take maximum up to seven games. Winners have played 123 games combined, when defeated teams – 41. "Synergy Sports Technology" database did not provide Brooklyn "Nets" team post-up data for usage ratio and efficiency, therefore post-up modality has logs from 36 games. Sample of variables: Offensive modalities for this research were chosen from provided “Synergy Sports Technology” software database:

Isolation - When the possession ending event is created during a "one on one" matchup. The defender needs to be set and have all of his defensive options at the initiation of the play. Transition – When the possession ending event comes before the defense sets following a possession change and a transition from one end of the court to the other. Post-up – When an offensive player receives the ball with their back to the basket and is less than 15' from the rim when the possession ending event occurs Spot-up – When the possession ending event is a catch and shoot or catch and drive play. Cut – An interior play where the finisher catches a pass while moving toward, parallel to or slightly away from the basket. This will include the back screen and flash cuts.

Hand-off – The screen setter starts with the ball and hands the ball to a player cutting close by. This enables the player handing the ball off to effectively screen off a defender creating space for the player receiving the ball. Off-screen offense – Identifies players coming off of screens (typically downs screens) going away from the basket toward the perimeter. This includes curl, fades, and coming off straight. Pick and Roll (ball handler) - A screen is set on the ball handler's defender out on the perimeter. The offensive player can use the screen or go away from it and as long as the play yields a possession ending event, it is tagged as a pick and roll. Pick and Roll (roll man) – When a screen is set for the ball handler, and the screen setter then receives the ball for a possession ending event. This includes: pick and rolls, pick and pops, slips pick. Putbacks – When the rebounder attempts to score before passing the ball or establishing themselves in another play type. Miscellaneous - When the action doesn't fit any of the other play types. This includes, but is not limited to, last-second full-court shots, fouls in the backcourt, or errant passes not out of a different play type, etc.

Research tool: Data is collected from “Synergy Sports Technology” software paid subscription, one of the most used databases between professional basketball teams. This database provides every team's and player's game data for advanced statistical metrics. Measures are provided and calculated dividing game video to separate clips and calculating data (Oliver, 2013). Statistical analysis methods: Statistical analysis method presents descriptive statistic measures (mean, std. dev., min, max results) of each variable. The quantitative usage ratio comparison between winning and losing team is tested by the T-test of independent samples in each of eleven variables, where differences were significant if p<0.05. The frequency ratio of offensive modalities was afterward supported by relative parameters of their efficiency. The statistical processing was performed by the program SPSS Statistics.

3. RESULTS

This part of the research results of statistical data analysis will be presented. All data is collected “Synergy Sports Technology” database (https://shop.synergysportstech.com). Eleven offensive modalities were chosen for this research as all of them are listed and categorized on “Synergy Sports Technology” software. Winning teams were the ones who advanced to further rounds and played more games in total (123) compared to defeated teams (41).

Table 1. Offensive modalities usage ratio characteristics in possessions per game

W L W L W L W L W L Descriptive stats. GP Mean St. Dev Min Max Isolation 123 41 9.88 7.15 6.22 4.27 1 0 31 20 Transition 123 41 17.85 15.46 5.91 5.08 7 4 37 28 Post up 123 36 5.67 6.47 3.17 3.68 0 0 16 16 Spot up 123 41 23.83 23.54 6.13 6.92 12 10 40 37 Cuts 123 41 8.93 6.15 4.41 2.71 1 2 21 15 Handoff 123 41 5.33 5.24 3.27 2.60 0 1 16 10 Off screen 123 41 4.89 4.90 3.65 2.23 0 1 19 9 P&R (ball handler) 123 41 15.97 23.15 5.51 5.47 5 13 36 37 P&R (roll man) 123 41 5.85 7.98 2.94 3.35 0 2 14 16 Misc. 123 41 6.71 5.90 3.26 2.67 1 2 16 13 Putbacks 123 41 5.67 5.61 3.07 2.84 1 0 19 12

By analyzing descriptive statistics calculated on SPSS Statistics it is possible to determine some of the recurring tendencies of offensive modalities ratio during the 2018-2019 NBA playoffs. Teams were mostly relying on isolation (13.14 vs 8.68), transition (17.85 vs 15.46), spot-up shooting (23.83 vs 23.54) and pick and roll ball handler (28.71 vs 41.34) offensive modalities, while other finishing actions were used in a lesser capacity, which is no more than 8.93 times on average. Quite big ranges were found in the offensive modalities ratio Min and Max results as both type teams did not use at all or use just a few possessions per game of particular finishing action, except spot-up and pick and roll (ball handler) actions were still consistently used in higher number of possessions. The highest ranges of winning teams were found for pick and roll ball handler (5-36) and isolation (1-31) possessions per game, while lowest ones were found amongst losing teams on off-screen offense (1-9) and hand-off (1-10). Results also showed that all modalities on some games were executed way above or below their averages for both type teams. Dominant categories in this parameter were isolation, transition, spot-up, pick and roll ball handler. 20

Summing up descriptive parameters results it is clear that most dominant modalities were – isolation, transition, spot-up, pick and roll ball handler, while other modalities did not reach more than at least 9 possessions per game, which can indicate a general idea what offensive tendencies most of the teams had during 2018-2019 NBA playoff run.

3.1 Usage ratio of basketball offensive modalities

Analysis of overall usage of offensive modalities of all 16 teams offenses showed what part in percentages of their overall offense each modality take.

25% 21.33% 20% 17.60% 15.00% 15%

10% 7.67% 6.80% 6.22% 5.47% 5.68% 4.76% 4.41% 5.08% 5%

0% Isolation Transition Post Up Spot Up Cuts Hand off Off screen Pick and Pick and Misc Putbacks offense roll ball roll roll handler man

Figure 1. Usage ratio distribution between all teams of 2018-2019 NBA playoffs

Results showed that the most commonly used modality was Spot-Up (21.33%), the second modality was Pick and Roll ball handler (17.60%) and Transition (15%). The first three categories showed expected results as today's basketball game is based on a fast pace, high frequency of 3-point shooting and Pick and Roll game by guards. Isolation (7.67%) and cuts (6.80%) were used in a lesser ratio. Pick and Roll by roll man (6.22%) action shows that in today's NBA guards are finishing these plays more often than screeners. 5.68% of offensive possessions end up in the miscellaneous category. Post-Up (5.47%), putbacks (5.08%) action and hand-off (4.76%) showed similar results. Off-Screen offense (4.41%) was rarely used in coaches strategies. Concluding results of offensive modalities usage ratio in overall team offenses it is clear that spot-up, pick and roll ball handlers were the most used modalities. To add more, if Pick and Roll 21 action would be combined as a whole (ball handler and roll man finishes) then this action would become the most used one during NBA playoffs (23.82%).

3.2 Efficiency of basketball offensive modalities

Calculations of overall offensive modalities efficiencies of all teams during NBA playoffs showed that Cuts to the basket were the most efficient way to score a basketball (59.97%), which indicates that moving without a ball is a significantly important offensive modality to follow. Putbacks has 51.23% of efficiency and is one of the most effective modalities as well. Pick and Roll (roll man) action shows 50.34% efficiency as pick and roll ball handler finishes results in 40.16% scoring frequency. If combined pick and roll action results in 45.25% of score frequency, but a difference between roll man and ball-handler scoring efficacy differs by almost 10%.

70.00% 59.97% 60.00% 49.62% 50.34% 51.23% 50.00% 42.45% 41.68% 40.16% 40.00% 37.79% 37.59% 36.43% 31.26% 30.00%

20.00%

10.00%

0.00% Isolation Transition Post Up Spot Up Cuts Hand off Off screen Pick and Pick and Misc Putbacks offense roll ball roll roll handler man

Figure 2. Offensive modalities efficiency distribution of all teams of 2018-2019 NBA playoffs

As visible in the (Figure 2) transition modality generated 49.62% score frequency. Results showed that isolation play type was finished at 41.68% efficiency. It is worth mentioning that some teams played a low number of isolation possessions compared to others, but had a significantly higher score frequency. Post-up (42.45%) and spot-up (37.79%) showed similar results in efficiency as post play is used in a lesser capacity during NBA playoffs, but were more effective play than catch and shoot action which is widely used these days. Handoff (37.59%) and off-screen offense (36.43%) as

22 well as miscellaneous (31.26%) had the lowest efficiency, but were just slightly less effective than Post up and Spot up actions. Results of all playoff team's efficiencies for offensive modalities showed that finishing actions, which were used in lesser ratio have been more effective rather than most used modalities.

3.3 Offensive modalities usage ratio between winning and losing teams

Analysis of offensive modalities usage ratio distribution amongst winning and losing teams showed that nine out of eleven modalities were in favor of winning teams. Two modalities that losing teams had an advantage was both pick and roll types (ball-handler, roll man). Significant differences were found in five of the modalities, which can be seen in Table 2.

Table 2. Offensive modalities usage ratio differences analysis between losing and winning teams t-test t df p Isolation 2.61 162 0.01 Transition 2.32 162 0.02 Post up -1.28 157 0.20 Spot up 0.26 162 0.80 Cuts 3.81 162 0.00 Handoff 0.14 162 0.89 Off screen -0.01 162 0.99 P&R ball handler -7.24 162 0.00 P&R roll man -3.86 162 0.00 Misc. 1.43 162 0.16 Putbacks 0.12 162 0.90 Note: *p<0.05

23 and roll action involving ball handler or roll man and used in high capacity has not helped teams to win more games. Isolation is one of the modalities that were used frequently in the team's offenses and this might have been caused by the change of the game as guard play became dominant in the team's offenses using their speed. For the winning team's isolation plays were used 8.93% of the time versus 6.41% for losing teams. It is worth mentioning that Houston "Rockets" were the team playing the most isolation offense of all playoff teams (22.4%). Differences between winning and losing teams in this category showed significant differences in favor of winners. Best teams in the NBA usually have one or two individually exceptional players who are effective in these situations, while teams without all-star caliber players are running more team-oriented offense with more movement and ball passing. Naturally teams then want to play in their strengths and use their star player's abilities in more possessions for increasing chances to win games. Transition offense was the third most used modality during the 2018-2019 NBA playoffs. Winning teams showed a slight advantage in the usage ratio (16.14% vs 13.86%) against defeated teams, but those differences were a significant factor in terms of deciding the result of a game. This consents with Trninić et al., (1995) results as winners used this modality more frequently. According to Selmanović et al., (2015) findings, most of the transition offenses starts immediately after a missed field goal attempt, defensive rebound or a steal. Being able to have more transition possessions is a result of opponents shooting accuracy, rebounding and team defense, which also has to be a factor why more transition possessions results in more wins. A significant factor to team's success was found in Cuts usage ratio, where winners (8.08%) have been using modality more than defeated teams (5.51%), which coincides with findings from Selmanović et al., (2015) and Zukolo et al., (2019) as cuts was found as one of the offensive modalities that are more frequently executed by winning teams. Cuts involves player movement, setting screens and taking advantage of the opponent's defensive strategy or mistakes. While cuts are not used in a high amount of possessions compared to other offensive modalities, but statistical analysis showed the importance of modality in terms of influencing result of a basketball game. Looking at a statistical analysis table (t-test) it is visible that 6 of the 11 modalities have not shown any differences between winning and losing teams in terms of their usage ratio. Spot-up (21.55% vs 21.10%) were in favor of winning teams, as well as post-up (6.18% vs 5.78%) against defeated teams. which matches Zukolo et al. (2019) results for those modalities as a very slim difference in terms of usage averages. Hand-off (4.82% vs 4.70%) as well as off-screen offense (4.43% vs 4.39%) was also in favor of winning teams, but no significant differences were found. Winning teams had a slight advantage on putbacks (5.13% vs 5.03%), but has no significant effect 24 on deciding the winner of a game, even though according to Özmen (2016) results, Euroleague team who grabs at least 1 offensive rebound more than opponents has a 6,3% higher chance to win a game. According to “Synergy Sports Technology” glossary, miscellaneous modality includes any other finishing act other than listed modalities, which includes half-court shots, fouls drawn, errant passes, etc. Those kinds of plays were in favor of winning teams (6.07% vs 5.29%), but also had no significant influence on the result of a game.

25%

21.55%

21.10% 20.75%

20%

16.14% 14.44%

15% 13.86%

10% 8.93%

8.08%

7.15%

6.41%

6.07%

5.80%

5.51%

5.29% 5.29%

5.13% 5.13%

5.03%

4.82%

4.70% 4.43% 5% 4.39%

0% Isolation Transition Post Up Spot Up Cuts Hand off Off screen Pick and Pick and Misc Putbacks offense roll ball roll roll handler man

WIN LOSS

Figure 3. Distribution of offensive modalities usage ratio between winning and losing teams

3.4 Offensive modalities efficiency between winning and losing teams

Scoring efficiency of selected offensive modalities display what percentage of specific action was successful during the whole 2018-2019 NBA playoffs between winning and losing teams. Analysis of the offensive modalities scoring efficiency showed that 8 out of 11 studied finishing actions were in favor of winning teams, while defeated teams showed an advantage in post-up, hand- off, pick and roll (roll man) modalities (Figure 4). The biggest differences were logged between off-screen offense efficiency (42.24% vs 30.63%) in favor of winning teams, which also matches results with Zukolo et al., (2019) findings. Bazanov et al., (2006) results showed that off-ball screen efficiency differs due to the number of off screens used during possession as offenses with two ball screens had 44% scoring efficiency, while 25 using one screen had 56% and four screens had 50% efficiency. Miscellaneous modality showed the second biggest difference (35.79% vs 26.86%), which shows that winning teams were more efficient in non-categorized finishes: half-court shots, shooting free throws.

70% 62.64%

60%

55.30%

54.48%

54.31%

49.96% 49.28%

50% 47.21%

46.38%

43.27%

42.24%

41.78%

41.73%

41.61%

41.53%

40.80%

39.53% 38.43%

40% 37.16%

36.49%

35.79% 30.63%

30% 26.86%

20%

10%

0% Isolation Transition Post Up Spot Up Cuts Hand off Off screen Pick and Pick and Misc Putbacks offense roll ball roll roll handler man

WIN LOSS

Figure 4. Distribution of offensive modalities scoring efficiency between winning and losing teams

Cuts (62.64% vs 54.48%) showed a significant efficiency for both type teams as usually it is a result of player movement without a ball and scoring wide open close to the basket. Putbacks (55.30% vs 47.21%) also showed high-efficiency results comparing to other offensive modalities even though Özmen (2016) findings show that Euroleague team who grabs at least 1 offensive rebound more than opponents has a 6,3% higher chance to win a game and considering that most of the offensive rebounds occur close to the basket, it is easier to score from those rebounds. Other data did not provide big differences between winning and losing teams. Winners had a slight advantage with isolation (41.78% vs 41.61%), transition (49.96% vs 49.28%), spot-up (38.43% vs 37.16%) and pick and roll (ball handler) (40.80% vs 39.53%) offensive modalities efficiency in their offense. Defeated teams showed that they have been more effective on a post-up play (43.27% vs 41.73%), hand-off (41.53% vs 36.49%), and pick and roll (roll man) (54.31% vs 46.48%). Results show that winning teams were dominant in most of the offensive modalities listed and highly efficient in actions including moving without the ball, going to the free-throw line, making free throws and being able to generate points from offensive rebounding compared to defeated teams.

4. CONSIDERATIONS

This research aimed to identify selected offensive modalities usage ratio and efficiency amongst winning and losing team on NBA playoffs, which was achieved with data collected from the “Synergy Sports Technology” database and statistical analysis. However, while usage ratio data were available for separate games, efficiency numbers consisted of scoring frequency averages of all winning and all losing teams. In this situation, a WIN% and LOSS% formulas were used to determine winning and losing teams of 2018-2019 NBA playoffs. Firstly, the total usage ratio distribution of offensive modalities amongst all teams were found, which provided information about overall offensive tendencies during last year's NBA playoffs. Secondly, efficiency numbers showed that part of the rarely used modalities were more efficient than frequently used ones. Finally, a comparison of offensive modalities usage ratio and efficiency amongst winning and losing teams were done together with analysis of main and significant differences. With statistical data analysis of the offensive modalities ratio, it was confirmed that there are significant differences between winning and losing teams, while modalities efficiency differences could not have been fully confirmed due to data collection limitations and proper statistical analysis. Collected data of usage ratio led to the results that five out of eleven selected offensive modalities showed significant differences between winning and losing teams. Davenport (2014), stated that coaches and managers these days believe in analytical offensive approach where team mostly shoots three points and near the basket shots and by analyzing data it is visible that spot-up shooting concludes the big part of the offense for both type teams together with pick and roll ball handler finishing action. As both type teams focus on the same modalities, winning teams supposedly should have had an advantage in the modalities which require better individual skills, teamwork or game plan strategy. In the playoffs coaches' goal is to prepare the most effective game plan against an opponent, but also took advantage of their game strategy, offensive structure and habits created during the whole regular season. Winning teams offensive usage ratio of isolation, transition and cuts offensive modalities were higher than defeated teams and also showed significant differences. Coaches were able to find strategies where their team was able to create more opportunities for transition possessions due to good team defense, opponent's shooting accuracy and rebounds (Christmann et al., 2018). This coincides with Trninič et al., (1995), results as winners used this modality more frequently. However, Selmanović et al., (2015) show that transition was used 20.23% of the time during 15 randomly selected NBA playoff games on the 2010/2011 season, but during 2018-2019 NBA playoff results show that this number has reduced to 15%. Isolation differences

27 could have been expected as best teams in the NBA nowadays usually has one or two individually exceptional players who are effective in these situations and coaches rely on their abilities. Houston "Rockets" were the team playing the most isolation offense of all playoff teams (22.4%). Cuts is an indication of winning teams taking advantage of the opponent's defense by moving the ball, executing their sets, settings screens and player movement on the floor. Selmanović et al., (2015) and Zukolo et al., (2019) in their studies found that cuts were also used more by winning teams, which can be a strong indication of the importance of modality and influence to the result of a game, especially when it usually ends with an easy basket, even though Zukolo et al., (2019) showed no significant differences. Worth to mention that Selmanović et al., (2015) provided data showed that NBA teams during 2010/2011 playoffs used Cuts as one of their offensive modality with a 12.27% ratio, which is visible drop during 2018-2019 NBA post-season. This might be a result of teams focusing more on long-range shooting, close to the basket shots and pick and rolls for ball handler or roll man. Both analyzed pick and roll modalities (ball handler and roll man) showed significant differences in the usage ratio, but in favor of losing teams. The pick and roll ball handler was a dominant modality in terms of ratio. These results might be the indication of guard dominant game in today's basketball as players use their speed, quickness and shooting ability to score. According to Remmert & Chau (2019) out of 1008 analyzed half-court ball screens and 89.4% of them were done to the ball handler. To add, Lamas et al., (2011) found that during the 2008 Olympic Basketball tournament out of all half-court possessions 68.7% ended in scoring opportunities from which 34.8% was after on ball screens. Roll man action was significantly run more often by losing teams as best teams in the NBA do not rely on centers to make plays anymore. None of the best four teams in the 2018-2019 NBA playoffs had a dominant post player. Comparing differences of winning and losing teams Zukolo et al., (2019) counted all pick and roll actions combined and did not find any significant variations. More than half of modalities (6) showed no differences between winning and defeated teams. They were used in lesser capacity overall, except spot-up shooting modality, which showed almost identical numbers for both type teams and perfectly matches with Zukolo et al., (2019) results. Spot- up shooting action is widely used due to the analytical approach as teams focus on open long-range shots. Chang et al., (2014) looked at shots off the dribble and catch-and-shoot situations and found that spot-up situations had a positive and significant difference in accuracy against shots after the dribble Post-up, off-screen offense, miscellaneous and putbacks results can be compared to Selmanović et al., (2015) as similar overall usage ratio percentages were found for NBA teams during 2010-2011 playoffs, but it is worth to mention that hand-off action was used almost twice during 2019 playoffs compared to authors findings.

Offensive modalities efficiency hypotheses could not have been fully confirmed as accessible data was not sufficient for statistical analysis (chi-square), due to lack of sample size, which should at least reach 30 (Vaitkevičius & Saudargienė, 2006), while “Synergy Sports Technology” can provide only separate team offensive modalities efficiency averages of all playoff games played. While any significant differences could not have been tested, but winning teams showed advantages in eight out of eleven selected modalities. The biggest differences in scoring frequency percentages for winning teams is detectable for off-screen offense, miscellaneous, putbacks and cuts, which correlates with Zukolo et al., (2019) results where differences for off-screen offense, cut, putbacks were similar of winners and defeated teams. Bazanov et al., (2006) results showed that off-ball screen efficiency differs due to the number of off screens used during possession as offenses with two ball screens had 44% scoring efficiency, while using one screen had 56% and four screens had 50% efficiency. Lamas et al., (2011) found that during the 2008 Olympic Basketball tournament 9.6% of half-court sets scoring came from using off-ball screening. Miscellaneous modality differences might show non-categorized possession endings as well drawing fouls in the backcourt and going to the free-throw line, even though according to Trninič et al., (2002) and Kreivytė & Čižauskas (2010) a number of free throws attempted per game had more influence to winning rather than being more accurate from the line. Cut modality efficiency is an indication of player movement and being able to take advantage of the opponent's defensive mistakes to score or pass to an open teammate. Both type teams showed high efficiency, but winners had an advantage, which coincides with Zukolo et al., (2019) findings. Also, the authors stated that cut modality is the most effective when 2 or 3 players are included. Scoring from putbacks modality is highly correlated with offensive rebounding as “Synergy Sports Technology” differentiates offensive rebounds for offense reset or putback ending. Özmen's (2016) findings show that the Euroleague team who grabs at least 1 offensive rebound more than opponents has a 6,3% higher chance to win a game. Putbacks was also found as one of the most efficient modality at Zukolo et al., (2019) as well as respective efficiency percentages in favor of winning teams. Isolation, transition, pick and roll ball handler showed very small differences in efficiency, even though all modalities were found having a significant difference in terms of usage ratio. Spot- up shooting was the most used modality, but comparing to other results fall behind in terms of efficiency, which coincides with Zukolo et al., (2019) findings. Winning teams were slightly more efficient on finishing pick and roll ball handler actions, but considering that losing teams were using modality significantly more, the total amount of points generated per game is higher. A similar study of Zukolo et al., (2019) found that efficiency on 9 out of 10 modalities were in favor for winning teams and pick and pop action being equal, while on this research it was found that post-up, hand- 29 off, pick and roll (roll man) finishing actions was on the defeated team's side. According to Zhang et al., (2019) winning teams centers generated more points from the two-point field goal are, but considering this study results defeated teams were generating more points from this modality as significant differences were also found in the usage ratio. Most of the deficiencies in this research occur due to limitations of data collection from available databases in the field. “Synergy Sports Technology” is one of the most widely used software of professional teams in whole world leagues including NBA, Euroleague and other European leagues, but it was lacking structure to provide offensive modalities efficiency data from separate games, while usage ratios were available for each game. Due to limitations, WIN% and LOSS% formulas were selected to indicate winning and losing teams as each game usage ratio and efficiency of winners and losers could not have been compared equally. Due to lack of researches done in the field so far in terms of analyzing similar modalities most of the results as compared to fairly new Selmanović et al., (2015) and Zukolo et al., (2019) studies of offensive modalities role in team's offense and productivity, even though smaller sample sizes were used compared to this research. Improvements for future research might be done with access to more detailed databases or choosing smaller sample sizes and analyzing videotape, but specific and accurate modalities descriptions must be followed as basketball consists of numerous possible actions, movements and decisions on the floor. Also, a study of analyzing defense against offensive modalities can be done as then team offense and defense influence on winning is examined.

CONCLUSIONS

1. The three most dominant modalities in terms of usage ratio are spot-up shooting, pick and roll ball handler and transition actions, however, the biggest efficiency was also found of offensive modalities used in a lesser capacity. Cuts and putbacks were the most efficient modalities for 16 NBA teams during the 2018-2019 NBA playoffs. However, as Pick and roll ball handler and Transition were used in high capacity results show high efficiency as well as comparing to other results. NBA teams follow analytical strategy trends and tend to rely on long-range shooting, shots to close to the basket and easy baskets, which are created by transition fast breaks.

2. Comparing winning and losing teams offensive modalities usage ratio significant differences were found in isolation, transition and cuts in favor of winners. Defeated teams showed significant usage ratio advantage on finishing their offensive possessions with pick and roll ball handler and pick and roll (roll man) actions. Statistical analysis showed that the other 6 offensive modalities ratio was not notably different between winning and losing teams. Both type teams play in similar offensive structures, but winners were able to create more fast- break opportunities due to their defensive presents and rebounds, move without a ball and take advantage of opponent's defensive mistakes as well as play more one-on-one basketball handing the ball to the most skilled team players.

3. Winning teams showed higher efficiency in eight out of eleven offensive modalities. Biggest differences were found in off-screen offense, miscellaneous, putbacks and cuts modalities. Results conclude that off the ball movement and executing team sets correctly has a big impact on overall offensive efficiency even though modalities were not used often. Being able to make tough shots that do not fall into any of categories, drawing fouls, making free-throws and taking more offensive rebounds for opportunities to score influence game score. Defeated teams showed higher efficiency in post-up, hand-off and pick and roll (roll man) modalities, which indicates that big man play even though can be efficient but did not result in winning more games.

RECOMMENDATIONS

1. There are numerous basketball researches done for specific on-court actions, statistical measures, but lacking offensive modalities studies, where they would have been analyzed collectively and in a high sample size.

2. “Synergy Sports Technology” software and database is a valuable tool for professional teams worldwide (NBA, Euroleague, European leagues, etc.), which provides various and detailed data, but data collection limitations were the main reason why this research could not have been analyzed in a more detailed way. Another database where separate games offensive modalities usage ratio and efficiency statistics can be found should be used. To add more, video analysis could be used as well, but it is highly time-consuming and specific and accurate modality descriptions must be agreed as basketball analytics consists of numerous variables.

3. Researches towards offensive modalities can be valuable for coaches and decision-makers on teams regarding collecting team rosters or creating team strategies and tactics. Having this in mind, it is encouraging that new studies on how team defense characteristics influence end game results against offensive modalities should be done.

REFERENCES

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ANNEXES

Annex A

PRODUCT Suggested scientific article to “Baltic Journal of Sport and Health Sciences” ISSN: 2351-6496/ eISSN: 2538-8347

OFFENSIVE MODALITIES AND THEIR INFLUENCE ON BASKETBALL EFFICIENCY BETWEEN WINNING AND LOSING TEAMS

Corresponding author Julius Demenius Lithuanian Sports University Sporto str. 6, LT-44221 Kaunas Lithuania Tel. +37063070175 Email [email protected]

ABSTRACT Background: Alamar (2013) in his book about sports analytics for coaches and other decision-makers in basketball started by stating that analytics basketball is a new and evolving set of tools that are commonly used to provide a competitive advantage. In recent years, basketball is highly analyzed, which provides coaches valuable data of efficiency, player or team performance during a game or whole season (Mikołajec, Maszczyk, Zając 2013). One of the areas that lacking scientific researches is offensive modalities, their usage and efficiency between winning and losing teams in the most important stretch of basketball season – playoffs. Methods: Offensive modalities for this research were chosen from the provided “Synergy Sports Technology” database and software. WIN% and LOSS% were used to determine winning and losing teams on NBA playoffs. Significance was tested with the SPSS Statistics analysis program. Results: Three most used modalities were found to be: spot-up, pick and roll ball handler and transition, while most efficient ones were cut and putbacks. Results show that significant differences were found in isolation, transition and cuts finishing action for winning teams and pick and roll ball handler as well as roll man for defeated teams. Efficiency differences for winning teams were found in off-screen offense, miscellaneous, putbacks and cuts modalities. Conclusions: NBA teams follow analytical strategy trends and tend to rely on long-range shooting, shots to close to the basket and easy baskets. Both type teams play in similar offensive structure, but winners were able to create more fast-break opportunities due to their defense and rebounding. Off the ball movement and executing team sets correctly has a big impact on overall offensive efficiency even though modalities were not used often.

Keywords: basketball, statistical analysis, finishing actions.

INTRODUCTION According to Alamar (2013) analytics in basketball is a new and evolving set of tools that is being used more commonly in the industry, which should also provide a competitive advantage for decision-makers by using statistical analysis. In recent years, basketball is highly analyzed, which provides coaches valuable data of efficiency, player or team performance during a game or whole season (Mikołajec, Maszczyk, Zając 2013). Nikolaidis, (2013) added that basketball in the US and mostly in major league NBA (National Basketball Association) is highly analyzed by the statistics, various data measurements and quantitative techniques. However, Goldsberry (2012) suggests statistical analysis cannot be effective without spatial analysis and decision-makers should combine both. While the game of basketball is changing rapidly coaches need to keep up with new trends and create offensive strategies for efficient scoring to gain competitive advantage. Zukolo, Dizdar, Selmanović & Vidranski (2019) in collective research about the role of finishing 40 actions in the final result of basketball match distinguished 11 possession finishing actions and used it for prediction of team performance: isolation, post-up, spot-up, cuts, handoff, pick and pop, off-screen offense, pick & roll, putbacks, free throws, other actions. Bazanov, Võhandu, Haljand (2006) in their work analyzed various offensive modalities and their efficiencies at different level leagues and Zukolo et al., (2019), determining differences of finishing actions ratio and efficiency between winning and losing teams in European championship on 2013, while Selmanović, Škegro & Milanović (2015) focused their study on offensive modalities usage frequencies and comparison between European and American professional leagues. In this paper, a distribution of offensive modalities and their efficiency between winning and losing teams in NBA playoffs, where playing level rises as regular season (82 games) by fans, coaches, analysts are not considered seriously. Oliver (2004) introduced "Four-factors", where shooting, turnovers, rebounds and free throws are considered as main categories that have the biggest impact between winning and losing teams. While researches about basketball efficiency can be found, but there is still a lack of analysis of offensive modalities and their direct influence between winning and losing teams on the highest basketball level. Aim of the study: To determine which offensive modalities have most influence between winning and losing teams on NBA playoffs. Hypothesis 1: There will be differences between winning and losing teams regarding offensive modalities usage ratio. Hypothesis 2: There will be differences between winning and losing teams regarding offensive modalities efficiency. METHODS Research Sample: A sample of 82 total games during the 2018-2019 NBA playoffs were taken. During NBA playoffs 16 teams from Western (8) and Eastern conferences (8) are playing for four wins in the series starting from conferences quarterfinal going to a semi-final, conference final and NBA final. Series can take maximum up to seven games. Winners have played 123 games combined, when defeated teams – 41. Procedure: Data was collected from “Synergy Sports Technology” software paid subscription, one of the most used databases between professional basketball teams. This database provides every team's and player's game data for advanced statistical metrics. Measures: The main objective was to identify offensive modalities, which was done following “Synergy Sports Technology” database provided play types: isolation, transition, post-up, spot-up, cut, hand- off, off-screen offense, pick and roll (ball-handler), pick and roll (roll man), putbacks, miscellaneous. Offensive modalities usage ratio data of each game was collected and distributed between winning and losing teams. Efficiency averages of each playoff participant teams were collected and distributed amongst winning and losing teams. Team types were determined by winning and losing percentage formula (WIN%; LOSS%): 41

WIN% = (games won/ total games) *100; LOSS% = (games loss/ total games) *100. Winning team is considered as 50% or more games won, losing team – less than 50% of games won. Data analysis: Data was analyzed using SPSS Statistics analysis software. Usage ratio and efficiency data averages were calculated first. Then usage ratio data mean and ranges were calculated as well as significance was compared between winning and losing teams using statistical analysis t-test, where significant differences were found if p<0.05. Offensive modalities efficiency data was compared by winning and defeated teams scoring frequency data averages.

RESULTS By analyzing descriptive statistics calculated on SPSS Statistics it is possible to determine some of the recurring tendencies of offensive modalities ratio during the 2018-2019 NBA playoffs. Teams were mostly relying on isolation (13.14 vs 8.68), transition (17.85 vs 15.46), spot-up shooting (23.83 vs 23.54) and pick and roll ball handler (28.71 vs 41.34) offensive modalities, while other finishing actions were used in a lesser capacity, which is no more than 8.93 times on average.

1 table. Offensive modalities usage ratio characteristics per game

Quite big ranges were found in the offensive modalities ratio Min and Max results as both type teams did not use at all or use just a few possessions per game of particular finishing action, except spot-up and pick and roll (ball handler) actions were still consistently used in higher number of possessions. The highest ranges of winning teams were found for pick and roll ball handler (5-36) and isolation (1-31) possessions per game, while lowest ones were found amongst losing teams on off-screen offense (1-9) and hand-off (1- 10). Results also showed that all modalities on some games were executed way above their averages for 42 both type teams. Dominant categories in this parameter were again isolation, transition, spot-up, pick and roll ball handler. Results showed that the most commonly used modality was Spot-Up (21.33%), the second modality was Pick and Roll ball handler (17.60%) and Transition (15%). The first three categories showed expected results as today's basketball game is based on a fast pace, high frequency of 3-point shooting and Pick and Roll game by guards. Isolation (7.67%) and cuts (6.80%) were used in a lesser ratio. Pick and Roll by roll man (6.22%) action shows that in today's NBA guards are finishing these plays more often than screeners. 5.68% of offensive possessions end up in the miscellaneous category. Post-Up (5.47%), putbacks (5.08%) action and hand-off (4.76%) showed similar results. Off-Screen offense (4.41%) was rarely used in coaches strategies. Calculations of overall offensive modalities efficiencies of all teams during NBA playoffs showed that Cuts to the basket were the most efficient way to score a basketball (59.97%), which indicates that moving without a ball is a significantly important offensive modality to follow. Putbacks has 51.23% of efficiency and is one of the most effective modalities as well. Pick and Roll (roll man) action shows 50.34% efficiency as pick and roll ball handler finishes results in 40.16% scoring frequency. If combined Pick and Roll action results in 45.25% of score frequency, but a difference between roll man and ball-handler scoring efficacy differs by almost 10%. Transition modality generated 49.62% score frequency. Results showed that Isolation play type was finished at 41.68% efficiency. It is worth mentioning that some teams played a low number of isolation possessions compared to others, but had a significantly higher score frequency. Post-up (42.45%) and Spot- up (37.79%) showed similar results in efficiency as post play is used in a lesser capacity during NBA playoffs, but were more effective play than catch and shoot action which is widely used these days. Handoff (37.59%) and off-screen offense (36.43%) as well as Miscellaneous (31.26%) had the lowest efficiency, but were just slightly less effective than Post up and Spot up actions. Analysis of offensive modalities usage ratio distribution amongst winning and losing teams showed that nine out of eleven modalities were in favor of winning teams. Two modalities that losing teams had an advantage was both pick and roll types (ball-handler, roll man). Significant differences were found in five of the modalities, which can be seen in Table 2.

Cuts 3.81 162 0.00 Handoff 0.14 162 0.89 Off screen -0.01 162 0.99 P&R ball handler -7.24 162 0.00 P&R roll man -3.86 162 0.00 Misc. 1.43 162 0.16 Putbacks 0.12 162 0.90 Note: *p<0.05

Pick and Roll ball handler modality was the second most used finishing action for both type teams, which in part complies with Wang et.al (2009) findings that pick and roll action is the most commonly used action in professional and amateur level. As visible in (Figure 1), the pick and roll ball handler shows the biggest difference between winners and defeated teams (14.44% vs 20.75%). By statistical analysis it was also found that pick and roll ball handler modality differences were significant (p<0.05). To add more, pick and roll (roll man) were used in a lesser capacity than ball handler action by winners and losers (5.29% vs 7.15%), but results showed significant differences as well. In today's NBA guards are finishing these plays more often than screeners. By the results pick and roll action involving ball handler or roll man and used in high capacity has not helped teams to win more games. Isolation is one of the modalities that were used frequently in the team's offenses and this might have been caused by the change of the game as guard play became dominant in team's offenses using their speed and quickness. For the winning team's isolation plays were used 8.93% of the time versus 6.41% for losing teams. It is worth mentioning that Houston "Rockets" were the team playing the most isolation offense of all playoff teams (22.4%). Differences between winning and losing teams in this category showed significant differences in favor of winners. Transition offense was the third most used modality during the 2018-2019 NBA playoffs. Winning teams showed a slight advantage in the usage ratio (16.14% vs 13.86%) against defeated teams, but those differences were a significant factor in terms of deciding the result of a game. This consents with Trninić (1995) results as winners used this modality more frequently. According to Selmanović et al., (2015) findings, most of the transition offenses starts immediately after a missed field goal attempt, defensive rebound or a steal. A significant factor to team's success was found in Cuts usage ratio, where winners (8.08%) have been using modality more than defeated teams (5.51%), which coincides with findings from Selmanović et al., (2015) and Zukolo et al., (2019) as cuts was found as one of the offensive modalities that are more frequently executed by winning teams. Cuts involves player movement, setting screens and taking advantage of an opponent's defensive strategy or mistakes. While cuts are not used in a high amount of possessions compared

44 to other offensive modalities, but statistical analysis showed the importance of modality in terms of influencing result of a basketball game. Looking at a statistical analysis table (t-test) it is visible that 6 of the 11 modalities have not shown any differences between winning and losing teams in terms of their usage ratio. Spot-up (21.55% vs 21.10%) were in favor of winning teams, as well as post-up (6.18% vs 5.78%) against defeated teams. which matches Zukolo et al. (2019) results for those modalities as a very slim difference in terms of usage averages. Hand- off (4.82% vs 4.70%) as well as off-screen offense (4.43% vs 4.39%) was also in favor of winning teams, but no significant differences were found. Winning teams had a slight advantage on putbacks (5.13% vs 5.03%), but has no significant effect on deciding the winner of a game, even though according to Özmen (2016) results, Euroleague team who grabs at least 1 offensive rebound more than opponents has a 6,3% higher chance to win a game. According to “Synergy Sports Technology” glossary, miscellaneous modality includes any other finishing act other than listed modalities, which includes half-court shots, fouls drawn, errant passes, etc. Those kinds of plays were in favor of winning teams (6.07% vs 5.29%), but also had no significant influence on the result of a game.

25%

21.55%

21.10% 20.75%

20%

16.14% 14.44%

15% 13.86%

10% 8.93%

8.08%

7.15%

6.41%

6.07%

5.80%

5.51%

5.29% 5.29%

5.13% 5.13%

5.03%

4.82%

4.70% 4.43% 5% 4.39%

0% Isolation Transition Post Up Spot Up Cuts Hand off Off screen Pick and Pick and Misc Putbacks offense roll ball roll roll handler man

WIN LOSS

Figure 1. Distribution of offensive modalities usage ratio between winning and losing teams

Scoring efficiency of selected offensive modalities display what percentage of specific action was successful during the whole 2018-2019 NBA playoffs between winning and losing teams. Analysis of the offensive modalities scoring efficiency showed that 8 out of 11 studied finishing actions were in favor of 45 winning teams, while defeated teams showed an advantage in post-up, hand-off, pick and roll (roll man) modalities (Figure 2). The biggest differences were logged between off-screen offense efficiency (42.24% vs 30.63%) in favor of winning teams, which also matches results with Zukolo et al., (2019) findings. Bazanov et al., (2006) results showed that off-ball screen efficiency differs due to the number of off screens used during possession as offenses with two ball screens had 44% scoring efficiency, while using one screen had 56% and four screens had 50% efficiency. Miscellaneous modality showed the second biggest difference (35.79% vs 26.86%), which shows that winning teams were more efficient in non-categorized finishes: half-court shots, shooting free throws.

70% 62.64%

60%

55.30%

54.48%

54.31%

49.96% 49.28%

50% 47.21%

46.38%

43.27%

42.24%

41.78%

41.73%

41.61%

41.53%

40.80%

39.53% 38.43%

40% 37.16%

36.49%

35.79% 30.63%

30% 26.86%

20%

10%

0% Isolation Transition Post Up Spot Up Cuts Hand off Off screen Pick and Pick and Misc Putbacks offense roll ball roll roll handler man

WIN LOSS

Figure 2. Distribution of offensive modalities scoring efficiency between winning and losing teams

46 modalities efficiency in their offense. Defeated teams showed that they have been more effective on a post- up play (43.27% vs 41.73%), hand-off (41.53% vs 36.49%), and pick and roll (roll man) (54.31% vs 46.48%). DISCUSSION This research aimed to identify selected offensive modalities usage ratio and efficiency amongst winning and losing team on NBA playoffs, which was achieved with data collected from the “Synergy Sports Technology” database and statistical analysis. However, while usage ratio data were available for separate games, efficiency numbers consisted of scoring frequency averages of all winning and all losing teams. With statistical data analysis of the offensive modalities ratio, it was confirmed that there are significant differences between winning and losing teams, while modalities efficiency differences could not have been fully confirmed due to data collection limitations and proper statistical analysis.

Offensive modalities usage ratio between winning and losing teams Collected data of usage ratio led to the results that five out of eleven selected offensive modalities showed significant differences between winning and losing teams. Davenport (2014), stated that coaches and managers these days believe in analytical offensive approach where team mostly shoots three points and near the basket shots and by analyzing data it is visible that spot up shooting concludes big part of offense for both type teams together with pick and roll ball handler finishing action. As both type teams focus on same modalities, winning teams supposedly should have had an advantage in the modalities which requires better individual skills, teamwork or game plan strategy. In the playoffs coaches goal is to prepare the most effective game plan against opponent, but also took advantages from their game strategy, offensive structure and habits created during the whole regular season. Winning teams offensive usage ratio of isolation, transition and cuts offensive modalities were higher than defeated teams and also showed significant differences. Coaches were able to find strategies where their team was able to create more opportunities of transition possessions due to good team defense, opponent’s shooting accuracy and rebounds (Christmann, Akamphuber, Müllenbach & Güllich, 2018). This coincides with Trninič, Milanović, Blašković, Birkić & Dizdar (1995), results as winners used this modality more frequently. However, Selmanović et al., (2015) shows that transition was used 20.23% of the time during 15 random selected NBA playoff games on 2010/2011 season, but during 2018-2019 NBA playoffs results show that this number has reduced to 15%. Isolation differences could have been expected as best teams in the NBA nowadays usually has one or two individually exceptional players who are effective in these situations and coaches rely on their abilities. Houston “Rockets” were the team playing the most isolation offense of all playoff teams (22.4%). Cuts is an indication of winning teams taking advantage of opponent’s defense by moving the ball, executing their sets, settings screens and player movement on the floor. Selmanović et al., (2015) and Zukolo et al., (2019) in their studies found that cuts were also used more by 47 winning teams, which can be a strong indication of the importance of modality and influence to the end result of a game, especially when it usually ends with an easy basket, even though Zukolo et al., (2019) showed no significant differences. Worth to mention that Selmanović et al., (2015) provided data showed that NBA teams during 2010/2011 playoffs used Cuts as one of their offensive modality with 12.27% ratio, which is visible drop during 2018-2019 NBA post-season. This might be a result of teams focusing more on long range shooting, close to the basket shots and pick and rolls for ball handler or roll man. Both analyzed pick and roll modalities (ball handler and roll man) showed significant differences in the usage ratio, but in favour for losing teams. Pick and roll ball handler was a dominant modality in terms of ratio. These results might be the indication of guard dominant game in today’s basketball as players uses their speed, quickness and shooting ability to score. According to Remmert & Chau (2019) out of 1008 analyzed half-court ball screens and 89.4% of them were done to ball handler. To add, Lamas et al., (2011) found that during 2008 Olympic Basketball tournament out of all half-court possessions 68.7% ended in scoring opportunities from which 34.8% was after on ball screens. Roll man action was significantly run more often by losing teams as best teams in NBA does not rely on centers to make plays anymore. None of the best four teams in 2018-2019 NBA playoffs had a dominant post player. Comparing differences of winning and losing teams Zukolo et al., (2019) counted all pick and roll actions combined and did not find any significant variations. More than half modalities (6) showed no differences between winning and defeated teams. They were used in lesser capacity overall, except spot-up shooting modality, which showed almost identical numbers for both type teams and perfectly matches with Zukolo et al., (2019) results. Spot-up shooting action is widely used due to analytical approach as teams focus on open long-range shots. Chang et al., (2014) looked at shots off the dribble and catch-and-shoot situations and found that spot up situations had a positive and significant difference in accuracy against shots after the dribble Post-up, off-screen offense, miscellaneous and putbacks results can be compared to Selmanović et al., (2015) as similar overall usage ratio percentages were found for NBA teams during 2010-2011 playoffs, but it is worth to mention that hand-off action was used almost twice during 2019 playoffs compared to authors findings.

Offensive modalities scoring efficiency between winning and losing teams Offensive modalities efficiency hypotheses could not have been fully confirmed as accessible data was not sufficient for statistical analysis (chi-square), due to lack of sample size, which should at least reach 30 (Vaitkevičius & Saudargienė, 2006), while “Synergy Sports Technology” can provide only separate team offensive modalities efficiency averages of all playoff games played. While any significant differences could not have been tested, but winning teams showed advantages in eight out of eleven selected modalities. The biggest differences in scoring frequency percentages for winning teams is detectable for off-screen offense, miscellaneous, putbacks and cuts, which correlates with Zukolo et al., (2019) results where differences for 48 off-screen offense, cut, putbacks were similar of winners and defeated teams. Bazanov et al., (2006) results showed that off-ball screen efficiency differs due to the number of off screens used during possession as offenses with two ball screens had 44% scoring efficiency, while using one screen had 56% and four screens had 50% efficiency. Lamas et al., (2011) found that during the 2008 Olympic Basketball tournament 9.6% of half-court sets scoring came from using off-ball screening. Miscellaneous modality differences might show non-categorized possession endings as well drawing fouls in the backcourt and going to the free-throw line, even though according to Trninič, Dizdar & Lukšić, (2002) and Kreivytė & Čižauskas (2010) a number of free throws attempted per game had more influence to winning rather than being more accurate from the line. Cut modality efficiency is an indication of player movement and being able to take advantage of the opponent's defensive mistakes to score or pass to an open teammate. Both type teams showed high efficiency, but winners had an advantage, which coincides with Zukolo et al., (2019) findings. Also, the authors stated that cut modality is the most effective when 2 or 3 players are included. Scoring from putbacks modality is highly correlated with offensive rebounding as “Synergy Sports Technology” differentiates offensive rebounds for offense reset or putback ending. Özmen's (2016) findings show that the Euroleague team who grabs at least 1 offensive rebound more than opponents has a 6,3% higher chance to win a game. Putbacks was also found as one of the most efficient modality at Zukolo et al., (2019) as well as respective efficiency percentages in favor of winning teams. Isolation, transition, pick and roll ball handler showed very small differences in efficiency, even though all modalities were found having a significant difference in terms of usage ratio. Spot-up shooting was the most used modality, but comparing to other results fall behind in terms of efficiency, which coincides with Zukolo et al., (2019) findings. Winning teams were slightly more efficient on finishing pick and roll ball handler actions, but considering that losing teams were using modality significantly more, the total amount of points generated per game is higher. A similar study of Zukolo et al., (2019) found that efficiency on 9 out of 10 modalities were in favor for winning teams and pick and pop action being equal, while on this research it was found that post-up, hand-off, pick and roll (roll man) finishing actions was on the defeated teams' side. According to Zhang et al., (2019) winning teams centers generated more points from the two-point field goal are, but considering this study results defeated teams were generating more points from this modality as significant differences were also found in the usage ratio.

Limitations and improvements Most of the deficiencies in this research occur due to limitations of data collection from available databases in the field. “Synergy Sports Technology” is one of the most widely used software of professional teams in whole world leagues including NBA, Euroleague and other European leagues, but it was lacking structure to provide offensive modalities efficiency data from separate games, while usage ratios were 49 available for each game. Due to limitations, WIN% and LOSS% formulas were selected to indicate winning and losing teams as each game usage ratio and efficiency of winners and losers could not have been compared equally. Due to lack of researches done in the field so far in terms of analyzing similar modalities most of the results as compared to fairly new Selmanović et al., (2015) and Zukolo et al., (2019) studies of offensive modalities role in team's offense and productivity, even though smaller sample sizes were used compared to this research. Improvements for future research might be done with access to more detailed databases or choosing smaller sample sizes and analyzing videotape, but specific and accurate modalities descriptions must be followed as basketball consists of numerous possible actions, movements and decisions on the floor. Also, a study of analyzing defense against offensive modalities can be done as then team offense and defense influence on winning is examined.

CONCLUSIONS As Pick and roll ball handler and Transition were used in high capacity results show high efficiency as well comparing to other results. NBA teams follow analytical strategy trends and tend to rely on long-range shooting, shots to close to the basket and easy baskets, which are created by transition fast breaks. Both type teams play in similar offensive structures, but winners were able to create more fast-break opportunities due to their defensive presents and rebounds, move without a ball and take advantage of opponent's defensive mistakes as well as play more one-on-one basketball handing the ball to the most skilled team players. Defeated teams showed bigger efficiency in Post-up, Hand-off and Pick and Roll (roll man) modalities, which indicates that big man play even though can be efficient but did not result in winning more games.

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Annex B