Difference of Offensive Structure Between European and American Top-Level Basketball

LITHUANIAN SPORTS UNIVERSITY DEPARTMENT OF COACHING SCIENCE INTERNATIONAL BASKETBALL COACHING AND MANAGEMENT

JAKOB HJORTH JØRGENSEN

DIFFERENCE OF OFFENSIVE STRUCTURE BETWEEN EUROPEAN AND AMERICAN TOP-LEVEL BASKETBALL

FINAL MASTER’S THESIS

Scientific Supervisor: Asst. Prof. Dr. Aleksandar Selmanović, PhD

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Table of Content

Introduction……………………………………………………………………………………… 6 Hypotheses………………………………………………………………………………. 6 1. Literature Review……………………………………………………………………………. 7 1.1 Distribution and Efficiency of Transition and Early Offense……………………. 7 1.2 Use and Efficiency of Transition and Early Offense…………………………….. 9 1.3 Distribution of Finishing Action and Efficiency…………………………………… 10 2. Research Methodology and Organization………………………………………………. 14 2.1 Research Object……………………………………………………………………. 14 2.2 Research Strategy and Logic……………………………………………...……… 14 2.3 The Nature of the Research………………………………………………………. 19 2.4 Contingent of Research Subjects………………………………………………… 19 2.5 Research Methods…………………………………………………………………. 21 2.6 Research Organization…………………………………………………………….. 21 2.7 Methods of Statistical Analysis…………………………………………………….. 21 3. Research Findings…………………………………………………………………………... 22 3.1 Descriptive Statistics……………………………………………………………….. 22 3.2 Distribution of Types of Offenses…………………………………………………. 23 3.3 Points per Possession in Basic Types of Offenses………………………..…… 23 3.4 Transition and Early Offense………………………………………………………. 24 3.5 Finishing Actions……………………………………………………………………. 25 4. Considerations………………………………………………………………………………. 28 4.1 Research Deficiencies……………………………………………………………… 32 Conclusions……………………………………………………………………………………… 33 Recommendations……………………………………….…………………………………….. 33 References………………………………………………………………………………………. 34 Annexes………………………………………………………………………………………….. 41

Abstract

Difference of Offensive Structure between European and American Top-level Basketball The aim of this study is to determine if there are any structural differences in relation to offensive style of play between European and NBA basketball. The focus of the study is on types of offenses, their distribution, efficiency, and finishing actions. 15 European (11 Euroleague playoff games, 3 EuroCup semifinals and finals, 1 Champions League final) and 15 NBA playoff games were analyzed using video analysis software. The games involved 14 different European teams and 16 NBA teams. A total of 5027 possessions were categorized into 4 types of basic offense and 11 types of finishing actions using notational analysis, where the following findings stand out: a) The results of the Chi2-test confirmed a statistically significant difference in the distribution of basic types of offenses between European and NBA basketball; b) European basketball had significant more finishing actions of pick and roll and post up, whereas in the NBA 1v1 face to the basket occurred significantly more; c) No statistical significant difference in efficiency, except that pick and roll is executed with a significantly higher points per possession (PPP) in European basketball. The study can help understand the nature of differences between two analyzed models of professional basketball and to establish specific structures that can be interpreted and evaluated constructively and used practicably and prospectively in coaching and other professional practices related to basketball.

KEYWORDS: professional basketball, basketball offense, finishing action, performance analysis

Santrauka

Krepšinio puolimo struktūros skirtumai tarp europietiško ir amerikietiško profesionalaus krepšinio Šio tyrimo tikslas nustatyti ar yra krepšinio puolimo struktūrinis skirtumas Europos krepšinio ir NBA. Tyrimas koncentruojasi ištirti krepšinio puolimo rūšis, jų pasiskirstymą, efektyvumą ir puolimo užbaigimo galimybes. 15 europietiško (11 „Eurolygos“ atkrintamųjų rungtynės, 3 „Eurocup“ pusfinalio ir finalo rungtynės, 1 „Champions league“ finalo rungtynės) ir 15 NBA atkrintamųjų rungtynių, buvo išanalizuotos naudojantis video analizės programine programa. Tyrime buvo išanalizuota 14 skirtingų Europos komandų ir 16 NBA krepšinio komandų. Iš viso ištirti 5027 krepšinio puolimai, kurie buvo suskirstyti į 4 pagrindines puolimo kategorijas ir 11 atakos užbaigimo veiksmų grupes pasitelkus žymėjimo analizės metodą, kuris leido aptikti, jog : a) Chi2-test patvirtino statistiškai reikšmingą skirtingą krepšinio puolimo rūšių pasiskirstymą, tarp europietiško krepšinio ir NBA; b) europietiškame krepšinyje puolimas statistiškai reikšmingai dažniau užbaigiamas „pick and roll“ ir „post up“ atakos užbaigimo veiksmais, priešingai, NBA statistiškai reikšmingas „1vs1“ puolimas žaidžiant veidu į krepšį; c) nebuvo aptikta statistiškai reikšmingo skirtumo puolimo efektyvumo rodiklyje, išskyrus tai, kad „pick and roll“ europietiškame krepšinyje išpildomas statistiškai reikšmingai didesnių „pelnyti taškai per ataką“ (points per possession) rodikliu. Šis tyrimas gali padėti geriau suprasti krepšinio puolimo skirtumus tarp dviejų analizuotų profesionalaus krepšinio modelių bei sukuriant specifinę metodiką, kuri gali būti plačiai interpretuojama ir lanksčiai vertinama bei perspektyviai panaudojama praktikoje krepšinio trenerių ir kitų krepšinio specialistų.

KEYWORDS: profesionalus krepšinis, puolimas krepšinyje, puolimo užbaigimo veiksmai, žaidybinė analizė

Introduction

The game of basketball is a multifaceted game under constant development. It is also one of the most scientifically analyzed sports through notational analysis (García et al., 2013; Lorenzo et al., 2010). Teams are increasingly attracted to an analytical approach to the game. The offensive phase, both technically and tactically, is very saturated but highly diverse in terms of scientific studies. The game is played all over the world with slightly different rules. Overall, the game has the same objective. This study focuses on the structural differences between European and North American top professional leagues with an emphasis on tactical finishes on offense. The aim of this study is to determine if there are any structural differences in relation to offensive style of play between European and NBA basketball. The focus of the study is on types of offenses, their distribution, efficiency, and finishing. The execution of these elements is key and highly influences the effectiveness of which the game is played.

Hypotheses

H1: HA - It is expected that there is a significant difference in the distribution of types of offense (Transition, Early Offense, and Set plays). The game in the NBA relies more on transition and early offense and in Europe the teams rely more on set plays.

H2: HA - It is expected that offensive efficiency in the types of offense is higher in the NBA than in Europe. The efficiency is expressed as points per possession (PPP).

H3: HA - It is expected that the NBA has a significantly higher use of transition and early offense and that they execute these phases significantly more efficiently expressed by PPP. It is also expected that the NBA has a more straightforward approach to attacking during transition - whereas the Europeans would have a more conservative approach - resulting in a significantly higher representation of situations in the NBA with finished possessions where the offensive players outnumber the defensive players (OD.

H4: HA - It is expected that there is a difference in the use of different finishing actions in the NBA compared to European competitions. It is expected that the use of 1v1 facing basket (FACE) is significantly more prevalent in the NBA, whereas the use of pick-and-roll (PNR) is expected to be significantly more predominant in Europe.

H5: HA - It is expected that the NBA-players’ skills and athleticism contribute to higher efficiency in technical and tactical execution of finishing actions. Efficiency expressed as PPP. 7

The study can help understand the nature of differences between two analyzed models of professional basketball and to establish specific structures that can be interpreted and evaluated constructively and used practicably and prospectively in coaching and other professional practices related to basketball. Structure of Work. Relevant scientific literature review, followed by data collection by video analysis and statistical analysis, and discussion on the basis of given results and considerations.

1. Literature Review

The collecting and recording of game-related indicators or factors that represent some of the basic parameters in evaluating sport can contribute to success or improved performance in games (Hughes & Bartlett, 2002). Identifying trends and structures are imperative in the effort to deliver optimal training and preparation and to enhance the ability of players to understand tactics and increase efficiency of decision-making (Courel-Ibáñez et al., 2018). To increase player corporation, you need to detect the mechanisms of the game e.g., offensive aims such as optimal fast break situations and effectiveness of scoring situations and to facilitate this tactical assessment through game analysis becomes an important tool that may be used to develop practice schemes for improving players’ decision-making during competition (Eccles, Ward, & Goodman, 2009). This research is focused on playoffs games as studies show that the NBA and the European competitions get more alike come playoffs time (Štrumbelj et al., 2013; Jungić et al., 2015; Milanović et al., 2014; Mandić et al., 2019). The focus of this study is on the offensive phase and the reason to look at the offense phase is based on reported results that indicated outcomes of games were more influenced by offense than defense in the NBA (Mikołajec et al., 2013).

1.1 Distribution and Efficiency of Types of Offense

The game of basketball has generally been well investigated in studies. Coaches and researchers alike have shown interest in analyzing the game to increase efficiency. There is a consensus that the offensive phase consists of mainly two operational phases: Transition and set offense, and then a third category for situations that does not fit the two others. (Milanović et al., 8

2014; Bazanov et al., 2006b; Lehto et al., 2010; Remmert, 2003; Selmanović et al., 2019, 2015).

Selmanović et al. (2015) defines three phases as follows: 1) The set phase consists of offenses consisting only of the set (positional) phase and offenses that consist of a transitional phase and a set phase - if the set phase is longer than the preceding transition phase. 2) The transition phase is defined as offenses consisting of the transition phase only and offenses consisting of the transition phase and a following set phase shorter than the preceding transition phase. 3) The last category “other” consists of possessions not fitting any of the two other categories.

However, in some cases, early offense is singled out as its own specific category as seen in Bazanov et al. (2006a) which operates with early offense as an independent category. Some operates with duration of time of the offense as the determinant (Bazanov et al., 2006a, 2006b) while others use type of scoring action as determinant (Selmanović et al., 2019, 2015; Zukolo et al., 2019; Lehto et al., 2010; Remmert, 2003). Additionally, it has been reported that the share of primary (30%) and secondary (30%) fast breaks during the transition phase is to be approximately equal with the last 40% being early offense possessions and that the frequencies across the NBA and the Euroleague show proportionate values (Selmanović et al., 2015). Others again only look at one phase i.e., set play (Solsona et al., 2020) and others dig even deeper and only look at one element like shooting or pick and roll offense (e.g., Stavropoulos et al., 2020; Stavropoulos & Stavropoulos, 2020; Vaquera et al., 2013, 2016) or putbacks (e.g. Suárez-Cadenas & Courel-Ibáñez, 2017). A comparative study between NBA and Euroleague suggests that American basketball holds a larger part of fast breaks 20.2% and 15.1%, respectively, and an enhancing factor is the ability of NBA teams to regain possession by either a defensive rebound or a steal (Milanović et al., 2014, Selmanović et al., 2015). Studies suggest that there is no need to distinguish between modalities of set offenses (man, zone) as the use of zone defense rarely occurs. Selmanović et al. (2015) states that man- to-man offense represents 96% of offenses in Europe and 99% of offenses in the NBA. During the 2006 World Cup it was similarly reported that teams on average played man-to-man defenses 85.3% of the time (Polykratis et al., 2010). Lehto et al. (2010) reports that teams played man-to- man defense 89% of the time and state that the success of half court offenses does not vary 9 greatly between man or zone defense. In a study on the German league, the teams played man- to-man defense 98.1% of the time and scored an average of 0.88 PPP (Remmert & Lysien, 2020) and in another study 1.03 PPP was reported (Remmert & Chau, 2019). A study from the Estonian league the investigated team averaged 1.13 PPP (Bazanov et al., 2006b). PPP is suggested to be a very useful statistical tool (Oliver, 2004).

1.2 Use and Efficiency of Transition and Early Offense

The fast break is an integral part of basketball therefore it is natural to examine some of the assumptions surrounding fast breaks. Generally, the transition phase has been quite well investigated, maybe due to the fact that transition or fast breaks have shown to be the most efficient way to score (Milanović et al., 2014, Selmanović et al., 2017; Bazanov et al., 2006b). Researchers have had a lot of focus on fast breaks and they generally agree that fast breaks are a very efficient way of scoring. Fast breaks were reported to be a crucial factor to winning as more fast breaks equal more wins (Evangelos et al., 2005) and therefore teams should strive to increase the number of fast breaks in games (Selmanović et al., 2015). Others reported that 80% of fast breaks ended with a shot in the lane area with a success rate of 73% (Garefis et al., 2007) and similarly it was concluded that the only predictor of a successful fast break was the finishing in the lane area (Conte et al., 2017). Similar results have been reported in various articles, for instance offenses with a duration under 1.82 sec have a 62% success rate (Bazanov et al., 2006a, 2006b). Another study dealing with the duration of fast breaks reported the average duration of fast break to be 3.89 secs (Refoyo et al., 2009). Additionally, fast breaks where “good” shots, defined as a shot from good position in rhythm and undefended, were taken, were more successful (Lehto et al., 2010). It is also reported that around 90% of all fast breaks are ended during the primary fast break phase (Cardenas et al., 2015, Refoyo et al., 2009) this is not concurrent with the 30%-30% split between primary and secondary fast breaks reported from the NBA and Euroleague (Selmanović et al., 2015). Additionally, to establish the most used ratio and to determine whether differences are present between Europe and NBA as reported by studies (Mandić et al., 2019; Selmanovic et al., 2019). It is interesting to see if European style of play will follow the development of the NBA with an increased focus on improvised and individually focused play or vice versa.

1.3 Distribution of Finishing Action and Efficiency

Comparative studies between professional basketball in Europe and the USA are scarcer. Quantitative studies based on game statistics alone are most frequent. A study comparing European players in the NBA to European players from Euroleague when playing head-to-head in the Eurobasket competition (national teams) has concluded that differences in game-related statistics primarily were due to the influence of body composition. The NBA players were on average taller, more athletic, and heavier than their Euroleague counterparts (Paulauskas et al., 2018). This observation could be the reason that dunking is more predominant in the NBA than in Euroleague and that the hook shot is more used in Euroleague compared to the NBA (Paulauskas et al., 2018). Whereas it suggests that European centers are more technically skilled as the hook shot is more frequently used by Europeans and the hook shot is more technically demanding (Erčulj & Štrumbelj, 2015). The positional difference in skill level across different leagues between guards, forwards, and centers was investigated and it was established that differences were evident (Sampaio et al., 2006). In terms of comparison of leagues across the globe, especially useful in terms of scouting when bringing talent from one league to another, the NBA has naturally been deemed the strongest league (Jungić et al. 2015) with Euroleague as the obvious frontrunner for second strongest league in the world. (Mandić et al., 2019). Studies referring to direct confrontation are scarcer as it is seldom that national teams and club teams play head-to-head in international competition. After the 2008 Olympics it was suggested that USA won due to the fact that they played a faster paced game and that they recovered more balls (steals, blocks, and opponents’ turnovers) from their opponents (Sampaio et al., 2010). The faster paced game leads to more possessions (Oliver, 2004) and superior assertive defense are linked to the increased number of recovered balls and thereby the Americans generated additional offensive possessions and opportunities to score. Team USA dominated other teams in number of possessions per game, however there is no direct link between team success and number of possessions (Trninić et al., 2002). The decisive factors in performance are multiple depending on which study one refers to. Milanović et al. (2016) suggests that the decisive factors are related to shooting and the ability to create steals and rebounds.

Pick and roll (PNR) offense has been scrutinized thoroughly by especially European researchers who have given the topic a fair share of interest. PNR is reported to most often (49.9%) be an action between the point guard and the center (Polykratis et al., 2010). According to several researchers, PNR is probably the single most important occurrence of collective tactical 11 behavior as every basketball game has PNR situations present (Vaquera et al., 2013; Stavropoulos & Stavropoulos, 2020; Nunes et al., 2016; Lamas et al., 2011; Remmert, 2003, Matulaitis & Bietkis, 2021). Štrumbelj and colleagues state, PNR is increasingly popular in Euroleague action as the game has purportedly slowed down and players have become more effective (Štrumbelj et al., 2013). This could be to be due to a rationalization the game has undergone leading players to be more cynical and deliberate. This cynical approach is exemplified by European basketball using a higher number of average passes per possession - Euroleague 3.64, NBA 3.43 - in order to create imbalances that will lead to easier scoring options (Selmanović, 2015). This is possibly due to the increased importance of every game since in the Euroleague teams play much fewer games compared to the NBA. NBA playoffs basketball is more similar to European style of basketball which suggests that NBA teams do not play their strongest basketball during the regular season (Štrumbelj et al., 2013; Jungić et al., 2015). It is reported in a study made on the Spanish top league (ACB) that the pace is slower during the playoffs compared to the regular season (García et al., 2013). The notion that European style of play is generally slower is supported by Çene (2018) who states that the pace is slower in Euroleague and that the overall skill set of players are inferior to that of the NBA players (Çene, 2018). Selmanović et al., (2015) reports that there is no significant difference in offense duration between NBA and Euroleague. The assumption that Euroleague prefered the use of PNR is supported by Solsona et al. (2020) since their study shows that in Euroleague action PNR was the most used concept on offense at 63.7% and followed by 1v1 play outside at 21.5%. In PNR situations, the ball handler is the finisher in 43% of the cases observed, even though it has been observed to be the least effective (Marmarinos et al., 2016b). In another study similar numbers were reported 37.3% (Remmert & Lysien, 2020). The most effective situations are reported to be when the pass is made directly to the roller from the ball handler or a shot taken after two passes (Marmarinos et al., 2016b). PNR studies have been conducted and it suggested that including PNR in elite games increases the performance and therefore should be included (Vaquera et al., 2016, Koutsouridis et al., 2018) and a value of 1.22 PPP is reported for PNR-situations (Remmert & Lysien, 2020). Others have tried to establish if there are clear indices of the successful PNR and if it was possible to predict the successful outcome of a given action. For instance, that the top teams predominantly used one type of shot in PNR situations compared to lower ranked teams (Stavropoulos et al., 2020). Recent research reports that since the introduction of the 14 sec. shot clock in FIBA rules, the number of PNR situations has increased by 40% (Lamas et al., 2015, Gómez et al., 2015). The reason that PNR is effective is the fact that PNR forces the defense to collectively respond to this action (Remmert 12

& Chau, 2019), however this trend could be regressing as 1v1 play dominates increasingly as for instance as penetrate-and-kick-situations (Remmert & Lysien, 2020). On the contrary, others report that the use of PNR does not increase the overall chance for success of a team, but it does lead to the immediate effect that outside shooting is promoted as more shots come from longer range and at a higher success rate (Lehto et al., 2010).

Comparative studies have also been made to establish which regional differences are present. Studies suggest that regional differences are evident in certain age groups. It was reported that the European teams played a much slower game with fewer possessions compared to the American teams that played with a high number of possessions (Madarame, 2018). Similarly, it was reported that European teams played with the least possessions per game compared to other continental tournaments (Ibáñez et al., 2003). During the regular season the pace is higher in the NBA, but the pace goes down in playoffs so that it resembles the play in Euroleague where every game holds a larger value (Mandić et al., 2019). On the other hand, it has been reported that there is no difference in pace between the NBA and Euroleague when looking at playoff competition in offenses per minute between Euroleague, 4.34 and the NBA, 4.33 (Milanović et al., 2014). Other studies have focused on more specific action after a given event, for instance shooting percentage after an offensive rebound (OReb) and how this influenced on winning and losing (Suárez-Cardenas & Courel-Ibáñez, 2017). Multiple studies have been conducted investigating the offensive phase. Several studies have been made focusing on the inside pass (Mavridis et al., 2009, Courel-Ibáñez, 2017, Courel-Ibáñez et al., 2016, 2018). The act of scoring in-game has been investigated by several researchers. The actions leading to the actual scoring - the finishing actions - have been grouped by various researchers (Remmert, 2003, Lehto et al., 2010, Gryko et al., 2018, Gomez et al., 2013, Selmanović et al., 2015, 2019) The influence of finishing actions has been investigated to detect any differences in behavior between winning and losing teams at the 2013 Eurobasket. It is reported that there is no statistically significant difference between finishing actions of winning and losing teams (Zukolo et al., 2019). There are many important factors and variables that influence an outcome of a basketball game. These variables may not always be important but nevertheless coaches must try to be structured and keep the teams organized (Mikołajec et al., 2013).

In regard to efficiency, it has been assumed that NBA players are superior to their European counterparts. Efficiency has been the theme for various studies in an attempt to quantify 13 the value of a given shot of possession. Shot selection in relation to shot clock has been investigated and it seems that NBA teams are reluctant to shot early in the shot clock leaving efficiency to go down (Skinner, 2012) which on the other hand contradict the reports that the NBA is exponent of a style based on transition and early offense. Others report that the efficiency is higher in situations related to PNR (Koutsoridis et al., 2018; Marmarinos et al., 2016b, Matulaitis & Bietkis, 2021). Other factors that could be indicating that styles differ in America and Europe is that some other performance indicators have been reported to be different between the NBA and Euroleague (Mikołajec et al., 2013, Csataljay et al., 2009, Doğan & Ersöz, 2019). The style of play may be different due to the difference in rules and in court size. Therefore, it is a very complex exercise to compare the levels of basketball. It further complicates the matter that European and NBA basketball are governed by separate specific basketball rules and court size that potentially increase the differences.

Some of the more well inspected areas are topics relating to the differences between winning and losing teams. Many of the studies have been made at inferior leagues and they differ a great deal in their conclusions and they attribute assist, steals, blocks, defensive rebounding (DReb), turnovers (TOV), and various types of scoring (Ibáñez et al., 2003; Lorenzo et al., 2010; Csataljay et al., 2009; García et al., 2007; Šeparović & Nuhanović, 2008; Daskalowski et al., 2014). At a higher level you start to see more studies, but they are less frequent. The studies agree on some factors to be important. Rebounding, both OReb but in particular DReb, is reported as a key factor in multiple studies (Gómez et al., 2008; Milanović et al., 2016; Doğan et al., 2016; Marmarinos et al., 2016a; Leicht et al., 2017; Conte et al., 2018; Çene, 2018; Teramoto & Cross, 2010). The other most frequently factors in these studies are assists, steals, and turnovers. Additionally, scoring is also mentioned as an important factor field goals (FG) and FG% are reported as important factors as well (Trninić et al., 2002; Leicht et al., 2012) and in some of the newer studies 3-point percentage (3p%) is mentioned as the key factor (Doğan & Ersöz, 2019) or one of the key factors (Conte et al. 2018; Csataljay et al., 2009). Jaguszewski (2020) supports this notion, however he points out that you can be successful even though you do not rely heavily on 3-point shooting but that relying on 3-point shooting would be an approach that would help your team to win more games. Dean Oliver suggests that there are four factors that are key performance indicators, namely shooting percentage from the field - most frequently effective field goal percentage (eFG%) has been used to represent this measure/quantity. Secondly, getting rebounds represented by offensive rebound percentage (OReb%) and thirdly committing 14 turnovers - represented by turnover percentage (TOV%). Finally, getting to the foul line and making a lot of shots using free throw rate (FTRate) to quantify this measure. He uses the four factors differently dependent on whether it is in relation to offense or defense (Oliver, 2004). These four factors are sometimes referred to slightly different e.g., Kubatko et al. (2007) uses turnovers per possession. Winning and losing naturally is a very important aspect of basketball. However, most of the researchers investigating winning/losing come up with some factors related to scoring and offense. Furthermore, it has been reported that winning and losing teams have almost identical structure of finishing action and that there is no statistically significant difference between finishing actions of winning and losing teams (Zukolo et al., 2019).

2. Research Methodology and Organization

2.1 Research Object

High-level basketball teams have various approaches to playing offense. Frequencies of offensive modalities and their particular efficiency give a general indication of successfulness of teams. The difference in style across leagues may be the determining factors in the reasoning behind why one league is stronger than the other. The primary goal of this study is to identify and interpret the differences between defined types of phases and realization of different types of basketball offenses across the two top leagues in the World. A statistical analysis was used to determine whether differences were evident.

2.2 Research Strategy and Logic

In previous research and in literature, possessions have been defined in multiple ways. In this study, a possession is defined as follows: A possession starts either by: a) inbounding the ball after an opponent’s made basket. b) inbounding the ball after an opponent’s turnover. c) inbounding the ball after a defensive team rebound. d) securing a live ball by a defensive rebound after an opponent’s missed basket. e) securing a live ball by stealing the ball from the opposition. 15

f) in special cases inbounding after one’s own made last free throw (FT), e.g., after technical or flagrant/unsportsmanlike fouls committed by the opponent. g) Gaining possession after the initial jump ball (or in NBA only: after possession changing jump ball) A possession ends either by: a) offensive team making a basket. b) defensive team gains possession of the ball. c) a team shooting FT (regardless of a potential miss of the last FT which would allow for an offensive rebound (OReb) and a continuation of offense - in such a case it is considered a new possession)

Possession Outcome. The possession outcome is either: made basket, missed basket, made basket plus 1 free throw, any number (1, 2, 3) of free throws earned with no prior made basket, and turnover.

Numerical Outcome. The numerical possession outcome is expressed numerically as 0 (miss or turnover), 1 (any number of FT - made 1 FT), 2 (made 2p FG or 2/2 FT, 2/3 FT), 3 (made 3p FG, made 2p FG + 1 FT, 3/3 FT), 4 (made 3p FG + 1 FT).

Basic Types of Offense. The theories mentioned in chapter 1.1 and reported values have led to the two hypotheses stated in the introduction because it would be interesting to establish if there is a significant difference in the distribution of types of offense (H1) and whether or not it holds true that the NBA indeed performs more efficiently than the Europeans (H2). Offense generally occurs in two states, set offense (positional offense) and transition offense (fast break). Early offense has been separated in its own category from the assumption that early offense is becoming a more predominant type of offense in particular in the NBA due to the more improvised style of play as described by Selmanović et al. (2019). This paper defines and uses four operational defined types of basketball offense: set (positional), transition, early offense, and other offenses (Figure 1).

Figure 1: Basic Types of Offense

1) The set offense or positional offense is the most frequently occurring form of offense (Milanović et al. 2014; Remmert 2003; Tavares & Gomes 2003). The set offense is always played with a player formation (PF) of 5v5 with a set defense with defensive control (DC). DC is defined as when the defense initially establishes a controlled 5v5 defensive situation. The set offense is characterized by involving tactical manoeuvres (TM) by the offense. The set phase can also include a transitional phase, but the transition phase is then shorter than the set phase. The set offense can be divided into modalities of playing against man-to-man defense or zone defense. Due to the scant use of zone defenses in Euroleague and the rules restricting the use of zone defense in the NBA these modalities have not been deemed important to track in the current study. In this paper, a possession is categorized as a positional play by default, if the final scoring action is preceded by either a sideline out of bounds play (SOB) or baseline out of bounds play (BOB) in the front court. The logic behind this is the defense has ample opportunity to gain DC. 2) The transitional phase is defined as possessions where the defense does not gain DC and most often the PF of the offense outnumber the defense (O>D) or they are even (O=D) - however special transition situations occur where offenses complete plays even though they are outnumbered (O

Type of Finishing Action. Successful continuous shooting (finishing action) is dependent on quality shots. What characterizes a good shot and which types of shots are interesting is the big question. Most literature points towards assumptions and categories similar to the ones outlined in this study. Researchers call these categories by various names but essentially it is very similar or the same (Selmanović, 2015; Selmanović et al., 2015, 2019; Zukolo et al., 2019; Lehto et al., 2010; Lamas et al., 2011; Solsona et al., 2020; Remmert, 2003; Matulaitis & Bietkis,

2021). The current study defines eleven different finishing actions to be investigated and to determine whether some are more used in the NBA in comparison to Europe and which practical implications that may transpire. An interesting point to be examined in this study is an assumption that European teams play a lot more PNR compared to their American counterparts and the assumption that the NBA is a league with more isolation (1v1 face up). Since researchers report various results regarding the use of finishing actions, this investigation will investigate the differences that might be present between the two styles both in terms of distribution and efficiency (H4 and H5). It is expected on one hand that the NBA uses 1v1 facing the basket (FACE) more than the Europeans, and on the other hand it is expected that the Europeans run more PNR as it is stated by several researchers as mentioned in the literature review (Chapter 1.3).

The eleven types of finishing actions are defined and categorized as follows (Figure 2): 1) FACE: Scoring actions when a player attacks facing the basket. The offensive player trying to beat the opposition without any teammates involved while facing the basket. 2) POST: Scoring actions with a player trying to beat the opposition with his back to the basket. Also commonly referred to as posting up. 3) SPOT: Spot up shooting defined by situations when offensive players perform a jump shot after receiving a pass from a teammate without making a move of his own. 4) CUT: Cut to or away from the basket is movement of an offensive player leading to a scoring action without the use of screens or dribbling. 5) DISH: A penetrating player dishing off the ball (very short pass) to an open teammate close to the basket. 18

6) PNR: Pick and roll is defined as a situation where two offensive players are working together. The ball handler receives a screen (pick) from the screener. The ball handler uses this screen to get off a shot, drive to the basket to finish or to make a pass to the screener who is rolling to the basket. 7) POP: Pick and pop is defined as a situation where two offensive players are working together. The ball handler receives a screen (pick) from the screener. The ball handler uses this screen to draw attention to free up space allowing the screener to perform a jump shot after popping or flaring out. 8) HAND: Handoffs are defined as situations where two offensive players are working together. Initially, the situation resembles the PNR- and POP-situations but the pass made from the ball handler to the other player is made at close range (within touching distance). After the ball handler hands over the ball, he becomes the screener immediately hereafter. 9) OFF: Off-ball screens are situations where the scoring action occurs after the use of screens set away from the ball handler allowing him to pass to an open teammate for a shot or layup. 10) PUTB: Putbacks after offensive rebounds are situations when an offensive player puts himself in a position allowing him to attempt scoring after performing an offensive rebound following a missed shot from a teammate. 11) MISC: Miscellaneous finishing actions that do not fit any of the above listed scoring actions.

Scoring actions leading to fouls, either offensive or defensive, are categorized by the scoring action that either leads to free throws (FT) or to an offensive (charge) foul.

Ratio between Offensive and Defensive Players. Set plays and early offenses are defined in this investigation as situations with PF of 5v5. If a transition possession is detected it is analyzed and categorized according to the ratio between offense and defense. Hypothetical outputs can range from 1v0 to 5v4 (1v0, 1v1, 1v2, 1v3, 1v4, 1v5, 2v0, 2v1, 2v2, 2v3, 2v4, 2v5, 3v0, 3v1, 3v2, 3v3, 3v4, 3v5, 4v0, 4v1, 4v2, 4v3, 4v4, 4v5, 5v0, 5v1, 5v2, 5v3, 5v4). The relationship between successful outcomes of fast breaks (points scored) will be compared to the offense-vs-defense-ratio in H3, in which it will be investigated whether or not it can be established that the NBA is the superior league in regard to efficiency in transition and early offense, and if the NBA actually has a higher usage rate of these types of offenses compared to their European counterparts. 19

Figure 2: Finishing Actions Flow Chart

2.3 The Nature of the Research

The study is an observational study based on notational analysis. Notational analysis has been proved to be a valid way to analyze and interpret technical and tactical dimensions of game analysis in team sports (Hughes & Franks 2004, Hughes & Bartlett 2002, Hughes 2004).

2.4 Contingent of Research Subjects

All research material (games) used for this observational study is available publicly online and no one individual is mentioned by name in the thesis. Informed consent from athletes was not necessary as games were broadcast on television.

The sample was collected by analyzing 30 games of high qualitative rank of the European and American competition during the 2017-18 season. The NBA games were selected by 20 randomly selecting one game from each series of the NBA playoffs (15 games). By selecting in this manner, you ensure a broad representation of teams (16) in the sample. The European games were selected by choosing the Euroleague Final Four games (4 games), 7 randomly selected games from the Euroleague Playoffs (each team had to be represented at least once), the final and semifinals of Eurocup (3 games), and the final of Champions League (1 game). The finals and semifinals of the presumed inferior European competitions (Champions League and Eurocup) were included to secure a broader representation of teams in the sample (14 teams). The current format of the Euroleague playoffs only include eight teams to qualify and to assure that the data is not skewed by the preferences of one given team’s style of play more teams have been included. The final games of the other two minor European competitions were selected to ensure a highly competitive level of playoffs basketball (Table 1). The selected games generated a total of 5027 entities (n = 5027) of which 2886 entities were selected from 15 NBA games and 2141 entities (n = 2141) selected from 15 European games.

Table 1: List of analyzed games and results

Sample Variables: - Outcome of the possession - Numerical result - points scored - Type of offense - Type of finishing action - Ratio between offensive and defensive players

2.5 Research Methods

This research would be impossible without the use of computer data processing. All video research was made with Sideline Sports - XPS Video Analyzer. The use of video editing software allows the researcher to perform notational analysis by tagging and storing relevant footage and to export generated data. Data was exported to Microsoft Excel for processing and to RStudio for additional statistical processing.

2.6 Research Organization

The current study protocol was approved by the Lithuanian Sports University Ethics Committee. A request to conduct a social study was submitted 2019-09-13 to the Lithuanian Sports University Ethics Committee and implemented 2019-09-24 with permit No. 15550. See Annex 4.

2.7 Methods of Statistical Analysis

The quantitative variables noted in this study are reported in absolute (frequencies) and relative values (percentages). The quantitative descriptive variables are reported in frequencies, in absolute and relative values. Descriptive parameters used are mean, standard deviation (SD), min. and max. score, skewness, kurtosis, and independent t-test are used to compare the two groups. The qualitative variables were compared using the chi-squared test (χ² or Chi2) as a means to assess associations between the categorical variables. Descriptive statistics are used to assess the qualitative variables in terms of total number, percentage, median, and mode. Data was analyzed using the software RStudio (version 1.3.1093; RStudio PBC, Boston, MA, USA), and the significance level was set at p < 0.05. Percentages were calculated using descriptive statistics. For the purpose of testing the difference between the NBA and European styles, nonparametric statistics methods were applied, using Chi²-test in nominal value (p < 0.05), while t-test was used for independent samples for the purpose of determining differences in quantitative variables. The t-test was applied regardless of not normal distributed, skewed, and large dataset. The t-test is one of the most widely used tests and it is suggested to be reasonable to use even with large samples (n > 30) and that the t-test gains power with a larger sample size (Liu, 2014). The t-test is applied regardless of a not normally distributed and 22 skewed dataset as the Central Limit Theorem will apply when n ≥ 30 meaning that the distribution of sample means approaches normality as the size of n increases, regardless of the shape of the population distribution (LaMorte, 2016; Mordkoff, 2016).

3. Research Findings

3.1 Descriptive Statistics

This study has investigated a total of 30 basketball games from season 2017-18 and a total of 5027 possessions and 1335 minutes played. From the total sample, 2141 entities are from European and 2886 are from American basketball (Table 2). On average in European games, 165 points was scored (min. 143; max. 194; SD 14.7) whereas American games hold an average of 211 points/game (min. 187; max. 249; SD 17.2).

The normality test of the data sets of Europe and America was implemented using Shapiro-Wilk’s test. The test indicated that both data sets are not normally distributed (p < 0.001) and both skewed (EURO 0.255, NBA 0.313) and both with excess kurtosis (EURO -1.52, NBA - 1.50). Generally, when looking at absolute numbers, it is obvious that larger parameters exit in NBA due to longer duration of the game. But even relative parameters, such as possessions and points per minute show slightly higher figures. So more detailed statistical analysis of possessions and offensive efficiency is described further in the text.

Table 2: Basic descriptive statistics of European and American basketball offenses EURO NBA Number of games analyzed 15 15 Number of teams in sample 14 16 Minutes played 610 725 Total possessions 2141 2886 Possessions / game* 142.7 192.4 Possessions / minute 3.51 3.98 Points / game* 165 211 Points / minute 4.06 4.36

*includes overtime games

3.2 Distribution of Types of Offenses

When looking at types of offense in European and American basketball (Table 3), this study found a statistically significant difference (Chi2 = 54.98, df = 3, p < 0.001) in the distribution of types of offense: transition, early offense, set plays, and other offenses. Furthermore, when comparing European to American basketball, it is shown that American basketball relies statistically significantly (p < 0.001) more on transition (6.6% vs. 10.2%), early offense (11.6% vs. 16.5%). The Europeans rely more on set play (78.0% vs. 71.0%), and other offenses (3.9% vs. 2.3%).

Table 3: Types of offense in European and American basketball

EURO NBA

n % n % Chi2 p-value

Transition 142 6.6 293 10.2 19,27 <0.001*

Early offense 249 11.6 477 16.5 23,86 <0.001*

Set play 1669 78.0 2049 71.0 30,88 <0.001*

Other 81 3.9 67 2.3 9,19 0,002*

Total 2141 100 2886 100

Chi2 = 54.98, df = 3, p < 0.001*

3.3 Points per Possession in Basic Types of Offenses

When investigating PPP on the total sample of all possessions, it was found that there was a statistically significant difference (p = 0.047) during transition (Table 4), where European teams produced a statistically significantly larger PPP (1.486 ± 1.023, n = 142) compared to the American (1.266 ± 1.103, n = 293). The other categories showed no statistically significant (p > 0.05) differences.

Table 4: H2 - PPP all possessions

EURO NBA Mean SD Mean SD T-score p-value Transition 1.486 1.023 1.266 1.103 1.994 0.047* Early offense 1.273 1.160 1.119 1.176 1.679 0.094 Set play 1.128 1.182 1.079 1.166 1.268 0.205 Other 0.753 0.874 0.821 0.869 -0.471 0.638 Total 1.154 1.165 1.098 1.157 1.684 0.092

3.4 Transition and Early Offense

Distribution. It was found (Table 5) that the NBA has an overall statistically significantly (p < 0.001) higher use of transition and early offense (Trans+EO) compared to their European counterparts (EURO 18.3%, NBA 26.7%). In comparison to Europe, the Americans had the higher usage rates of ratios of transition except from Trans O>D, where no statistically significant difference was found. The NBA (n = 101, 2.8%) has a statistically significantly (p = 0.022) higher use of transitions with equal ratio between offense and defense (Trans O=D) compared to the Europeans (n = 51, 2.0%). The NBA (n = 94, 2.6%) also has a statistically significantly (p < 0.001) higher use of transitions with offense outnumbered by defense (Trans O

Table 5: H3 Distribution of Transition and Early Offense by Ratio between Offense and Defense EURO NBA n % n % Chi2 p-value Trans O>D 62 2.5 98 2.7 0.997 0.318 Trans O=D 51 2.0 101 2.8 5.236 0.022* Trans O

Points per Possession. When investigating PPP specifically during transition and early offense (Table 6), it was found when looking at all transitions combined the Europeans executed 25 significantly better as stated in Table 4. The Europeans (1.350 ± 1.115) also scored a statistically significantly (p = 0.013) higher PPP in the combined transition and early offense situations (Trans+EO) than the Americans (1.175 ± 1.150). In Trans O>D, Trans O=D, Trans O 0.05) differences were detected.

Table 6: PPP H3 Transition and Early Offense Efficiency

EURO NBA mean SD mean SD T-score p-value Trans O>D 1.726 0.890 1.469 1.037 1.607 0.110 Trans O=D 1.314 1.090 1.178 1.099 0.720 0.472 Trans O

3.5 Finishing Actions

Table 7: H4 - All Possessions: Finishing Actions and Points per Possessions EURO NBA EURO NBA n % n % Chi2 p-value PPP SD PPP SD T-score p-value FACE 469 25.59 895 35.32 46.913 <0.001* 1.198 1.112 1.153 1.120 0.710 0.478 POST 190 10.37 198 7.81 8.556 0.003* 1.258 0.966 1.273 1.001 -0.148 0.882 SPOT 326 17.79 422 16.65 0.959 0.327 1.482 1.475 1.358 1.461 1.144 0.253 CUT 96 5.24 158 6.24 1.933 0.164 1.708 0.724 1.595 0.814 1.121 0.263 DISH 59 3.22 77 3.04 0.114 0.735 1.475 0.971 1.662 0.788 -1.244 0.216 PNR 377 20.57 348 13.73 35.879 <0.001* 1.329 1.112 1.155 1.110 2.103 0.036* POP 19 1.04 37 1.46 1.508 0.220 1.316 1.455 0.838 1.191 1.318 0.193 HAND 22 1.20 48 1.89 3.248 0.072 1.591 1.260 1.042 1.320 1.639 0.106 OFF 94 5.13 150 5.92 1.263 0.261 1.447 1.250 1.327 1.223 0.740 0.460 PUTB 101 5.51 132 5.21 0.191 0.662 1.406 0.961 1.303 0.949 0.816 0.415 MISC 80 4.36 69 2.72 8.696 0.003* 1.228 0.791 1.145 0.827 0.509 0.611

Chi2 = 87.161, df = 10, p < 0.001*

Furthermore, when looking at all possessions, there is a statistically significant difference (p < 0.001) in the use of FACE being more prevalent in America (35.3%, n = 895) compared to Europe (25.6%, n = 469). The use of PNR is also statistically significantly (p < 0.001) more used in Europe (20.6%, n = 377) compared to America (13.7%, n = 348). Additionally, it occurs that POST is statistically significantly (p = 0.003) more present in European basketball (10.4%, n = 190) compared to America (7.8%, n = 198). In the rest of the finishing actions, no statistically significant (p > 0.05) differences were found. When investigating PPP, no statistically significant (p > 0.05) differences in either category except from PNR, where the Europeans (1.329 ± 1.112) scored statistically significantly (p = 0.036) higher than the Americans (1.155 ± 1.110).

Table 8: Transition: Finishing Actions and Points per Possessions

EURO NBA EURO NBA

n % n % Chi2 p-value PPP SD PPP SD T-score p-value

FACE 59 45.7 127 47.2 0.076 0.782 1,542 0,953 1,425 1,035 0,736 0,462

POST 2 1.6 4 1.5 0.002 0.961 2,500 0,707 1,500 1,291 0,985 0,381

SPOT 15 11.6 49 18.2 2.804 0.094 2,133 1,356 1,143 1,458 2,339 0,023*

CUT 37 28.7 63 23.4 1.283 0.257 1,622 0,758 1,698 0,710 -0,509 0,612

DISH 3 2.3 7 2.6 0.027 0.869 2,000 0,000 1,571 0,787 0,911 0,389

PUTB 6 4.7 14 5.2 0.056 0.813 1,333 1,033 0,714 0,914 1,338 0,198

MISC 7 5.4 5 1.9 3.795 0.514 1,286 0,756 0,000 0,000 3,750

Chi2 = 7.117, df = 6, p = 0.310

When you look at PPP values for the transition phase, there are also no statistically significant (p > 0.05) differences, except from SPOT where the Europeans (2.133 ± 1.156) perform statistically significantly (p = 0.023) better than the Americans (1.143 ± 1.458).

Table 9: Early Offense: Finishing Actions and Points per Possession EURO NBA EURO NBA n % n % Chi2 p-value PPP SD PPP SD T-score p-value FACE 62 28.1 163 38.4 6.903 0.009* 1,177 1,109 1,141 1,099 0,221 0,825 POST 33 14.9 44 10.4 2.867 0.090 1,455 0,869 1,364 0,942 0,433 0,666 SPOT 44 19.9 87 20.5 0.033 0.855 1,727 1,5 1,437 1,476 1,058 0,292 CUT 16 7.2 17 4.0 3.123 0.077 1,875 0,5 1,647 0,702 1,068 0,294 DISH 11 5.0 11 2.6 2.504 0.114 1,455 0,934 1,727 0,905 -0,696 0,495 PNR 35 15.8 67 15.8 0.000 0.991 1,229 1,165 1,179 1,154 0,205 0,838 POP 2 0.9 5 1.2 0.102 0.750 1,5 2,121 1,4 1,342 0,078 0,941 HAND 1 0.5 8 1.9 2.172 0.141 2 0,375 1,061 OFF 1 0.5 7 1.7 1.704 0.192 3 1,286 1,254 PUTB 11 5.0 15 3.5 0.778 0.378 1,545 0,82 1,2 1,082 0,887 0,384 MISC 5 2.3 0 0.0 1,2 0,447

Chi2 = 26.693, df = 10, p < 0.001*

FACE is the most frequently used in both styles of play, but it is used 38.4% of the time by the American teams whereas the Europeans use FACE 28.1% of the time. The use of PNR is not statistically significantly (p > 0.05) greater in Europe during this phase, nor is the rest of the finishing actions.

Finishing Actions - Set play. The Americans (33.7%) use a statistically significantly (p < 0.001) larger amount of FACE (Table 10) compared to the Europeans (24.1%). The Europeans (23.8%) have a statistically significantly (p < 0.001) bigger frequency of PNR compared to the Americans (15.7%). The statistically significant (p = 0.018) difference of the use of POST is also evident during set plays (EURO 10.8%, NBA 8.4%).

Furthermore, there is found a statistically significant difference in the use of SPOT (Chi2 = 4.026, p = 0.044) and CUT (Chi2 = 4.022, p = 0.045), however these results are not significant if you consider Yates’ correction (SPOT: Chi2 = 3.840, p = 0.05005); CUT: Chi2 = 4.022, p = 0.056).

Table 10: Set play: Finishing Actions and Points per Possessions

EURO NBA EURO NBA

n % n % Chi2 p-value PPP SD PPP SD T-score p-value

FACE 345 24.1 603 33.7 35.106 <0.001* 1,154 1,129 1,103 1,137 0,664 0,507

POST 155 10.8 150 8.4 5.583 0.018* 1,200 0,976 1,240 1,015 0,351 0,726

SPOT 267 18.7 286 16.0 4.026 0.044*# 1,404 1,469 1,371 1,459 0,272 0,786

CUT 43 3.0 78 4.4 4.022 0.045*# 1,721 0,766 1,500 0,908 1,351 0,179

DISH 45 3.1 59 3.3 0.058 0.810 1,444 1,013 1,661 0,779 -1,233 0,220

PNR 340 23.8 281 15.7 33.235 <0.001* 1,341 1,108 1,149 1,101 2,151 0,032*

POP 17 1.2 32 1.8 1.908 0.167 1,294 1,448 0,750 1,164 1,430 0,159

HAND 21 1.5 38 2.1 1.898 0.168 1,571 1,287 1,237 1,344 0,929 0,357

OFF 93 6.5 143 8.0 2.596 0.107 1,430 1,246 1,329 1,226 0,617 0,538

PUTB 82 5.7 102 5.7 0.002 0.968 1,427 0,969 1,392 0,914 0,249 0,804

MISC 22 1.5 17 1.0 0.130 0.130 1,045 0,899 1,529 0,624 -1,893 0,066

Chi2 = 72.324, df = 10, p < 0.001*

#if you consider the Yates correction then these categories are not statistically significantly different; SPOT (Chi2 = 3.840, p = 0.05005); CUT (Chi2 = 4.022, p = 0.056)

Finishing Actions - Points per Possession. The Europeans (1.329±1.112) produce a statistically significantly (p=0.036) higher PPP in PNR than the Americans (1.155±1.110) when considering all possessions. The remaining finishing actions do not produce any statistically significant differences.

4. Considerations

Hypothesis 1. According to the criteria set in this investigation, it is evident that the presented results show a statistically significant difference between European and American basketball (Chi2 = 54.98, df = 3, p < 0.001) in terms of distribution of types of offense (set plays, transition, early offense, and other offenses). This confirms alternate hypothesis 1 (HA1) as it is found that the types of offenses do not have equal representation in European and American basketball. Furthermore, it is shown that American basketball relies statistically significantly more on transition (Chi2 = 19.27, p < 0.001) and early offense (Chi2 = 23.86, p < 0.001), whereas European basketball relies statistically significantly more on set plays (Chi2 = 30.88, p < 0.001). Other researchers have reported statistically significant differences (Chi2 = 28.13, df = 2, p < 0.001) in comparative studies between the Euroleague and the NBA (Milanović et al., 2014; 29

Selmanović et al., 2015; Selmanović, 2015). However, the categories of basic offense were slightly different as they only used three categories (set, transition, other) compared to the four used in this study. The results reported were similar in terms of more transition plays present in the NBA. In this study, it was found that transition and early offense (Trans+EO) combined for Euro 18.3% vs. NBA 26.7% (Table 5) compared to Euro 15.1%, NBA 20.2% reported in the other studies (Milanović et al., 2014; Selmanović et al., 2015; Selmanović, 2015). This confirmed the idea that the NBA plays a more straightforward style of play based on quicker, less schematic, and more improvised offensive game. The tendencies were also mentioned by Selmanović et al. (2019) suggesting that the American style of play is more of a hybrid between organized and improvised play. A study on the Beijing Olympics in 2008 reports transition and early offense at 16.6%, based on five games only (Lehto et al., 2010).

Hypothesis 2. This investigation rejects the alternate hypothesis HA2 as it is shown that the NBA does not produce statistically significantly more PPP in either of the types of offense or combined totals. On the contrary, it is shown that European basketball (1.486 ± 1.023, n = 142) produced a statistically significantly larger PPP (p < 0.05) in transition compared to the NBA (1.266 ± 1.103, n = 293). These results indicate that European basketball is more conservative and calculating as suggested by Štrumbelj et al. (2013). The NBA is driven more and more by analytics (Davenport, 2014) and therefore it makes sense from an analytics standpoint to get as many transition and early offense situations as possible to maximize offensive output as they are the most efficient possessions (Christman et al., 2018). Transition and early offense were also reported to be the most efficient ways of scoring by other studies (Matulis & Bietkis, 2021). It has previously been reported by Milanović et al. (2014) that the NBA was slightly more efficient than Euroleague when considering transition offense.

Hypothesis 3. It was expected as stated in HA3 that the NBA would have a significantly higher use of transition and early offense (Trans+EO). This study confirms that the NBA (n = 770, 26.7%) has an overall statistically significantly higher use of transition and early offense compared to their European counterparts (n = 391, 18.3%). The higher use of transition and early offense in the NBA compared to Europe (20.2% vs. 15.1% respectively) is also reported by other researchers (Milanović et al., 2014; Selmanović et al., 2015; Selmanović, 2015). It is also confirmed that the NBA has a more straightforward approach to attacking during the transition and early offense phases if you look at the phases individually. The NBA (2.8%) has a statistically significantly higher use of transitions with equal ratio between offense and defense 30

(Trans O=D) compared to Europe (2.0%). Additionally, as hypothesized the NBA (2.6%, EURO 1.2%) also has a statistically significantly higher use of transitions where the offense is outnumbered by the defense (Trans OD) was rejected as the Americans (n = 98, 2.7%) attack in equal numbers and no significant difference was found (Chi2 = 0.997, p = 0.318). The numbers suggest that the Europeans are as willing as the Americans to attack in transition when outnumbering the opponent. Selmanović et al. (2015) reports a slightly higher use of primary break in Europe (Euro 29.6%, NBA 27.6%), no difference in secondary break (Euro 31.1%, NBA 31.0%), and a small difference in the use of early offense (Euro 39.3%, NBA 41.4%). The study concluded that there was no statistically significant difference in the distribution (Chi2 = 0.607, df = 2, p = 0.7383). This adaptation to different styles is something to consider when the NBA is looking for European talent and vice versa when European clubs look to sign American imports. The NBA is generally doing a good job of evaluating foreign talent for instance in relation to the annual draft according to researchers (Salador, 2011; Bieniek et al., 2014).

The part of HA3 regarding PPP is rejected as the NBA does not execute better than the Europeans in any transition and early offense categories (Table 6). Actually, it turns out that when looking at all transitions combined the Europeans execute significantly better as stated in HA1. The Europeans (1.350 ± 1.115) also scored a statistically significantly higher PPP in the combined transition and early offense situations than the Americans (1.175 ± 1.150). This could be because the European system is based on a larger degree of schematic offense and control as proposed by Selmanović et al. (2019).

Hypothesis 4. This study can confirm HA4 as there is a clear difference in the preference of the use of finishing actions between European and American basketball (Chi2 = 87.161, p < 0.001). Furthermore, it is statistically significantly stated that the use of FACE is more prevalent in the NBA (35.3%, n = 895) compared to Europe (25.6%, n = 469). This can be seen as odd since the NBA to a large extent is analytics driven and FACE is one of the least effective ways of attacking according to this study with a PPP of 1.153 ± 1.120. Another study also reports isolation plays (FACE) in Euroleague to be more likely to end up inefficiently rather than efficiently (Matulis & Bietkis, 2021). According to this study, FACE in European competition ranks the lowest in efficiency with 1.198 ± 1.112 PPP. 31

Even though teams preach unselfish play (Scarlatelli, 2015; Colás, 2012), this does not necessarily transcend into rewards given to players scoring compared to players making assists. It is suggested that a player’s future salary is increased by approximately $22,000 per FG personally scored and decreased by $6,000 per assist made (Uhlmann & Barnes, 2014). This suggests NBA teams tend to financially reward selfish isolation play (FACE) over team-oriented play, which naturally is a huge incentive for players (Smith et al., 2004; Goldman, 2007). The reason for this could be that players that can create something on their own tend to have a high value and big role on teams (Matulis & Bietkis, 2021). The use of PNR is also confirmed to be statistically significantly more used in Europe (20.6%, n = 377) compared to America (13.7%, n = 348). Additionally, it occurs that POST is more present in European basketball (10.4%, n = 190) compared to America (7.8%, n = 198). This could be supported by the assumptions presented by Erčulj & Štrumbelj (2015) who partially explained the absence of the hook shot in the NBA by inferior technique of NBA centers compared to European centers. The inferior skill set of NBA centers in turn could be the reason for NBA teams using POST less frequently than their European counterparts. Selmanović et al. (2019) also reports differences in the use of different finishing actions during the 2011-12 season. The Americans are characterized by a higher frequency of FACE (Euro 13.6%, NBA 19.4%) and SPOT (Euro 15.4%, 16.4%). Whereas the Europeans have a higher frequency of PNR (Euro 16.6%, NBA 15.3%), POST (Euro 7.9%, 5.9%), and PUTB (Euro 4.3%, NBA 3.8%). This means that the two main categories (FACE, PNR as mentioned in hypothesis 4) come out with similar findings in the current study and the earlier studies done by Selmanović and colleagues. POST is also found to be prevalently used in European basketball as mentioned by Selmanović et al. (2019). Remmert (2003) reported FACE 17.2%, POST 13.5%, and PNR 16.4% in a study on mixed leagues which therefore is not directly comparable. During set plays the hypothesis is fully confirmed as there are statistically significant differences found in relation to the use of FACE in the NBA and the prevalence in Europe towards PNR. Furthermore, POST and SPOT are significantly more frequent in Europe whereas CUT is significantly more frequent in the NBA.

Hypothesis 5. Considering HA5, then the hypothesis is completely rejected in this particular study as the results suggest the assumption is not the case. This is the case both when looking at all possessions regardless of type of offense and when investigating each type of offense individually. In fact, the opposite holds true in some instances. The Europeans (Mean 32

1.329 ± 1.112) produce a statistically significantly higher PPP in PNR than the Americans (Mean 1.155 ± 1.110) when considering all possessions (p = 0.036).

If you consider HA5 in terms of each type of offense, then the results suggest that the Europeans (2.133 ± 1.356) produce a statistically significantly higher PPP (p = 0.023) in SPOT than the Americans (1.143 ± 1.458) during the transition phase. During early offense there are found no statistically significantly differences in any categories. When only considering set play the statistically significant difference in PNR is also evident as when considering all possessions (EURO mean 1.341 ± 1.108; NBA mean 1.149 ± 1.101; p = 0.032).

4.1 Research Deficiencies

One of the major deficiencies in a comparative study across leagues is the challenge of differences in the rules governing the leagues. In Europe, the game is played according to the international FIBA-rules, whereas the NBA has its own set of rules. One of the most important differences is the obvious duration of a game. According to FIBA-rules a game is 40 minutes, and the NBA plays for 48 minutes. The NBA court is slightly bigger, and the 3-point-arc is further away from the hoop in the NBA. Additionally, the NBA has restrictions on the use of zone defense and has also deployed restrictions for the defense in the lane area (Defensive Three-Seconds Rule) - a rule implemented to emphasize 1v1-play as it is considered more spectator friendly by American standards. This could be one of the catalysts for the NBA to play more situations ending with FACE as finishing action. In Europe, a player is allowed to touch the ball (when the ball has touched the rim) within the cylinder above the rim and this is not allowed in the NBA (would be called as offensive interference) - something that could influence the number of putbacks (PUTB) in games. You also find more less evident differences to the governance of rules of definitions regarding criteria for calling fouls. Historically, there have been rule changes both in FIBA and the NBA that can pose a challenge in direct comparison. Regarding efficiency, the PPP is not that widely used (Lehto et al., 2010 Remmert, 2003) which could be an obstacle. PPP is very similar to the traditionally used offensive rating in the NBA. Offensive Rating (ORtg) = PTS/POSS × 100 (Oliver, 2004; Kubatko et al., 2007). For easier comparison more traditional statistics such as FG%, 2pFG%, 3pFG% etc. could be used as they are more widely investigated and more commonly known and used.

Conclusions

The primary goal of this study was to identify and interpret the differences between defined phases and the realization of different types of basketball offenses in American and European top-level basketball. The main findings of the comparative study were that:

1) the types of offense differ between European and NBA basketball, with European teams playing more set plays while the NBA showing more transition and early offense;

2) European basketball had significantly more finishing actions of post up and pick and roll with a higher PPP in pick and roll, while in the NBA 1v1 face to the basket was significantly more common; and 3) European teams also generate a significantly higher number of points per possession during transition and early offense. However, the Americans produce more total points during these phases as the proportion of offenses finished during the transition and early offense phases are higher in the NBA;

This study can help to understand the nature of differences between two analyzed models of professional basketball and establish specific structures that can be constructively interpreted and evaluated and used practically and prospectively in coaching and other professional practices related to basketball.

Recommendations

1) It is recommended that further research is conducted to see if the trend is the same over time as the development of the game is not static. The game styles will change and be inspired by each other and the influx of players that move between Europe and America will also help transfer playing styles. However, the actual development can only be recorded if it is researched.

2) For future studies, it is recommended to include more nuances in the finishing actions; particularly how the pick and roll situations play out. The pick and roll has multiple endings e.g. finish with drive by ball handler, pull up by ball handler, and finish by roll-man; and these sub- 34 finishing actions are very interesting and hold distinct features and qualities that separate them and often relate to how defenses chose to guard the pick and roll. and 3) it is recommended to set a standard in relation to efficiency for easier comparison across all leagues. The NBA uses offensive rating and some European based researchers used points per possession - others again used terms as successful, unsuccessful, neutral and many more. No matter what you do it has to be seen in respect to the league average. For instance, an offensive rating does not necessarily mean a successful team if the defense is bad. Therefore, to compare historical stats you have to take league average into account.

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Annex 1 Article prepared according to guidelines of Journal of Physical Education and Sport.

Difference of Offensive Structure between European and American Top-level Basketball

JAKOB HJORTH JØRGENSEN1,2, ALEKSANDAR SELMANOVIĆ3

1Department of Coaching Science, Lithuanian Sports University, LITHUANIA. 2Faculty of Kinesiology, University of Split, CROATIA. 3Department of Physical Education, University of Dubrovnik, CROATIA.

[email protected]

Abstract: The aim of this study is to determine if there are any structural differences in relation to offensive style of play between European and NBA basketball. The focus of the study is on types of offenses, their distribution, efficiency, and finishing actions. 15 European (11 Euroleague playoff games, 3 EuroCup semifinals and finals, 1 Champions League final) and 15 NBA playoff games were analyzed using video analysis software. The games involved 14 different European teams and 16 NBA teams. A total of 5027 possessions were categorized into 4 types of basic offense and 11 types of finishing actions using notational analysis, where the following findings stand out: a) The results of the Chi2-test confirmed a statistically significant difference in the distribution of basic types of offenses between European and NBA basketball; b) European basketball had significant more finishing actions of pick and roll and post up, whereas in the NBA 1v1 face to the basket occurred significantly more; c) No statistical significant difference in efficiency, except that pick and roll is executed at a significantly higher points per possession in European basketball. The study can help understand the nature of differences between two analyzed models of professional basketball and to establish specific structures that can be interpreted and evaluated constructively and used practicably and prospectively in coaching and other professional practices related to basketball.

Key Words: Basketball, NBA, Euroleague, Offense, Finishing Action, Performance Analysis

Introduction Among basketball scholars and experts there is generally consensus that the National Basketball Association (NBA) is the strongest league in the World as most of the best players and coaches are present in this league. However, other leagues are catching up. The second strongest league is arguably the Euroleague. Games between NBA and Euroleague teams do not occur very often, making direct comparison difficult. When and if they take place, they are often pre-season or promotional games without competitive relevance. Direct comparison is also made difficult because of the difference in the rules governing the leagues. Previous literature has indicated that there is a difference in the offensive preferences of Euroleague and NBA teams (Selmanović et al., 2015; Milanović et al., 2014; Mandić et al., 2019; Selmanović et al., 2019; Erčulj & Štrumbelj, 2015). Other comparative studies dealing with offensive structure at either lower level competition or international competition (Remmert, 2003; Lehto et al., 2010) has given indication for values of points per possession in relation to finishing actions. Additional studies about finishing actions showed different results regarding the preferences in the use of finishing actions (Solsona et al., 2020; Matulaitis & Bietkis, 2021; Zukolo et al., 2019). The aim of this study is to determine if there are any structural differences in relation to offensive style of play between European and NBA basketball. The focus of the study is on types of offenses, their distribution, efficiency, and finishing actions. The main conclusions of this study are that there are differences in the basic offensive structure, regarding transition, early offense, set plays, and other offenses. Additionally, there are found differences in the preference of the use of finishing actions in the two styles of play.

Material & methods 42

The current study protocol was approved by the Lithuanian Sports University Ethics Committee. All research material (games) used for this observational study is available publicly online. Informed consent from athletes was not necessary as games were broadcast on television. All video research was made with Sideline Sports - XPS Video Analyzer. The use of video editing software allows the researcher to perform notational analysis by tagging and storing relevant footage and to export generated data. Data was exported to Microsoft Excel for processing and to RStudio for additional statistical processing. Data was analyzed using the software RStudio (version 1.3.1093; RStudio PBC, Boston, MA, USA). Percentages were calculated using descriptive statistics. For the purpose of testing the difference between the NBA and European styles nonparametric statistical methods were applied, using Chi² test in nominal value (p < 0.05), while t-test was used for independent samples for the purpose of determining differences in variables.

Sample procedure The sample was collected by analyzing 30 games of high qualitative rank of the European and American competition during the 2017-18 season. The NBA games were selected by randomly selecting one game from each series of the NBA playoffs (15 games). By selecting in this manner, you ensure a broad representation of teams (16) in the sample. The European games were selected by choosing the Euroleague Final Four games (4 games), 7 randomly selected games from the Euroleague Playoffs (each team had to be represented at least once), the final and semifinals of EuroCup (3 games), and the final of Champions League (1 game). The finals and semifinals of the presumed inferior European competitions (Champions League and EuroCup) were included to secure a broader representation of teams in the sample (14 teams) because the current format of the Euroleague playoffs only include eight teams qualify, and to assure that the data is not skewed by the preferences of one given team’s style of play more teams have been included. The final games of the other two main competitions were selected to ensure a highly competitive level of playoffs basketball. The selected games generated a total of 5027 entities (n = 5027) of which 2886 entities were selected from 15 NBA games and 2141 entities (n = 2141) selected from 15 European games.

Sample variables - Outcome of the possession - Numerical result - points scored - Type of offense - Type of finishing action - Ratio between offensive and defensive players

In previous research and in literature, possessions have been defined in multiple ways. In the current study, possessions are defined as follows. A possession starts either by: a) inbounding the ball after an opponent’s made basket. b) inbounding the ball after an opponent’s turnover. c) inbounding the ball after a defensive team rebound. d) securing a live ball by a defensive rebound after an opponent’s missed basket. e) securing a live ball by stealing the ball from the opposition. f) in special cases inbounding after one’s own made last free throw (FT) e.g., after technical or flagrant fouls committed by the opponent. g) gaining possession after the initial jump ball (or in NBA only: after possession changing jump ball) A possession ends either by: a) offensive team making a basket. b) defensive team gains possession of the ball. c) a team shooting FT (regardless of a potential miss of the last FT which would allow for an offensive rebound (OReb) and a continuation of offense - in such a case it is considered a new possession) The possession outcome is either: made basket, missed basket, made basket plus 1 free throw, any number of free throws earned with prior made basket, and turnover. The numerical possession outcome is expressed numerically as 0 (miss or turnover), 1 (any number of FT - made 1 FT), 2 (made 2p FG or 2/2 FT, 2/3 FT), 3 (made 3p FG, made 2p FG + 1 FT, 3/3 FT), 4 (made 3p FG + 1 FT). 43

Offense generally occurs in two states, set offense (positional offense) and transition offense (fast break). This paper defines and uses four operational defined types of basketball offense: set (positional), transition, early offense, and other offenses. 1) The set offense or positional offense is the most frequently occurring form of offense (Milanović et al. 2014; Remmert 2003). The set offense is always played with a player formation (PF) of 5v5 with a set defense with defensive control (DC). DC is defined as when the defense initially establishes a controlled 5v5 defensive situation. The set offense is characterized by involving tactical manoeuvres by the offense. The set phase can also include a transitional phase, but the transition phase is then shorter than the set phase. The set offense can be divided into modalities of playing against man-to-man defense or zone defense. Due to the scant use of zone defenses in Euroleague and the rules restricting the use of zone defense in the NBA these modalities have not been deemed important to track in the current study. In this paper, a possession is categorized as a positional play by default, if the final scoring action is preceded by either a sideline out of bounds play (SOB) or baseline out of bounds play (BOB) in the front court. The logic behind this is the defense has ample opportunity to gain DC. 2) The transitional phase is defined as possessions where the defense does not gain DC and most often the PF of the offense outnumber the defense (O>D) or they are even (O=D) - however special transition situations occur where offenses complete plays even though they are outnumbered (O

Types of Finishing Action Eleven types of finishing actions are defined and categorized in this investigation: 1) FACE: Scoring actions when a player attacks facing the basket. The offensive player trying to beat the opposition without any teammates involved while facing the basket. 2) POST: Scoring actions with a player trying to beat the opposition with his back to the basket. Also commonly referred to as posting up. 3) SPOT: Spot up shooting defined by situations when offensive players perform a jump shot after receiving a pass from a teammate without making a move of his own. 4) CUT: Cut to or away from the basket is movement of an offensive player leading to a scoring action without the use of screens or dribbling. 5) DISH: A penetrating player dishing off (very short pass) the ball to an open teammate close to the basket. 6) PNR: Pick and roll is defined as a situation where two offensive players are working together. The ball handler receives a screen (pick) from the screener. The ball handler uses this screen to get off a shot, drive to the basket to finish or to make a pass to the screener who is rolling to the basket. 7) POP: Pick and pop is defined as a situation where two offensive players are working together. The ball handler receives a screen (pick) from the screener. The ball handler uses this screen to draw attention to free up space allowing the screener to perform a jump shot after popping or flaring out. 8) HAND: Handoffs are defined as situations where two offensive players are working together. Initially, the situation resembles the PNR- and POP-situations but the pass made from the ball handler to the other player is made at close range (within touching distance). After the ball handler hands over the ball, he immediately becomes the screener hereafter. 9) OFF: Off-ball screens are situations where the scoring action occurs after the use of screens set away from the ball handler allowing him to pass to an open teammate for a shot or layup. 10) PUTB: Putbacks after offensive rebounds are situations when an offensive player puts himself in a position allowing him to attempt scoring after performing an offensive rebound following a missed shot from a teammate. 11) MISC: Miscellaneous finishing actions that do not fit any of the above listed scoring actions.

Ratio between offensive/defensive players Set plays and early offenses are defined in this investigation as situations with PF of 5v5. If a transition possession is detected it is analyzed and categorized according to the ratio between offense and defense. Hypothetical outputs can 44 range from 1v0 to 5v4 (1v0, 1v1, 1v2, 1v3, 1v4, 1v5, 2v0, 2v1, 2v2, 2v3, 2v4, 2v5, 3v0, 3v1, 3v2, 3v3, 3v4, 3v5, 4v0, 4v1, 4v2, 4v3, 4v4, 4v5, 5v0, 5v1, 5v2, 5v3, 5v4).

Results This study has investigated a total of 30 basketball games from season 2017-18 and a total of 5027 possessions and 1335 minutes played (Table 1). On average in European games, 165 points was scored (min. 143; max. 194; SD 14.7) whereas American games hold an average of 211 points/game (min. 187; max. 249; SD 17.2).

Table 1: Basic descriptive statistics of European and American basketball offenses EURO NBA Games 15 15 Teams in sample 14 16 Minutes played 610 725 Total possessions 2141 2886 Possessions / game* 142.7 192.4 Possessions / minute 3.51 3.98 Points / game* 165 211 Points / minute 4.06 4.36 *includes overtime games

Distribution of Types of Offenses When looking at types of offense in European and American basketball (Table 2), this study found a statistically significant difference (Chi2=54.98, df=3, p<0.001) in the distribution of types of offense: transition, early offense, set plays, and other offenses. Furthermore, when comparing European to American basketball, it is shown that American basketball relies statistically significantly more on transition (Chi2=19.27, p<0.001) and early offense (Chi2=23.86, p<0.001). The Europeans rely more on set play (Chi2=30.88, p<0.001) and other offenses (Chi2=9.19, p=0.002).

Table 2: Types of offense in European and American basketball EURO NBA n % n % Transition 142 6.6 293 10.2 Early offense 249 11.6 477 16.5 Set play 1669 78.0 2049 71.0 Other 81 3.9 67 2.3 Chi2 = 54.98, df = 3, p < 0.001*

Points per Possession in Basic Types of Offenses When investigating points per possession (PPP) on the total sample of all possessions, it was found that there was a statistically significant difference (p=0.047) during transition (Table 3), where European teams produced a statistically 45 significantly larger PPP (1.486±1.023, n=142) compared to the American (1.266±1.103, n=293). The other categories showed no statistically significant (p>0.05) differences.

Table 3: PPP all possessions EURO NBA Mean SD Mean SD T-score p-value Transition 1.486 1.023 1.266 1.103 1.994 0.047* Early offense 1.273 1.160 1.119 1.176 1.679 0.094 Set play 1.128 1.182 1.079 1.166 1.268 0.205 Other 0.753 0.874 0.821 0.869 -0.471 0.638 Total 1.154 1.165 1.098 1.157 1.684 0.092

Transition and Early Offense

Distribution. It was found that the NBA has an overall statistically significantly (p<0.001) higher use of transition and early offense (Trans+EO) compared to their European counterparts (EURO 18.3%, NBA 26.7%). In comparison to Europe, the Americans had the higher usage rates of ratios of transition except from Trans O>D, where no statistically significant difference was found. The NBA (n=101, 2.8%) has a statistically significantly (p=0.022) higher use of transitions with equal ratio between offense and defense (Trans O=D) compared to the Europeans (n=51, 2.0%). The NBA (n=94, 2.6%) also has a statistically significantly (p<0.001) higher use of transitions with offense outnumbered by defense (Trans O

Points per Possession. When investigating PPP specifically during transition and early offense, it was found when looking at all transitions combined the Europeans executed significantly better as stated in Table 4. The Europeans (1.350±1.115) also scored a statistically significantly (p=0.013) higher PPP in the combined transition and early offense situations (Trans+EO) than the Americans (1.175±1.150). In Trans O>D, Trans O=D, Trans O0.05) differences were detected.

Table 4: Transition and Early Offense

EURO NBA

n % n % Chi2 p-value Trans O>D 62 2.5 98 2.7 0.997 0.318 Trans O=D 51 2.0 101 2.8 5.236 0.022* Trans O

Finishing Actions

Fishing Actions - All Possessions. When investigating all possessions combined, this study indicated that there is a difference in the distribution of finishing actions between European and American basketball (Table 5). Furthermore, when looking at all possessions, there is a statistically significant difference (p<0.001) in the use of FACE being more prevalent in America (35.3%, n=895) compared to Europe (25.6%, n=469). The use of PNR is also statistically significantly (p<0.001) more used in Europe (20.6%, n=377) compared to America (13.7%, n=348). Additionally, it occurs that POST is more statistically significantly (p=0.003) present in European basketball 46

(10.4%, n=190) compared to America (7.8%, n=198). In the rest of the finishing actions no statistically significant (p>0.05) differences were found. When investigating PPP no statistically significant (p>0.05) differences in either category except from PNR, where the Europeans (1.329±1.112) scored statistically significantly (p=0.036) higher than the Americans (1.155±1.110).

Table 5: All Possessions: Finishing Actions and Points per Possessions

EURO NBA EURO NBA n % n % Chi2 p-value PPP SD PPP SD T-score p-value FACE 469 25.59 895 35.32 46.913 <0.001* 1.198 1.112 1.153 1.120 0.710 0.478 POST 190 10.37 198 7.81 8.556 0.003* 1.258 0.966 1.273 1.001 -0.148 0.882 SPOT 326 17.79 422 16.65 0.959 0.327 1.482 1.475 1.358 1.461 1.144 0.253 CUT 96 5.24 158 6.24 1.933 0.164 1.708 0.724 1.595 0.814 1.121 0.263 DISH 59 3.22 77 3.04 0.114 0.735 1.475 0.971 1.662 0.788 -1.244 0.216 PNR 377 20.57 348 13.73 35.879 <0.001* 1.329 1.112 1.155 1.110 2.103 0.036* POP 19 1.04 37 1.46 1.508 0.220 1.316 1.455 0.838 1.191 1.318 0.193 HAND 22 1.20 48 1.89 3.248 0.072 1.591 1.260 1.042 1.320 1.639 0.106 OFF 94 5.13 150 5.92 1.263 0.261 1.447 1.250 1.327 1.223 0.740 0.460 PUTB 101 5.51 132 5.21 0.191 0.662 1.406 0.961 1.303 0.949 0.816 0.415 MISC 80 4.36 69 2.72 8.696 0.003* 1.228 0.791 1.145 0.827 0.509 0.611

Chi2 = 87.161, df = 10, p < 0.001*

Finishing Actions - Transition. If you look at transition possessions as isolated cases, there is no statistically significant difference found (Chi2=7.117, p=0.310) when considering totals nor the various finishing actions. When you look at PPP values for the transition phase, there are also no statistically significant (p>0.05) differences, except from SPOT where the Europeans (2.133±1.156) perform statistically significantly (p=0.023) better than the Americans (1.143±1.458).

Finishing Actions - Early Offense. During the early offense phase (Table 9) there is a statistically significant spread in the distribution of finishing actions when looking at totals (Chi2=26.693, p<0.001) and there is found a statistically significant difference in the use of FACE here as well (Chi2=6.903, p=0.009). FACE is the most frequently in both styles of play but it is used 38.4% of the time by the American teams whereas the Europeans use FACE 28.1% of the time. The use of PNR is not statistically significantly (p>0.05) greater in Europe during this phase, nor is the rest of the finishing actions.

Finishing Actions - Set play. The Americans (33.7%) use a statistically significantly (p<0.001) larger amount of FACE (Table 6) compared to the Europeans (24.1%). The Europeans (23.8%) have a statistically significantly (p<0.001) bigger frequency of PNR compared to the Americans (15.7%). The statistically significant (p=0.018) difference of the use of POST is also evident during set plays (EURO 10.8%, NBA 8.4%). Furthermore, there is found a statistically significant difference in the use of SPOT (Chi2=4.026, p=0.044) and CUT (Chi2=4.022, p=0.045), however these results are not significant if you consider Yates’ correction (SPOT: Chi2=3.840, p=0.05005); CUT: Chi2=4.022, p=0.056).

Table 6: Set play: Finishing Actions and Points per Possessions EURO NBA EURO NBA n % n % Chi2 p-value PPP SD PPP SD T-score p-value FACE 345 24.1 603 33.7 35.106 <0.001* 1.154 1.129 1.103 1.137 0.664 0.507 POST 155 10.8 150 8.4 5.583 0.018* 1.200 0.976 1.240 1.015 0.351 0.726 SPOT 267 18.7 286 16.0 4.026 0.044*# 1.404 1.469 1.371 1.459 0.272 0.786 CUT 43 3.0 78 4.4 4.022 0.045*# 1.721 0.766 1.500 0.908 1.351 0.179 DISH 45 3.1 59 3.3 0.058 0.810 1.444 1.013 1.661 0.779 -1.233 0.220 PNR 340 23.8 281 15.7 33.235 <0.001* 1.341 1.108 1.149 1.101 2.151 0.032* POP 17 1.2 32 1.8 1.908 0.167 1.294 1.448 0.750 1.164 1.430 0.159 HAND 21 1.5 38 2.1 1.898 0.168 1.571 1.287 1.237 1.344 0.929 0.357 OFF 93 6.5 143 8.0 2.596 0.107 1.430 1.246 1.329 1.226 0.617 0.538 PUTB 82 5.7 102 5.7 0.002 0.968 1.427 0.969 1.392 0.914 0.249 0.804 MISC 22 1.5 17 1.0 0.130 0.130 1.045 0.899 1.529 0.624 -1.893 0.066 Chi2 = 72.324, df = 10, p < 0.001* #if Yates correction is considered then these categories are not statistically significantly different; SPOT (Chi2 = 3.840, p = 0.05005), CUT (Chi2 = 4.022, p = 0.056)

Discussion According to the criteria set in this investigation, it is evident that the presented results show a significant difference between European and American basketball (Chi2 = 54.98, df = 3, p < 0.001) in terms of distribution of types of offense (set plays, transition, early offense, and other offenses). This confirms that the types of offenses do not have an equal representation in European and American basketball. Furthermore, it is shown that American basketball relies statistically significantly more on transition (Chi2 = 19.27, p-value < 0.001) and early offense (Chi2 = 23.86, p-value < 0.001) whereas European basketball relies statistically significantly more on set plays (Chi2 = 30.88, p-value < 0.001). Other researchers have reported statistically significant differences (Chi2 = 28.13, df = 2, p-value < 0.001) in comparative studies between the Euroleague and the NBA (Milanović et al., 2014; Selmanović et al., 2015). However, the categories of basic offense were slightly different as they only used three categories (set, transition, other) compared to the four used in this study. The results reported were similar in terms of more transition plays present in the NBA. In this study, it was found that transition and early offense (Trans+EO) combined for Euro 18.3% vs. NBA 26.7% (Table 4) compared to Euro 15.1%, NBA 20.2% reported in the other studies. This confirmed the idea that the NBA plays a more straightforward style of play based on quicker, less schematic, and more improvised offensive game. The tendencies were also mentioned by Selmanović et al. (2019) suggesting that the American style of play is more of a hybrid between organized and improvised play. A study on the Beijing Olympics in 2008 reports transition and early offense at 16.6%, based on five games only (Lehto et al., 2010). This investigation has shown that the NBA does not produce statistically significantly more PPP in either of the types of offense or combined totals. On the contrary, it is shown that European basketball (1.486 ± 1.023, n = 142) produced a statistically significantly larger PPP (p < 0.05) in transition compared to the NBA (1.266 ± 1.103, n = 293). These results indicate that European basketball is more conservative and calculating as suggested by Štrumbelj et al. (2013). The NBA is driven more and more by analytics (Davenport, 2014) and therefore it makes sense from an analytics standpoint to get as many transition and early offense situations as possible to maximize offensive output as they are the most efficient possessions (Christman et al., 2018). Transition and early offense were also reported to be the most efficient ways of scoring by other studies (Matulis & Bietkis, 2021). It has previously been reported by Milanović et al. (2014) that the NBA was slightly more efficient than Euroleague when considering transition offense 48

It was expected that the NBA would have a significantly higher use of transition and early offense (Trans+EO). This study confirms that the NBA (n = 770, 26.7%) has an overall statistically significantly higher use of transition and early offense compared to their European counterparts (n = 391, 18.3%). The higher use of transition and early offense in the NBA compared to Europe (20.2% vs. 15.1% respectively) is also reported by other researchers (Milanović et al., 2014; Selmanović et al., 2015). It is also confirmed that the NBA has a more straightforward approach to attacking during the transition and early offense phases if you look at the phases individually. The NBA (2.8%) has a statistically significantly higher use of transitions with equal ratio between offense and defense (Trans O=D) compared to Europe (2.0%). Additionally, the NBA (2.6%, EURO 1.2%) also has a statistically significantly higher use of transitions where the offense is outnumbered by the defense (Trans OD) was rejected as the Americans (n = 98, 2.7%) attack in equal numbers and no significant difference was found (Chi2 = 0.997, p = 0.318). The numbers suggest that the Europeans are as willing as the Americans to attack in transition when outnumbering the opponent. Selmanović et al. (2015) reports a slightly higher use of primary break in Europe (Euro 29.6%, NBA 27.6%), no difference in secondary break (Euro 31.1%, NBA 31.0%), and a small difference in the use of early offense (Euro 39.3%, NBA 41.4%). The study concluded that there was no statistically significant difference in the distribution (Chi2 = 0.607, df = 2, p = 0.7383). This adaptation to different styles is something to consider when the NBA is looking for European talent and vice versa when European clubs look to sign American imports. The NBA is generally doing a good job of evaluating foreign talent for instance in relation to the annual draft (Salador, 2011; Bieniek et al., 2014). Even though the NBA is considered superior, it does not execute better than the Europeans in any transition and early offense categories. Actually, it turns out that when looking at all transitions combined the Europeans execute significantly better. The Europeans (1.350 ± 1.115) also scored a statistically significantly higher PPP in the combined transition and early offense situations than the Americans (1.175 ± 1.150). This could be because the European system is based on a larger degree of schematic offense and control as proposed by Selmanović et al. (2019). This study can confirm there is a clear difference in the preference of the use of finishing actions between European and American basketball (Chi2 = 87.161, p < 0.001). Furthermore, it is found that the use of FACE is statistically significantly more prevalent in the NBA (35.3%, n = 895) compared to Europe (25.6%, n = 469). This can be seen as odd since the NBA to a large extent is analytics driven and FACE is one of the least effective ways of attacking according to this study with a PPP of 1,153 ± 1,120. Another study also reports isolation plays (FACE) in Euroleague to be more likely to end up inefficiently rather than efficiently (Matulis & Bietkis, 2021). According to this study, FACE in European competition ranks the lowest in efficiency with 1,198 ± 1,112 PPP. Even though teams preach unselfish play (Scarlatelli, 2015; Colás, 2012), this does not necessarily transcend into rewards given to players scoring compared to players making assists. It is suggested that a player’s future salary is increased by approximately $22,000 per FG personally scored and decreased by $6,000 per assist made (Uhlmann & Barnes, 2014). This suggests NBA teams tend to financially reward selfish isolation play (FACE) over team-oriented play, which naturally is a huge incentive for players (Smith et al., 2004; Goldman, 2007). The reason for this could be that players that can create something on their own tend to have a high value and big role on teams (Matulis & Bietkis, 2021). The use of PNR is also confirmed to be statistically significantly more used in Europe (20.6%, n = 377) compared to America (13.7%, n = 348). Additionally, it occurs that POST is more present in European basketball (10.4%, n = 190) compared to America (7.8%, n = 198). This could be supported by the assumptions presented by Erčulj & Štrumbelj (2015) who partially explained the absence of the hook shot in the NBA by inferior technique of NBA centers compared to European centers. The inferior skill set of NBA centers in turn could be the reason for NBA teams using POST less frequently than their European counterparts. Selmanović et al. (2019) also reports differences in the use of different finishing actions during the 2011-12 season. The Americans are characterized by a higher frequency of FACE (Euro 13.6%, NBA 19.4%) and SPOT (Euro 15.4%, 16.4%), whereas the Europeans have a higher frequency of PNR (Euro 16.6%, NBA 15.3%), POST (Euro 7.9%, 5.9%), and PUTB (Euro 4.3%, NBA 3.8%). This means that the two main categories (FACE, PNR) come out with similar findings in the current study and the earlier studies done by Selmanović and colleagues. POST is also found to be prevalently used in European basketball as mentioned by Selmanović et al. (2019). Remmert (2003) reported FACE 17.2%, POST 13.5%, and PNR 16.4% in a study on mixed leagues which therefore is not directly comparable. During set plays there are statistically significant differences found in relation to the use of FACE in the NBA and the prevalence in Europe towards PNR. Furthermore, POST and SPOT are significantly more frequent in Europe whereas CUT is significantly more frequent in the NBA. 49

The Europeans (Mean 1.329 ± 1.112) produce a statistically significantly higher PPP in PNR than the Americans (Mean 1.155 ± 1.110) when considering all possessions (p = 0.036). If you consider each type of offense, then the results suggest that the Europeans (2.133 ± 1.356) produce a statistically significantly higher PPP (p = 0.023) in SPOT than the Americans (1.143 ± 1.458) during the transition phase. During early offense there are found no statistically significantly differences in any categories. When only considering set play, the statistically significant difference in PNR is also evident as when considering all possessions (EURO mean 1.341 ± 1.108; NBA mean 1.149 ± 1.101; p = 0.032).

Research Deficiencies One of the major deficiencies in a comparative study across leagues is the challenge of differences in the rules governing the leagues. In Europe the game is played according to the international FIBA-rules, whereas the NBA has its own set of rules. One of the most important differences is the obvious duration of a game. According to FIBA-rules a game is 40 minutes, and the NBA plays for 48 minutes. The NBA court is slightly bigger, and the 3-point-arc is further away from the hoop in the NBA. Additionally, the NBA has restrictions on the use of zone defense and has also deployed restrictions for the defense in the lane area (Defensive Three-Seconds Rule) - a rule implemented to emphasize 1v1-play as it is considered more spectator friendly by American standards. This could be one of the catalysts for the NBA to play more situations ending with FACE as finishing action. In Europe, a player is allowed to touch the ball (when the ball has touched the rim) within the cylinder above the rim and this is not allowed in the NBA (would be called as offensive interference) - something that could influence the number of putbacks (PUTB) in games. You also find more less evident differences to the governance of rules of definitions regarding criteria for calling fouls. Historically, there have been rule changes both in FIBA and the NBA that can pose a challenge in direct comparison. Regarding efficiency, the PPP is not that widely used (Lehto et al., 2010 Remmert, 2003) which could be an obstacle. PPP is very similar to the traditionally used offensive rating in the NBA. Offensive Rating (ORtgt) = PTSt/POSSt × 100 (Oliver, 2004; Kubatko et al., 2007). For easier comparison more traditional statistics such as FG%, 2pFG%, 3pFG% etc. could be used as they are more widely indicated and more commonly known and used.

Suggestions and Recommendations It is recommended that further research is conducted to see if the trend is the same over time as the development of the game is not static. The game styles will change and be inspired by each other and the influx of players that move between Europe and America will also help transfer playing styles. However, the actual development can only be recorded if it is researched. For future studies, it is recommended to include more nuances in the finishing actions; particularly how the pick and roll situations play out. The pick and roll has multiple endings e.g. finish with drive by ball handler, pull up by ball handler, and finish by roll-man; and these sub-finishing actions are very interesting and hold distinct features and qualities that separate them and often relate to how defenses chose to guard the pick and roll and it is also recommended to set a standard in relation to efficiency for easier comparison across all leagues. The NBA uses offensive rating and some European based researchers used points per possession - others again used terms as successful, unsuccessful, neutral and many more. No matter what you do it has to be seen in respect to the league average. For instance, an offensive rating does not necessarily mean a successful team if the defense is bad. Therefore, to compare historical stats you have to take league average into account.

Conclusions The primary goal of this study was to identify and interpret the differences between defined phase types and the realization of different types of basketball offenses in American and European top-level basketball. The main findings of the comparative study were that: 1) the types of offense differ between European and NBA basketball, with European teams playing more set plays while the NBA showing more transition and early offense; 2) European basketball had significantly more finishing actions of post-up and pick and roll with a higher PPP in pick and roll, while in the NBA 1v1 face to the basket was significantly more common; and 3) European teams also generate a significantly higher number of points per possession during transition and early offense. However, the Americans produce more total points during these phases as the proportion of offenses finished during the transition and early offense phases are higher in the NBA. The study can help to understand the nature of differences between two analyzed models of professional basketball and establish specific structures that can be constructively interpreted and evaluated and used practically and prospectively in coaching and other professional practices related to basketball. Generally, the results could also be interpreted as a testament to the more deliberate and schematic style of European teams. On the other hand, in 50

American basketball the total amount of points scored during transition and early offense is higher due to a larger volume. Defining relevant structures of the game allows coaches and players to better understand the underlying systems and should thereby be better suited to perform successfully.

Conflicts of interests: The authors declares no conflict of interest.

References:

Bieniek, P., Augustyn, K., Zubik, M., & Kwater, K. (2014). Diversification of game-related indicators in novice NBA players. Journal of Health Sciences, 4(3), 141-147.

Davenport, T. H. (2014). Analytics in sports: The new science of winning. International Institute for Analytics, 2, 1- 28.