Available Online at http://www.recentscientific.com International Journal of CODEN: IJRSFP (USA) Recent Scientific

International Journal of Recent Scientific Research Research Vol. 10, Issue, 09(B), pp. 34607-34617, September, 2019 ISSN: 0976-3031 DOI: 10.24327/IJRSR Research Article

QUANTITATIVE ANALYSIS OF 2017 FIBA ZONE CHAMPIONSHIPS BASED ON A DISCRIMINANT REGRESSION MODEL

Slobodan Simović*1, Filip Jovanović1, Jasmin Komić2, Bojan Matković3 and Zoran Pajić4

1University of Banja Luka, Faculty of Physical Education and Sport, Banja Luka, the Republic of Srpska, Bosnia and Herzegovina 2University of Banja Luka, Faculty of Economy, Banja Luka, the Republic of Srpska, Bosnia and Herzegovina 3University of Zagreb, Faculty of Kinesiology, Zagreb, Croatia 4University of Belgrade, Faculty of Sport and Physical Education, Belgrade, Serbia

DOI: http://dx.doi.org/10.24327/ijrsr.2019.1009.3933

ARTICLE INFO ABSTRACT

This study attempts to ascertain whether there are differences or similarities in the way basketball is Article History: played across the world by observing four top-tier competitions on different continents. The results th Received 4 June, 2019 show that the 2017 continental zone championships are different in four variables. On the other th Received in revised form 25 July, 2019 hand, the field goals attempted variable proved to be the variable derived in all first iterations of the rd Accepted 23 August, 2019 absolute indicator model, but also in three continental championships in the relative model, Africa th Published online 28 September, 2019 being the exception. When it comes to the steals percentage of the efficiency variable, as a rule it showed a difference between the winning and losing teams only in the American championship. The very existence of the percentage of personal foul efficiency variable, which did show a difference, is Key Words: quite disputable, although the results of this study indicate that further research should attempt to

Differences, loser, quantitative find an objective measurement method for this variable. information, variables, winner, zone basketball championships

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INTRODUCTION order to provide interpretation of specific situations and issues encountered in sports practice. The number of studies In terms of the volume of scientific research, basketball is one employing quantitative analysis in basketball by using of the most analysed sports (Mikolajec et al, 2013). The statistical data gathered during a match has especially grown various analytical approaches expand the knowledge and define after the year 2000 (Kubatko et al, 2007). the maxims of the game of basketball, contributing to a better understanding of the game and to its continual development The transformation of events on the court into numbers (Selmanović, 2016). Basketball performance indicators are interpretable by the coach allows for targeted training and usually explored using biomechanical and notational analysis remedying deficiencies of both the individual players and the (Hughes and Bartlett, 2002; Sindik, 2015). team as a whole (Ciampolini et al, 2018). The advancement of technology and the need for a better and deeper understanding Notational analysis uses match data, techniques and tactics to of the technical-tactical player performance are making this derive information on the technical, tactical, physical and topic more and more interesting to academic circles (Gómez et psychological requirements of basketball (Hughes and Franks, al, 2013; Vilar et al, 2012). Discriminant analysis is used 2004). It is an objective way of recording performance particularly often to identify the game-related statistics which indicators, providing for a consistent quantification of key discriminate between winning and losing teams in a league or events, and subsequently, accurate and objective quantitative tournament. (Sampaio et al, 2013). and qualitative feedback (Sporiš et al, 2014). Ever since the early beginnings of notational analysis in basketball (Fay and Accordingly, various comparisons have been made between Messersmith, 1938), literature employed various methods of leagues and tournaments. There have been particularly many performance analysis (popularly known as match statistics), in comparisons of European and American leagues (primarily the

*Corresponding author: Slobodan Simović University of Banja Luka, Faculty of Physical Education and Sport, Banja Luka, the Republic of Srpska, Bosnia and Herzegovina Slobodan Simović et al., Quantitative analysis of 2017 Fiba Zone Championships Based on A Discriminant Regression Model

NBA), to draw conclusions on the differences and similarities License” is defined in Chapter 8: Statistics (pp. 48-49) of the between the two playing styles (Mavridis et al, 2009; “FIBA Internal Regulations: Book 3, Players and Officials” Milanović et al, 2014; Paulauskas et al, 2018; Selmanović et (2017). It is exactly because the whole process of data al, 2017), as well as some other basketball competitions (Crum, gathering has been stipulated by FIBA that the data gathered in 2013). At the same time, one should always bear in mind the such a manner is considered reliable in basketball research problems in comparing different competition systems, as well (Garcia et al, 2013; Paulauskaset al, 2018). Also, previous as differences in game rules (Lukšić, 2001). similar studies which used FIBA data confirmed their high reliability (Ibáñez et al, 2018; Gómez et al, 2018; Sampaio et Comparisons of national team competitions under the al, 2010). Federation of International Basketball Associations are getting increasingly interesting to authors. The reasons are primarily Gilles Celeux and Valérie Robert pointed out that “Basketball the same game rules and similar competition systems, where is a wonderful sport for statistics. After each game, a box score the differences are mainly in the number of competing teams. is made available. This box score provides for each player and Lately there has been a number of papers studying the each team, quantitative information about 15 variables” (2015, differences among continental championships. This topic was p. 51). It is in this paper as well that these variables consisted particularly studied by Japanese scientist Haruhiko Madarame, of the manifest variables taken from the FIBA official website, who examined the differences between winning and losing where the data is stored (“2017 FIBA Afrobasket”, 2017; teams at the men’s FIBA Asia Championships and EuroBasket “2017 FIBA AmeriCup”, 2017; “2017 FIBA Asia Cup”, 2017; (Madarame, 2017), the differences in rebounds in women’s “2017 FIBA Eurobasket”, 2017): PST - total points, A2 - 2 continental championships in Asia and Europe (Madarame, points attempted, M2 - 2 points made, A3 - 3 points attempted, 2018a), four continental championships for women in 2017 M3 - 3 points made, AFG - field goals attempted, MFG - field (Madarame, 2018b) and, in two papers, differences among four goals made, AFT - free throws, MFT - free throws made, OR - continental championships for U18 women in 2016 offensive rebounds, DR - defensive rebounds, TOTR - total (Madarame, 2018c, 2018d). Also, Ibáñez Sergio Joséet al. rebounds, AS - assists, PF - personal fouls, TO - turnovers, ST (2018) examined “the performance indicators differentiating - steals, and BS - blocked shots. between five continental championships in 2015” (p. 44). It is Alongside these absolute variables, this paper included the worth noting that senior continental championships are following variables with relative values: 2PTS% - two-point analysed in only two papers (Madarame, 2018b; Ibáñez et al, percentage (M2/A2)*100, 3PTS% - three-point percentage 2018), and that the second paper dealt with male basketball (M3/A3)*100, FG% - field goals percentage players in 2015, where the authors had a problem in that “... (MFG/AFG)*100, FT% - free throws percentage FIBA Oceania championship was eliminated from the final (MFT/AFT)*100, OR% - efficiency percentage of offensive sample... (only two teams take part and they play a two game rebounds in relation to field points missed {OR/(FGA- home-and-away playoff)” (Ibáñez et al, 2018, p. 44). FGM)+[(FTA-FTM)=2]}*100, DR% - efficiency percentage of The aim of this study is to ascertain the differences and defence rebounds in relation to field points missed by opponent similarities in the way basketball is played around the world, {DR/(FGAopp-FGMopp)+[(FTAopp-FTMopp)=2]}*100, establishing whether the points difference in a match in these AS% - percentage of assist efficiency (AS/MFG)*100, PCTPF competitions is a function of the differences of quantitatively - percentage of personal foul efficiency (PF/BP)*100, TO% - represented recorded absolute and relative indicators of turnover percentage of inefficiency (TO/BP)*100, ST% - steals situational efficiency of the game of basketball. percentage of efficiency (ST/BPopp)*100; BS% - block percentage of efficiency (BS/ BPopp)*100, and TBP - team MATERIALS AND METHODS ball possession AFG + 0.5 x FTA - ORB + TO. The sample of entities in this paper consisted of games played during the 2017 FIBA continental championships. Table 1 provides the basic information on these competitions.

Table 1 Basic information on the 2017 FIBA continental championships

No. of Place Time National Teams games (n=16): Angola, Cameroon, Central African Republic, DR Congo, , Egypt, Guinea, Ivory Coast, Mali, 1. 60 Senegal & Tunisia Sep. 8-16 Morocco, Mozambique, Nigeria, Rwanda, Senegal, South Africa, Tunisia, Uganda Argentina, Columbia, Aug. 25 – Sep. (n=12): Argentina, Brazil, Canada, Columbia, Dominican Republic, Mexico, Panama, Puerto Rico, 2. 22 & Uruguay 3 United States, Uruguay, Venezuela, Virgin Islands (n=16): Australia, China, Chinese Taipei, Hong Kong, India, Iran, Iraq, Japan, Jordan, Kazakhstan, 3. 40 Lebanon Aug 8-20 Lebanon, New Zealand, Philippines, Qatar, South Korea, Syria Turkey, Finland, Aug. 31 – Sep. (n=24): Belgium, Croatia, Czech Republic, Finland, France, Georgia, Germany, Great Britain, Greece, 4. 76 Israel, & Romania 17 Hungary, Iceland, Israel, Italy, Latvia, Lithuania

Key: 1. - 29thAfroBasket 2017; 2. - 18th 2017 FIBA AmeriCup; 3. - 29th 2017 FIBA Asia Cup; 4. - 40th EuroBasket 2017.

This study did not evaluate the reliability of the gathered Other literature may offer different formulas for calculating statistical data, which some authors point out as an issue relative variables of the standardly observed parameters of the (Madarame, 2018c). The method of gathering data during a basketball game. This paper adopted formulas recommended basketball game is objective and standardized in all game by SlavkoTrninić (1996), while the TBP variable was situations (“FIBA Statisticians' Manual 2016”, 2016). calculated using a formula designed by Dean Oliver (2004). Furthermore, the manner of obtaining the “FIBA Statistician 34608 | P a g e International Journal of Recent Scientific Research Vol. 10, Issue, 09(B), pp. 34607-34617, September, 2019

The points difference (which ultimately provides the winner of RESULTS a match) between the winning and the losing team was labelled ΔPTS. This difference is a function of all the individual Table 2 shows the results of the difference of the mean values differences of the observed game parameters. Impact of the first model parameters using one-way ANOVA, where it evaluation is based on deriving a multiple linear regression can be seen that two variables, ΔMFG (F(1, 3) = 2.95, p = model in which ΔPTS stands as a subordinate variable, while .034) and ΔAS (F(1, 3) = 3.32, p = .021) have statistical the differences (Δ) of other parameters of the game stand as significance. insubordinate (independent) variables; and on the selection of Table 2 Differences of parameters of the first model at 2017 variables in the set of regression aiming to point to the specific continental championships (ANOVA) weight of each observed variable (SimovićandKomić, 2008). 2017 Continental Championships Two basic regression models were formed for the purposes of Variables F p 1 2 3 4 this paper. Both models incorporated the same dependent ΔA2 2.03 -1.77 1.23 -.57 1.13 .340 variable, ΔPTS. The first model was designed to represent a set ΔM2 4.68 2.82 4.30 1.91 2.37 .072 of insubordinate variables comprising the differences of all ΔA3 -.77 2.27 1.40 .32 .79 .500 absolute parameters of the standardly observed game ΔM3 1.57 2.45 2.20 1.80 .33 .802 parameters: ΔAFG 1.27 .50 2.63 -.25 .69 .500 ΔMFG 6.25 5.27 6.28 3.71 2.95 .034 ∆ ΔAFT 3.37 1.82 3.88 5.45 .97 .407 ΔMFT 2.33 1.50 2.90 4.74 1.77 .155 ∆2, ∆2, ∆3, ∆3, ∆, ∆, ∆, ∆, = ΔDR 5.97 4.36 5.60 5.14 .36 .782 ∆, ∆, ∆, ∆, ∆, ∆, ∆, ∆ ΔOR 1.63 -1.00 1.93 1.24 1.16 .327 = + + + + + + ΔTOTR 7.60 3.36 7.53 6.58 1.33 .266 + + + + ΔAS 5.22 5.45 6.70 3.07 3.32 .021 ΔPF -1.45 -1.23 -2.10 -2.53 .67 .574 + + + + ΔTO .08 -1.68 -2.63 -.66 2.26 .083 + + + ΔST 1.77 2.41 2.18 .57 2.20 .089 ΔBS 1.28 .41 .63 .70 .78 .506 where the variable labels are: = ∆2, = ∆2, = th th th Key: 1) - 29 AfroBasket 2017; 2. - 18 2017 FIBA AmeriCup; 3. - 29 2017 FIBA Asia th ∆3, = ∆3, = ∆, = ∆, = ∆, Cup; 4. - 40 EuroBasket 2017.

= ∆, = ∆, = ∆, = ∆, Table 3 shows the results of the difference of the mean values = ∆, = ∆, = ∆, = ∆, and of the second model parameters using one-way ANOVA, = ∆. where it can be seen that two variables, ΔPF% (F(1, ) = 3.92, p The second model was designed to include all insubordinate = .010) and ΔST% (F(1, ) = 2.62, p = .052) have statistical variables of the relative, derived parameters of the game: significance.

∆2%, ∆3%, ∆%, ∆%, ∆%, Table 3 Differences of parameters of the second model at 2017 ∆ = ∆%, ∆%, ∆%, ∆%, ∆%, ∆% continental championships (ANOVA)

= + + + + + + 2017 Continental Championships F p Variables + + + + 1 2 3 4 + + Δ2% 9.91 9.05 9.00 5.62 1.66 .177 Δ3% 7.38 7.45 9.37 7.21 .24 .866 where the variable labels are: = ∆2%, = ∆3%, ΔFG% 9.37 7.49 8.18 6.18 1.44 .234 ΔFT% .41 -1.28 2.70 2.70 .38 .788 = ∆%, = ∆%, = ∆%, = ∆%, = ∆%, = ∆%, = ∆%, = ∆%, and ΔDR% 6.54 9.82 7.99 6.87 .30 .825 ΔOR% 7.60 .97 8.92 7.51 1.81 .146 = ∆%. ΔAS% 6.44 7.65 8.61 1.68 1.26 .288 ΔPF% -1.56 -1.46 -2.89 -7.59 3.92 .010 Mutual relations among the established variables were studied ΔTO% -.19 -2.29 -3.37 .73 2.29 .079 through regression and correlation analysis of the designed ΔST% 1.02 3.12 2.86 -.04 2.62 .052 regression models using gradual regression (stepwise), which ΔBS% 1.63 .64 .77 .73 .64 .592 defined the conditions for gradual regression for inclusion and Key: 1. - 29thAfroBasket 2017; 2. - 18th 2017 FIBA AmeriCup; 3. - 29th 2017 FIBA Asia Cup; 4. - 40th EuroBasket 2017. exclusion of variables in the model - in particular, criterion F for the inclusion of variables into the equation is of .05 The obtained regression models and the partial correlation significance, and. 10 for exclusion (standard values). coefficients provide for the conclusion that in the first model Standardization at this level ensured consistency and (Figure 1), the final scores of matches played at the 2017 comparability of results at different levels and in different time continental championships were influenced by variables ΔMFG periods. Also, the determined variables and their parameters (β = .94, p ˂ .000, rp = .98), ΔMFT (β = .61, p ˂ .000, rp = .96) were examined in terms of the level of significance they and ΔM3 (β = .35, p ˂ .000, rp = .89). If we break down the exhibited (t-test and F test), all with an aim to obtain well- results to the individual continental championships, we can see defined models providing ground for valid extrapolation. Also, that in the first model, the final scores of matches played at the the differences among competitions were established by one- Afro Basket were influenced by variables ΔMFG (β = 1.34, p ˂ way ANOVA arithmetic means of the observed variables .000, rp = .99), ΔMFT (β = .55, p ˂ .000, rp = .98) and ΔM2 (β across the competitions. = -.53, p ˂ .000, rp = -.96); at the AmeriCup ΔMFG (β = 1.00, p ˂ .000, rp = .97) and ΔMFT (β = .41, p ˂ .000, rp = .84); at the Asia Cup ΔMFG (β = .91, p ˂ .000, rp = .98), ΔMFT (β = .40, p ˂ .000, rp = .90) and ΔM3 (β = .27, p ˂ .000, rp = .81); 34609 | P a g e Slobodan Simović et al., Quantitative analysis of 2017 Fiba Zone Championships Based on A Discriminant Regression Model and at the EuroBasket ΔMFG (β = .97, p ˂ .000, rp = .97), It is worth noting that in the present study, the number of ΔMFT (β = .86, p ˂ .000, rp = .95) and ΔM3 (β = .44, p ˂ .000, iterations was determined in such a way that when an iteration rp = .88). Partial correlation values (rp) are particularly presented a variable with partial correlation rp˂.40 (indicating noteworthy, as they express the significance of the influence slight correlation), the previous iteration was taken as the final that certain selected variables have on the final score of the one. The same behaviour could be seen by the slight increase of game. At the same time, the influence of the other variables is adjusted R square (R2). deemed to be unaffected. All models show significant correlation between subordinate variable (ΔPTS) and the set of insubordinate variables included in the model. In the model based on absolute variables: the third iteration for AfroBasket explains 98.9% of the phenomenon (R2 = .99, F(3, 56) = 1833.34, p ˂ .000), the second iteration for AmeriCup explains 93.1% of the phenomenon (R2 = .94, F(2, 19) = 141.97, p ˂ .000), the third iteration for Asia Cup explains 95.9% of the phenomenon (R2 = .96, F(3, 36) = 306.36, p ˂ .000) and the third iteration for EuroBasket explains 94.6% of the phenomenon (R2 = .95, F(3, 72) = 440.74, p ˂ .000). When it comes to the results of all four 2017 continental championships, in the obtained model the third iteration explains 97.1% of the phenomenon (R2 = .97, F(3, 194) = 9156.00, p ˂ .000). Since the determining coefficient is R2 ˂ 0.90 in all five observed models, it can be

concluded that, according to the Chaddock scale, there is a very Figure 1 Variables included in the first model at the 2017 FIBA Continental high positive correlation between the subordinate variable PTS, Championships i.e. ΔPTS, and the respective sets of insubordinate variables. In the second model (Figure 2), the final score of games played In the model based on relative variables: the fourth iteration for at the 2017 continental championships was influenced by AfroBasket explains 73.0% of the phenomenon (R2 = .73, F(4, variables ΔFG% (β = .66, p ˂ .000, rp = .79), ΔTO% (β = -.48, 55) = 1774.16, p ˂ .000), the third iteration for AmeriCup 2 p ˂ .000, rp = -.69), ΔOR% (β = .44, p ˂ .000, rp = .65), ΔFT% explains 82.1% of the phenomenon (R = .82, F(3, 18) = (β = .26, p ˂ .000, rp = .47) and Δ3% (β = .26, p ˂ .000, rp = 815.29, p ˂ .000), the third iteration for Asia Cup explains .42). When we break down the results to the individual 84.4% of the phenomenon (R2 = .84, F(3, 36) = 2247.55, p ˂ continental championships, we can see that in the second model .000) and the fifth iteration for EuroBasket explains 79.4% of the final scores at the AfroBasket were influenced by variables the phenomenon (R2 = .79, F(5, 70) = 1219.23, p ˂ .000). Δ2% (β = .63, p ˂ .000, rp = .78), ΔTO% (β = -.46, p ˂ .000, rp When it comes to the results of all four 2017 continental = -.67), Δ3% (β = .38, p ˂ .000, rp = .60) and ΔDR% (β = .34, p championships, in the obtained model the fifth iteration 2 ˂ .000, rp = .55); at the AmeriCup ΔFG% (β = .80, p ˂ .000, rp explains 76.8% of the phenomenon (R = .77, F(5, 192) = = .87), ΔST (β = .40, p ˂ .000, rp = .70) and ΔOR (β = .25, p ˂ 4374.80, p ˂ .000). Since the size of correlation of the results in .000, rp = .48); at the Asia Cup ΔFG% (β = .85, p ˂ .000, rp = all five models ranges from .70 to .90 on the Chaddock scale, it .91), ΔTO (β = -.44, p ˂ .000, rp = -.75) and ΔDR (β = .38, p ˂ can be said that there is a very high positive correlation .000, rp = .71; and at the EuroBasket ΔFG% (β = .71, p ˂ .000, between the subordinate variable and the respective sets of rp = .81), ΔDR% (β = .47, p ˂ .000, rp = .70), ΔTO% (β = -.57, insubordinate variables. p ˂ .000, r = -.72), ΔPF% (β = -.47, p ˂ .000, r = -.68) and p p DISCUSSION Δ3% (β = -.37, p ˂ .000, rp = -.59). The 2017 continental basketball competitions were organized by the FIBA, so the present study was not hindered by any differences in rules and competition systems, which authors of some previous studies have pointed out as possible issues (Lukšić, 2001).

Out of the 213 national associations under the auspices of FIBA in 2018 (Madarame, 2018b), 31.92% qualified for final continental tournaments in 2017 (n = 68). The championships were held at the same time of the year with a span of a little over one month (8 August to 17 September). During our research we did not have the problems that Ibáñez Sergio José et al. encountered (2018) when they studied continental championships in 2015 and had to eliminate the results from the FIBA Oceania Championship, because only two national teams participated, with two matches played between them. Namely, the 2015 Oceania Championship was the last to be

held as from 2017, the tournament merged with the former Figure 2 Variables included in the second model at the 2017 FIBA Continental FIBA Asia Championship. Championships 34610 | P a g e International Journal of Recent Scientific Research Vol. 10, Issue, 09(B), pp. 34607-34617, September, 2019

Two variables, ΔFGM and ΔAS, proved to be statistically in basketball, and shooting is the backbone of the game” (cited significant when it comes to the differences among the four in Hoover, 2012, p. 11). The previous studies done according to zone basketball championships in 2017. Although this study this model also showed a predominance of shot variables in determined that there is a difference in 2017 continental both models (Simović et al, 2012; SimovićandKomić, 2008; championships in the assists variable, this variable did not Simović et al, 2019). prove to be statistically significant for the final score in any of In all models, the second place variable is ΔMFT, which has a the observed models. Assists were ascertained as a variable that high partial correlation with ΔPTS: AfroBasket (r = .98), makes a statistically significant difference among the p EuroBasket (r = .95), Asia Cup (r = .90) and AmeriCup (r = continental championships by other studies as well. For p p p .84). instance, Sergio José Ibáñez et al. (2018), when analysing 2015 zone championships, established that “... in Europe basketball The significance of free throws for the final score was teams are more similar in level and team work (assists)” (p. ascertained in other studies as well (Csataljay et al, 2009; Lyra 51), and the difference was also confirmed by Haruhiko et al, 2017; Reano et al, 2006; SampaioandJaneira, 2003; Madarame (2018d) in his study of the differences among the Sampaio et al, 2006; Tavares and Gomes, 2003). 2016 U18 zone competitions. The difference in assists between At the EuroBasket (r = .88) and Asia Cup (r = .81), the the Asia Cup and EuroBasket 2011, 2013 and 2015 was also p p variable ΔM3 displayed a very high partial correlation with confirmed by Madarame (2017), although only in unbalanced ΔPTS. The number of three-point shots that discriminates games (final score ≥16 points). The larger number of assists winning teams was ascertained in other studies as well (Çene, indicates a higher level of team (organized) play, since it 2018; Choiet al, 2006; de Carvalhoet al, 2017; Ibáñez et results in passes followed by easy field goals, as emphasized in al,2009; Lorenzo et al,2010). On the other hand, instead of the study by Javier García Rubio et al. (2015). The differences ΔM3, the discriminating variable at the AfroBasket was ΔM2 in assists among the continental championships indicate that (r = .96), whose significance was also identified in previous the playing styles and levels of tactical skills differ across the p studies (Çene, 2018; Choi et al, 2006; Garcíaet al, 2013; individual regions of the world. This is corroborated by a larger Gomez et al, 2006a, 2006b; Gomez et al, 2008; Ibáñez et number of balanced games (PST = ≤15) and fewer ball al,2009; Lorenzo et al, 2010; SampaioandJaneira, 2003; possessions (i.e. number of attacking plays), where Europe is in Sampio et al, 2015). the lead. Haruhiko Madarame (2017) reached the same conclusions. This indicates that the other three championships The question is then raised why the variable ΔM2 proved to were played at a faster pace than the Eurobasket. This is more significant at the AfroBasket than ΔM3, and why did nothing new, because as much as fifteen years ago studies neither of these two variables prove to be significant at the indicated that better teams have fewer ball possessions along AmeriCup? Comparison of the 2015 continental championships with a higher offence efficiency coefficient (Ibáñezet al. 2003). showed that the highest differences were “...between Unlike AS, the other variable, MFG, not only showed a Eurobasket and the Asia Cup and Afrobasket championships, difference among the 2017 zone championships, but also with the FIBA America championship revealing values proved to be a variable that was obtained in all first iterations somewhere in the middle” (Ibáñez et al, 2018, p. 49). The of the models which observed absolute game indicators. This reasons for this may be found by analysing defensive play variable explains 62% of success, i.e. wins in basketball games (Csataljayet al, 2009). Studies have established a relation of the 2017 continental championships (R2 = .62, F(1, 196) = between shot efficiency and the level of pressure and 320.78, p ˂ .000): from 38% at the EuroBasket (R2 = .38, F(1, aggression exerted by defence players on players attempting 74) = 47.52, p ˂ .000) to 68% at the AfroBasket (R2 = .68, F(1, field goals (Alvarez et al, 2009; Ibáñez et al, 2007; Ortega and 58) = 123.87, p ˂ .000), 76% at the Asia Cup (R2 = .76, F(1, Fernandez, 2007). Studies have also shown a seemingly 38) = 127.24, p ˂ .000) and 77% at the AmeriCup (R2 = .77, illogical fact that winning teams are more efficient in three- F(1, 20) = 141.97, p ˂ .000). In the second model, with relative point shots than in shots closer to the rim (Csataljay et al, variables, the variables ΔFG% was obtained in the first 2013). The reason lies in the fact that most basketball coaches iteration and explains 44% of the phenomenon (R2 = .44, F(1, base their defence philosophies on stopping layups from close 196) = 157.09, p ˂ .000) at three zone championships: distance and 3 point shots from set position after out passes AmeriCup (R2 = .55, F(1, 20) = 27.12, p ˂ .000), Asia Cup (R2 from penetration or from low post moves (Messina, 2011). = .49, F(1, 38) = 38.27, p ˂ .000) and EuroBasket (R2 = .49, Evidently, the answer to the question raised earlier could be F(1, 38) = 38.27, p ˂ .000). Other studies have reached similar inferior, less aggressive defensive pressure. results (Csataljayet al, 2012; Gomez et al, 2008; Ibáñez et al, Basketball experts have long noticed that the sheer sum of 2008; Karapidiset al, 2001; Leichtet al, 2017; Malarranha et al, gathered indicators is not in itself sufficient for a detailed 2013; SimovićandKomić, 2008; Witkos, 2010). analysis of all events taking place during a game (Simovićet al, It should be noted that in the first model, all the singled out 2012). One of the first authors who have dealt with this variables that are marked as significant for winning a game in problem was Dean Smith (1999), in the first edition of his 1982 basketball pertain to shot efficiency. It is a generally accepted book “Basketball, multiple offense and defense”, where he notion that shooting is the most important basketball skill. emphasised, discussing rebounding in basketball, that the sheer Although the other basketball skills that we call fundamental number of rebounds is not a reliable indicator, but the (passing, dribbling, defence and rebounding) may provide a efficiency of rebounds instead. All of this lead to the practice of high shot percentage for a player, he or she still has to be able not using relative statistical indicators as is, but trying to to score (Oliver, 2004; Pietteet al, 2010). As Bill Sharman put mathematically model different formulas which should aid in it simply, “He wins who scores more points than his opponents making conclusions on game performance (Aizemberget al, 34611 | P a g e Slobodan Simović et al., Quantitative analysis of 2017 Fiba Zone Championships Based on A Discriminant Regression Model

2014; Csataljay et al, 2010; Kubatko et al, 2007; Mikołajec et was singled out as the variable that showed a difference among al, 2013). the 2017 continental championships. Steals indicate the quality of the team defence (Gómez et al. 2009), and “steals also mark If we analyse the efficiency of standardly observed parameters the difference in level between the participating teams” (Ibáñez it can be noticed that, as previously stated, the variable ΔFG et al. 2018, p. 50). Further, steals enable easy points from had an influence on the final score of 44%, with a statistically transition offences and reduce the number of passes (Swalgin, high partial correlation (rp = .79). 2014) and offence duration, which is a measure of play The second iteration, explaining 56% of the influence on the intensity (Bazanov, 2007). Different authors have differing final score, singles out the variable ΔTO% (R2 = .56, F(2, 195) views on the influence of intensity on situational efficiency. On = 123.65, p ˂ .000). This variable is singled out at the Asia the one hand, it was established that longer offences with more Cup, AfroBasket and EuroBasket, while at the AmeriCup it did passes improves cooperation and is beneficial to finding a shot not prove to be significant for the final score of games, but opportunity closer to the rim, thus increasing efficiency instead the variable ΔST%, which, along with ΔFG% explained (Stavropoulos andFoundalis, 2005). On the other hand, Dean 78% of the phenomenon (R2 = .78, F(2, 19) = 38.03, p ˂ .000). Oliver (2001) ascertained that the emphasis on defence that The significance of turnovers to the final score in a game was emerged in the 1990s significantly lowered the pace of the indicated by other authors as well (de Carvalho et al, 2017; game, and that having to spend more time on finding a shot Lorenzo et al, 2010; Nakić, 2004, Simović et al, 2019), and opportunity has taken a negative toll on offence efficiency. cited as the most common reasons are bad passes in 40.00% of Later studies support the idea that a faster pace of counter- the cases, carrying in 23.90% of the cases, and travelling in attacks and the ability to prolong the opponent’s offence are 23.60% of the cases (Fylaktakidou et al, 2011). The negative major factors in increasing the score difference and winning a sign in the ΔTO% variable indicates that the losing teams had game (Courel-Ibáñez et al, 2014). higher numbers of turnovers. In accordance with the stated Shorter offences mean increasing the pace of play, and by the results of the study, game rule violations (leading to turnovers) same logic, increasing the number of steals and turnovers per should be separated from loss of ball in possession, which game. However, two issues arise here. The first is a small provides opportunities for the opponent executing an offence number of studies dealing with the differences in playing styles (bad pass, bad catch, bad dribbling), because as a rule it results between American national and club teams and teams that from good and aggressive defence and enables a transition belong to other FIBA zones. One of the studies explored the offence, which produces a high percentage of field goals. dominance of the US national team at the 2008 Beijing Winning teams had the opportunity to score “easy points”, due Olympics and found that it was mostly a consequence of to bad passes resulting in either a counter-attack by the defensive pressure by exceptionally fit athletes, which yields opponent or scoring from close vicinity to the rim without higher ball possession, i.e. more offences than the opponent, pressure from the defence or with more offence players than and thus more opportunities for scoring (Sampaio et al, 2010). defence players involved. If basketball is a game of mistakes, However, the authors did not precisely identify how and to as described by the great American coach John Wooden what extent does a faster or slower pace affect performance, (Wooden and Walton, 1998), then it should be noted that the but have instead cited the results of previous studies which winning team will be the one with fewer mistakes. Turnovers explained it through a high correlation with steals, blocks and reduce the shot percentage of the team, and increase that of the the opponent's mistakes under defensive pressure, which was opponent, resulting in dual failure (Trninić, 2006). Studies have reduced to empirical, although logical and expert conclusions. also shown that making fewer passes during an offence It should be noted that efficiency percentage of rebounds is increases the likelihood of a positive outcome, i.e. scoring significantly less prominent at 2017 continental championships, (Swalgin, 2014). It is also important to point to the results of a as opposed to numerous previous studies (Csataljay et al, 2009; study by Lyra et al. (2017) which ascertained that the Csataljay et al, 2017; de Carvalho et al, 2017; García et al, significance of turnovers grows as the competition is nearing 2013; Gómez et al, 2008; Gómez et al, 2017; Gómez et al, its end. 2014; Kubatko et al, 2007; Lorenzo et al, 2010; Madarame, In any case, effective field goal percentage and turnovers, 2017; Madarame, 2018; Oliver, 2004; Sampaio et al, 2006; along with offensive rebounding percentage and free throw Sampaio et al, 2010; SimovićandNićin,2011; Simović et al, rate, are singled out as Four Factors which can explain the total 2012; Simović et al, 2019; Sporiš et al, 2006; Suárez-Cadenas offensive and defensive efficiency of a team (Baghal, 2012; and Courel-Ibáñez, 2017; Trninićet al, 2002).

Kubatko et al, 2007), while some subsequent studies Ultimately, it should be noted that in the second model, the corroborated the direct link of these four factors with winning a difference among the continental championships (Table 3) is game (Teramato and Cross, 2010). It was also ascertained that made, apart from ΔST%, by the variable ΔPF%. Although, the significance of each of the four factors to the final score is calculating this variable is debatable, since it has not been not the same, but moves in the range of 10, 6, 3 and 3 (Küpfer, studied in relevant literature so far, but only its absolute 2005), at least as far as the NBA is concerned. This means that indicator was used, which brings into question the accuracy of shot efficiency and turnovers explain around 63% of the final the data. If the results are accurate, then it would be an outcome of the game, which is congruent to the results of this indication of the difference in the level of defence aggression study. among these competitions. But some of the presented results, Unlike the other continental championships, the AmeriCup and especially those relating to shot parameters, lost possession presented steals which, along with field goal efficiency, explain and steals, support the idea that further research should find a 78% of the difference in the final score of a match (R2 = .78, way to objectively represent the variable PF in relative F(2, 19) = 38.03, p ˂ .000). It is worth noting that this variable indicators as well. This is further supported by the fact that this 34612 | P a g e International Journal of Recent Scientific Research Vol. 10, Issue, 09(B), pp. 34607-34617, September, 2019 variable was the only one with a significance of .01, while the same time frames, and very similar formats, but also in the other three (ΔAS, ΔMFG and ΔST%) had a significance of .05. methodology, which had not been previously used to tackle this The limitations of this study pertain to the general limitations issue. The obtained results have shown that: (1) The 2017 of performance research in notational analysis. First of all, the men's FIBA zone championships presented with differences in data was gathered by someone else, with a possibility of errors four variables: field goals attempted, assists, percentage of that would reduce the reliability of the analysis in general or personal foul efficiency, and steals percentage of efficiency; (2) even bring it into question (Škegro, 2013). A review article assists did not discriminate winning and losing teams in any of examining 72 studies which used notational analysis the four zone championships, but only showed a difference ascertained that in 70% of the cases the authors did not conduct among the championships themselves; (3) field goals attempted any analysis of variable reliability (Hughes et al, 2002). was obtained as a variable that discriminates winning and However, in the present study, the data that was used came losing teams in all four competitions in the first iteration, and from continental competitions of national teams, which are as a variable that shows a difference among the championships; inherently top-tier competitions in basketball, both in terms of (4) the final score in all three championships in the first model the participating teams and the players. The data was taken was influenced by variables pertaining to shooting; (5) in the from the official FIBA website, where the data is permanently second model, along with the shot variables, the final score at stored. Research has long shown that a coach is able to see only three competitions was influenced by the variable turnover 30% of match events (Franks and Miller, 1986). More recent percentage of inefficiency, while at the FIBA AmeriCup it was research on the topic gave a slightly better percentage, 59.2% the variable steals percentage of efficiency, which was also the (Lairdand Waters, 2017). This is why notational analysis is variable that showed a difference among the championships; employed, and authors take different approaches to analysing (6) the existence of the percentage of personal foul efficiency the structure of the game of basketball (Selmanović, 2016) and variable, which shows a difference among the observed the relevant literature lists over 200 systems for its objective competitions, is disputable, although the results of this study assessment (Martinez, 2012). The study of competition point to a need to devise an appropriate calculation formula. performance is increasingly reminiscent to the quest for the “holy grail” (Ibid), the reason being primarily in the non- References linearity of relation between efficiency and multidimensionality Aizemberg, L., Roboredo, M.C., Ramos, T.G., Soares de and unpredictability of player behaviour in specific, ever- Mello, J.C., Meza, L.A., and Alves, A.M. (2014). changing game conditions (Grehaigneand Godbout, 1995). Measuring the NBA teams’ cross-efficiency by DEA Consequently, authors are constantly trying to come up with game. American Journal of Operations Research, 4, 101– new criteria systems (Trninićet al, 2000), and this study is an 112. doi: 10.4236/ajor.2014.43010. effort in that direction. The game of basketball is closely Alvarez, A., Ortega, E., Gomez, M.A., and Salado, J. (2009), related to numbers. All numerically recorded movement on the Study of the defensive performance indicators in peak court is a set of statistical data. No measurement has meaning performance basketball. Revista de Psicologia del Deporte, without criteria, and in basketball, sheer superiority in numbers 18, (Supplement), 379–384. is not always sufficient for a victory (Yalçinet al, 2016). Or, in Baghal, T. (2012). Are the “Four Factors” indicators of one the words the great coach Pat Riley (1993), not all skills can be factor? An application of structural equation modeling measured mechanically, but it is certain that they were all methodology to NBA data in prediction of winning measurable in one way or another and that the events observed percentage. International Journal of Performance Analysis and noted during matches can be expressed in numbers. in Sport,.doi: 10.1515/1559-0410.1355.

Along with the methodological contribution to this line of Bazanov, B. (2007). Integrative approach of the technical and research, this study attempted to go a step further by tactical aspects in basketball coaching. Abstract of overcoming the problem that most studies have, having to do Dissertations on Social Sciences. Tallinn University. with game variables that discriminate between winning and Celeux, G., Robert, V. (2015). Towards an objective team losing teams which relate to a season in a national efficiency rate in basketball. Journal de la SociétéFrançaise championship, or two seasons, or a specific international de Statistique, 156(2), 51–68. championship (Ibáñez et al, 2008) by exploring several Çene, E. (2018). What is the difference between a winning and different championships. a losing team: Insights from Euroleaguebasketball. International Journal of Performance Analysis in Sport, Another limitation is that this study took on a purely 18(1), 55–68. doi: 10.1080/24748668.2018.1446234. quantitative approach, which yields valid and objective Choi, H., O’Donoghue, P.G., and Hughes, M.D. (2006), A information, but needs to supplemented by another type of Study of team performance indicators by separated time qualitative analysis to get information on the how and the why. scale using a real-time analysis technique within English

CONCLUSION national basketball league. In H. Dancs, M.D. Hughes & P. O’Donoghue (Eds.), World Congress of Performance Although the issue of evaluating team and individual Analysis of Sport VII – Book of Proceedings (pp.125– performance is riddled with limitations, we are witnesses to 128). Cardiff: UWIC. constant attempts to devise a new criteria system. This study Ciampolini, V., do Nascimento, J.V., Ibáñez, S.J., de Oliveira represents one of those attempts, and the authors hope to have Nicolazzi, L., Camaroto, M., Schmitt Rocha, J.C., at least made a small step towards finding a solution. This is andSaad, M.A. (2018). Offensive tecnical-tactical analysis visible in the fact that the study covered top-tier competitions on NBA finalist teams: A case study. Journal of Physical with reliably gathered variables, but also with the same rules, Education, 29, e2931. doi: 10.4025/jphyseduc.v29i1.2931. 34613 | P a g e Slobodan Simović et al., Quantitative analysis of 2017 Fiba Zone Championships Based on A Discriminant Regression Model

Courel-Ibáñez, J., McRobert, A., Ortega, E., and Cardenas, D. Garcia, J., Ibáñez, S.J., De Santos, R.M., Leite, N., (2014). The impact of match status on game rhythm in andSampaio, J. (2013). Identifying basketballperformance NBA basketball. In De Haan, A., De Ruiter, C.J., indicators in regular season and playoff games. Journal &Tsolakidis, E. (Eds.), 19th annual Congress of the Human Kinetic, 36(1), 161–168. doi: 10.2478/hukin-2013- European college of sport science, Book of abstracts. 0016. Amsterdam, The Netherlands: European College of Sport García, J.; Cañadas, M., andAntúnez, A. (2015Effects of Science. Retrived from attendance, crowd density and capacity of arena in home www.researchgate.net/publication/263661548. advantage according to NBA conference. Cuadernos de Crum, P. (2013). Space creation dynamic in basketball: Psicología del Deporte, 15(3), 175-180. doi: Comparasion between British and Spanish league. 10.4321/S1578-84232015000300018. Unpublished master thesis, University of Chester. Gómez, M.A., Avugos, S., ÁngelOñoro, M., Lorenzo Calvo, Csataljay, G., Hughes, M., James N., andDancs, H. (2010). A., and Bar-Eli, M. (2018). Shaq is not alone: Free-throws Pace as an influencing factor in basketball. In M.Hughes, in the final moments of a basketball game. Journal Human H. Dancs, K. Nagyváradi, T. Polgár, N. James, G. Sporis, Kinetic, 62(1), 135–144. doi: 10.1515/hukin-2017-0165. G. Vuckovic, & M. Jovanovic (Eds), Coference Research Gomez, M.A. Calvo, L.A., and Toro, O.E. (2006a). Differences Methods and Performance Analysis (pp. 178–187). between winning and losing under-16 male basketball Sopron, Hungary: University of West Hungary. teams. In H. Dancs, M.D. Hughes & P. O’Donoghue Csataljay, G., James, N., Hughes, M., andDancs, H. (2009). (Eds.), World Congress of Performance Analysis of Sport Performance indicators that distinguish winning and losing VII – Book of Proceedings (pp.142–149). Cardiff: UWIC. teams in basketball. International Journal of Performance Gomez, M. A., Calvo, L. A., and Toro, O. E. (2006b), Analysis in Sport, 9(1), 60–66. doi: Performance differences between winning and losing 10.1080/24748668.2009.11868464. teams in elite Spanish male and female basketball. In H. Csataljay, G., James, N., Hughes, M., andDancs, H. (2012). Dancs, M.D. Hughes & P. O’Donoghue (Eds.), World Performance differences between winning and losing Congress of Performance Analysis of Sport VII – Book of basketball teams during close, balanced and unbalanced Proceedings (pp.180–184). Cardiff: UWIC. quarters. Journal of Human Sport & Exercise, 7(2), 356– Gómez MA, Ibáñez SJ, Parejo I, andFurley P. (2017). The use 364. doi: 10.4100/jhse.2012.72.02. of classification and regression tree when classifying Csataljay, G., James, N., Hughes, M., andDancs, H. (2013). winning and losing basketball teams. Kinesiology, 49(1), Effects of defensive pressure on basketball shooting 47–56. performance. International Journal of Performance Gomez, M.A., Lorenzo, A., Barakat, R., Ortega, E., andPalao, Analysis in Sport, 13(3), 594–601. doi: J.M. (2008). Differences in game-related statistics of 10.1080/24748668.2013.11868673. basketball performance by game location for men’s Csataljay, G., James, N., Hughes, M., andDancs, H. (2017). winning and losing teams. Perceptual and Motor Skills, Analysis of influencing factors behind offensive 106(1), 43–50. doi: 10.2466/pms.106.1.43-50. rebounding performance in elite basketball. International Gómez, M., Lorenzo, A., andSampaio, J. (2009). Performance Journal of Sports Science & Coaching, 12(6), 774–781. analysis in basketball: It is possible to predict the results. doi: 10.1177/1747954117738900. Sevilla, Spain: Wanceulen. Csataljay, G., O’Donoghue, P., Hughes, M., andDancs, H. Gómez, M.A., Lorenzo, A., Ibañez, S.J., andSampaio, J. (2009). Performance indicators that distinguish winning (2013). Ball possession effectiveness in men's and and losing teams in basketball. International Journal of women's elite basketball according to situational variables Performance Analysis in Sport, 9(1), 60–66. doi: in different game periods. Journal of Sports Sciences, 10.1080/24748668.2009.11868464. 31(14), 1578–1587. doi: 10.1080/02640414.2013.792942. de Carvalho, E.M.O., Leicht, A.S., Nakamura, F.Y., Okuno, Gómez, M. Á., Pérez, J., Molik, B., Syzman, R. J., N.M., and Okazaki, V.H.A. (2017). Team statistical andSampaio, J. (2014). Performance analysis of elite determinants of victory in Brazilian basketball. MOJ men’s and women’s wheelchair basketball teams. Journal Sports Medicine, 1(4), 1–7. doi: of Sports Sciences, 32(11), 1066–1075. DOI: 10.15406/mojsm.2017.01.00018. 10.1080/02640414.2013.879334. Fay, P.J., and Messersmith, L,L. (1938). The effect of rule Grehaigne J. F., and Godbout P. (1995). Tactical knowledge in changes upon the distance traversed by basketball players. team sports – from a constructivist and cognitivist Research Quarterly, 9(2),136–137. perspective. Quest, 47(4), 490–550. doi: FIBA Internal Regulation: Book 3 Players and Officials. 10.1080/00336297.1995.10484171. (2017). Mies, Switzerland: International Basketball Hoover, P. (2012). Pro shooting secrets. 2nd ed. Columbus, Federation. OH: Author. FIBA Statisticals' Manual 2016. (2016). Mies, Switzerland: Hughes, M.D., andBartlett, R.M. (2002). The use of International Basketball Federation. performance indicators in performance analysis. J Sports Franks, I., and Miller, G.. (1986). Eyewitness testimony in Sci, 20(10), 739–754. doi: 10.1080/026404102320675602. sport. Journal of sport behaviour, 9, 39– 45. doi: Hughes, M. D., Cooper, S-M., andNevill, A. (2002). Analysis 10.1080/24748668.2008.11868424. procedures for non-parametric data from performance Fylaktakidou, A., Tsamourtzis, E., andZaggelidis, G. (2011). analysis. International Journal of Performance Analysis in The turnovers analysis to the women's national league Sport, 2(1), 6–20. DOI: basketball games. Sport Science Review, 20(3), 69−83. 10.1080/24748668.2002.11868257.

34614 | P a g e International Journal of Recent Scientific Research Vol. 10, Issue, 09(B), pp. 34607-34617, September, 2019

Hughes, M.D., and Franks, I.M. (2004). Notational Analysis of master thesis. University of Zagreb, Faculty of Physical Sport: Better systems for improving coaching and Culture. performance. 2nd Ed. London: E. & F.N. Spon. Madarame, H. (2017). Game-related statistics which Ibáñez, S. J., Feu, S., and Dorado, G. (2003). Analysis of discriminate between winning and losing teams in Asian differences in the game according to gender category. In and European Men’s Basketball Championships. Asian Sergio José Ibáñez Godoy (Ed.), II CongresoIbérico de Journal of Sport Medicine, 8(2), e42727. doi: Baloncesto: la formación y el rendimientoenbaloncesto 10.5812/asjsm.42727. (pp. 73–88). Cáceres: Facultad de Ciencias del Deporte. Madarame, H. (2018a). Defensive rebounds discriminate Ibáñez, S. J., Feu, S., Garcia, J., Canadas, M., and Parejo, I. winners from losers in European but not in Asian (2007). Multifactorial study of shot efficiency in the Women’s Basketball Championships. Asian Journal od Spanish professional basketball league. Iberian Congress Sport Medicine, 9(1), e67428. doi: 10.5812/asjsm.67428. on Basketball Research, 4, 54–57. Madarame, H. (2018b). Regional differences in women’s Ibáñez, S.J., Garcia, J., Feu, S., Lorenzo, A., andSampaio, J. basketball: A comparison among continental (2009). Effects of consecutive basketball games on the championships. Sports, 6(3), 65. doi: game-related statistics that discriminate winner and losing 10.3390/sports6030065. teams. Journal of Sport Science and Medicine, 8(3), 458– Madarame, H. (2018c). Basketball game-related statistics that 62. discriminate among continental championships for under- Ibáñez, S.J., García-Rubio, J., Gómez, M.A., and Gonzalez- 18 Women. Sports, 6(4), 114. doi: 10.3390/sports6040114. Espinosa, S. (2018). The impact of rule modifications on Madarame, H. (2018d). Are regional differences in basketball elite basketball teams’ performance. Journal Human already established in under-18 games? Motriz Rev. Educ. Kinetic. [in press]. doi: 10.1515/hukin-2017-0193. Fis., in press. Ibáñez, S.J., González-Espinosa, S., Feu S., andGarcía-Rubio, Malarranha, J., Figueira, B., Leite, N., andSampaio, J. (2013). J. (2018). Basketball without borders? Similarities and Dynamic modeling of performance in basketball. differences among Continental Basketball Championships. International Journal of Performance Analysis in Sport, Revistainternacional de ciencias del deporte, 14(51), 42– 13(2), 377–387. doi: 10.1080/24748668.2013.11868655. 54. doi: 10.5232/ricyde. Martinez, J. A. (2012). Factors determining production (FDP) Ibáñez, S.J., Sampaio, J., Feu, S., Lorenzo, A., Gomez, M.A., in basketball. Economics and Business Letters, 1(1), 21– and Ortega, E. (2008). Basketball game-related statistics 29. that discriminate between teams’ season long success. Marvidis, G., Tsamourtzis, E., Karipidis, A., andLaios, A. European Journal of Sport Science, 8(6), 369–372. doi: (2009). The inside game in World Basketball: Comparison 10.1080/17461390802261470 between European and NBA teams. International Journal Karapidis, A., Fotinakis, A., Taxildaris, K., andFatouros, J. of Performance Analysis in Sport, 6(3), pii: E65. doi: (2001). Factors characterizing a successful performance in 10.3390/sports6030065. basketball. Journal of human movement studies, 14(5), Messina, E. (2011). My favorite drills [online video]. FIBA 385–398. Europe – Coaching Clinics. Retrieved from: Kubatko, J., Oliver, D., Pelton, K., and Rosenbaum, D.T. http://coaching.fibaeurope.com/Default.asp?cid={E8DBE (2007). A starting point for analyzing Basketball statistics. C51-0212-4C9D-B387- Journal of Quantitative Analysis in Sports, 3(3), 1–22. doi: 4CB5D1D3A722}&coid={F3FDEF06-32B7-42F3-AED2- 10.2202/1559-0410.1070. 7EE3A4BF9483}&articleMode=on&. Küpfer, E. (2005). Team similarity. APBRMetrics forum. Mikołajec, K., Maszczyk, A., and Zając, T. (2013). Game Retrived from: Indicators Determining Sports Performance in the NBA. http://www.sonicscentral.com/apbrmetrics/viewtopic.php? Journal of Human Kinetics, 37(1), 145–151. doi: p=118&sid=ff925dbe120f6c6f86e596d0d6dabbb8, 10.2478/hukin-2013-0035. accessed 2/28/2011. Milanović, D., Selmanović, A., andŠkegro D. (2014). Laird, P., and Waters, L. (2017). Eyewitness recollection of Characteristics and differences of basic types of offenses sport coaches. International Journal of Performance in European and American top-level basketball. In: Analysis in Sport, 8(1), 76–84. doi: Milanović, D &Sporiš, G. (Eds.), 7th International 10.1080/24748668.2008.11868424. Scientific Conference on Kinesiology, Proceedings Book Leicht, A.S., Gómez, M.A., and Woods, C.T. (2017). (pp 400–403). Opatija, Croatia: Faculty of Kinesiology, Explaining match outcome during the Men’s basketball University of Zagreb. tournament at the Olympic games. Journal of Sport Nakić J. (2004). Differences in standard and derived situation Science and Medicine, 16(4), 468–473. efficacy parameters between men’s and women’s Lorenzo, A., Gómez, M.A., Ortega, E., Ibáñez, S.J., basketball teams in senior European basketball andSampaio, J. (2010). Game related statistics which championships in 2003. Unpublished master thesis. discriminate between winning and losing under-16 male University of Zagreb, Faculty of Kinesiology. basketball games. Journal of Sport Science and Medicine, Oliver, D. (2001). Statistical effects of proposed NBA rule 9(4), 664–668. changes. Journal of Basketball Studies. Retrieved from Lukšić, E. (2001). Differences between winning and losing top www.rawbw.com/~deano/index.html. European basketball teams and American professional and Oliver D. (2004). Basketball on paper: Rules and tools for university clubs in the finals of the playoffs. Unpublished performance analysis. Washington, DC: Brassey´s, Inc.

34615 | P a g e Slobodan Simović et al., Quantitative analysis of 2017 Fiba Zone Championships Based on A Discriminant Regression Model

Ortega, E., and Fernandez, R. (2007). Differences in 3-point Simović S, andNićin Đ. (2011). Latent structure of efficiency at shots between winning and losing teams in formative years 2009 Continental (Zone) basketball championships. of basketball play. Iberian Congress on Basketball Research in Kinesiology, 39(1), 31–36. Research, 4, 33–37. Simović, S., Komić, J., Matković, B., andNićin, Đ. (2012). Paulauskas, R., Masiulis, N., Vaquera, A., andSampaio, J. Analysis of results at Eurobasket 2011 – final results of (2018). Basketball game-related statistics that discriminate observed elements of basketball game. In S. Simović (Ed.), between European players competing in the NBA and in 3rd International scientific sonference Anthropological the Euroleague. Journal of Human Kinetics [in press]. doi: aspects of sports, physical education and recreation (pp. 10.2478/hukin-2018-0030. 19–28). Banja Luka: Faculty of Physical Education and Reano, G., Calvo, L., and Tore, O. (2006). Differences between Sport, University of Banja Luka. doi: winning and losing under-16 male basketball teams. In H. 10.5550/SP.3.2011.03. Dancs, M.D. Hughes & P. O'Donoghue (Eds.), World Simović, S., Komić, J., Matković, B., Pajić, Z., andGuzina, B. Congress of Performance Analysis of Sport VII – Book of (2019). Analysis of influence basketball game-related Proceedings (pp. 142–147). Cardiff: UWIC. statistics on final result based on differentiate at the Riley, P. (1993). The winner within: A life plan for team 2017 FIBA Asia Cup. Asia Journal of Sport Medicine, players. New York, NY: Putnam's Sons. e69109. doi: 10.5812/asjsm.69109. Sampaio, J., Ibáñez, S.J., and Lorenzo, A. (2013). Basketball. Sindik, J. (2015). Performance indicators of the top basketball In: McGarry. T., O’Donoghue, P., and Sampaio, J. (Eds), players: Relations with several variables. CollAntropol, Routledge Handbook of Sports Performance Analysis (pp. 39(3), 617–624. 357-366). Oxfordshire and New York: Routledge. Smith, D. (1999). Basketball, multiple offense and defense. 4th Sampaio, J., andJaneira, M. (2003). Statistical analyses of ed. Boston, MA: Allyn and Bacon. basketball team performance understanding teams’ wins Sporiš, G., Barišić, V., Fiorentini, F., Ujević, B., Jovanović, and losses according to a different index of ball M., andTalović, M. (2014). Situational efficiency in possessions. International Journal of Performance Analysis football. Glina, Croatia: Lena sport d.o.o. in Sport, 3(1), 40–49. doi: Sporiš, G., Šango, J., Vučetić, V., andMašina, T. (2006). The 10.1080/24748668.2003.11868273 latent structure of standard game efficiency indicators in Sampaio, J., Janeira, M., Ibanez, S., and Lorenzo, A. (2006). basketball. International Journal of Performance Analysis Discriminant analysis of game-related statistics between in Sport, 6(1), 120–129. doi: basketball guards, forwards and centers in three 10.1080/24748668.2006.11868360. professional leagues. European Journal of Sport Science, Stavropoulos, N., andFoundalis, H. (2005) The influence of 6(3), 173–178. doi: 10.1080/17461390600676200. passing and dribbling during out of bounds in offensive Sampaio, J., Lago, C., and Drinkwater, E.J. (2010). success in the game of basketball. Inquiries in Sport & Explanations for the United States of America’s Physical Education, 3(3), 298–303. dominance in basketball at the Beijing Olympic Games Suárez-Cadenas, E., andCourel-Ibáñez, J. (2017). Shooting (2008). Journal of Sports Sciences, 28(2), 147–152. doi: strategies and efectiveness after ofensive rebound and its 10.1080/02640410903380486. impact on game result in Euroleague basketball teams. Sampaio, J., McGarry, T., Calleja-González, J., Jiménez, S., Cuadernos de Psicologia del Deporte, 17(3), 217–222. Schelling, X., andBalciunas, M. (2015). Exploring game Swalgin, K. (2014). The relationship between the number of performance in the National Basketball Association using passes in a possession and the probability of scoring in player tracking data. PloS-One, 10(7), e0132894. men's division I. college basketball in the United States. In doi:10.1371/journal.pone.0132894. D. Milanovic& G. Sporiš (Eds.), 7th International Selmanović, A. (2016). Comparison of the offense structure in scientific conference on kinesiology (pp. 409–416). European and American basketball. Unpublished dictoral Opatija, Croatia: University of Zagreb, Faculty of dissertation, University of Zagreb, Faculty of Kinesiology. Kinesiology. Selmanović, A., Milanović, L., andBrekalo, M. (2017). Škegro, D. (2013). Evaluating different types of attack in a Analysis of ball conversion in European and American basketball game based on their start, outcome, duration professional basketball games. In Milanović, D., Sporiš, and number of additions. Unpublished doctoral G., Šalaj, S., &Škegro, D. (Eds), 8th International Dissertation, University of Zagreb, Faculty of Kinesiology. Scientific Conference on Kinesiology, Proceednigs Book Tavares, F., and Gomes, N. (2003). The offensive process in (pp. 406–410). Opatia, Croatia: Facuty of Kinesiology, basketball - a study in high performance junior teams. University of Zagreb. International Journal of Performance Analysis in Sport, Simović, S., andKomić, J. (2008). Analysis of influence of 3(1), 34–39. doi: 10.1080/24748668.2003.11868272. certain elements of basketball game on final result based Teramoto, M., and Cross, C.L. (2010). Relative Importance of on differentiation at the XIII, XIV and XV World Performance Factors in Winning NBA Games in Regular Championship. ActaKinesiologica, 2(2), 57–65. Season versus Playoffs. Journal of Quantitative Analysis in Simović, S., Matković, B., Mijanović, M., Kocić, M., Sports, 6(3), 1–19. doi: 10.2202/1559-0410.1260. andVojvodić, M. (2012). Structure of efficiency factor at Trninić, S. (1996). Analysis and learning of basketball game. XIII, XIV, XV, and XVI World Championship in Pula, Croatia: Vikta. basketball. Journal of Human Sport and Exersises, 7(2), Trninić, S. (2006). Selection, preparation and leadership of 527–543. doi: 10.4100/jhse.2012.72.16 basketball players and teams. Zagreb, Croatia: Vikta- Marko d.o.o.

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Trninić, S., Dizdar, D., andDežman, B. (2000). Empirical 2017 FIBA Afrobasket. (2017). Mies: International Basketball verification of the weighted system of criteria for the elite Federation [cited 2018 Sep 25]. Available from: basketball players quality evaluation. Collegium https://www.archive.fiba.com/pages/eng/fa/event/p/sid/132 anthropologicum, 24(2), 431–442. 46/_/2017_FIBA_Afrobasket/index.html Trninić, S., Dizdar, D., andLukšić, E. (2002). Differences 2017 FIBA AmeriCup. (2017). Mies: International Basketball between winning and defeated top quality basketball teams Federation [cited 2018 Sep 25]. Available from: in final tournaments of European club championship. Coll. https://www.archive.fiba.com/pages/eng/fa/event/p/sid/811 Antropol, 26(2), 521–531. 5/_/2017_FIBA_AmeriCup/index.html Vilar, L., Araújo, D., Davids, K., and Button, C. (2012). The 2017 FIBA Asia Cup. (2017). Mies: International Basketball role of ecological dynamics in analysing performance in Federation [cited 2018 Sep 25]. Available from: team sports. Sports Medicine, 42(1), 1–10. doi: http://www.archive.fiba.com/pages/eng/fa/event/p/sid/811 10.2165/11596520-000000000-00000. 0/_/2017_FIBA_Asia_Cup/index.html. Witkos, R. (2010). Determining the success of NCAA 2017 FIBA Eurobasket. (2017). Mies: International Basketball basketball teams through team characteristics. Unpubished Federation [cited 2018 Sep 25]. Available from: doctoral dissertation, Bryant University. https://www.archive.fiba.com/pages/eng/fa/event/p/sid/121 Wooden, J., and Walton, B. (1998). Practical modern 05/_/2017_FIBA_Eurobasket basketball. 3rd Ed. San Francisco, CA: Benjamin /index.html Cummings. Yalçin, Y.G., Altin, M., andDemir, H. (2016). Comparison of basketball performance and efficiency scores between Turkish Basketball League players who are Turkish, American and other nations origin. European Journal of Physical Education and Sport Science, 2(4), 153–163. doi: 10.5281/zenodo.164890.

How to cite this article:

Slobodan Simović et al.2019, Quantitative analysis of 2017 Fiba Zone Championships Based on A Discriminant Regression Model. Int J Recent Sci Res. 10(09), pp.34607-34617. DOI: http://dx.doi.org/10.24327/ijrsr.2019.1009.3933

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