Specific features of 3×3 basketball: factor analysis of the key performance indicators and their impact on game performance in the elite leagues
Background: Brought from the streets to the Olympics, the 3×3 basketball has gained relevance in worldwide sport over the last few years. Yet, available literature about its specific features is still scarce. We identified the specific features of elite 3×3 basketball and investigated the factors th...
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description | Background: Brought from the streets to the Olympics, the 3×3 basketball has gained relevance in worldwide sport over the last few years. Yet, available literature about its specific features is still scarce. We identified the specific features of elite 3×3 basketball and investigated the factors that determine the success of 3×3 basketball teams in official competitions. Method: This study analyzed 11 Masters stages and the final of the FIBA 3x3 World Tour (November 2-3, 2019, Utsunomiya, Japan), in which 56 teams participated. To assess the factors with the greatest impact on the percentage of wins of teams in the tournament, a regression analysis was performed. The percentage of wins (W%) in the total number of games played was taken as a performance indicator. To build a regression model, various game indicators were chosen, which are factorial manifestations. The obtained results revealed that W% was most influenced by the average turnovers and average rebounds per game. It was determined that the difference between the number of shots made per game under the basket and beyond the arc was insignificant (only 1-2 shots for some teams). In addition, a shooting map showed that some teams were more successful at shooting from outside the arc than from the middle range or behind the basket. To assess shooting activity, all final games were analyzed by video review. Results: The turnovers per game (TOPG) has the greatest influence on the share of wins, i.e., 56.4%. Nevertheless, the rebounds per game (REBPG) factor also has a significant influence, i.e., 23.7%. If we increase TOPG by 1%, W% decreases by 0.3%. Moreover, if REBPG increases by 1%, W% increases by 0.12%. The TOP-10 3×3 teams (according to FIBA 3×3 World Tour 2019) perform an average of 15.5 ± 1.7 attacks in the paint and from an average distance (one-point shots), 12.3 ± 1.7 2-point shooting attacks and 3.84 ± 0.6 free throws per game. On average, 8.9 ± 0.9 attacks from the paint zone and from an average distance were successful per game in the league (one-point goals). Conclusions: Our findings highlight the importance of long-range shots to win games in 3×3 basketball and improve our understanding on how teams offensively prepare themselves to beat their opponents. Coaches are recommended to pay more attention to throwing exercises beyond the arc with the resistance of a defender, from uncomfortable situations. A high proportion of training exercises should be aimed at working on blocking the |
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Yet, available literature about its specific features is still scarce. We identified the specific features of elite 3×3 basketball and investigated the factors that determine the success of 3×3 basketball teams in official competitions. Method: This study analyzed 11 Masters stages and the final of the FIBA 3x3 World Tour (November 2-3, 2019, Utsunomiya, Japan), in which 56 teams participated. To assess the factors with the greatest impact on the percentage of wins of teams in the tournament, a regression analysis was performed. The percentage of wins (W%) in the total number of games played was taken as a performance indicator. To build a regression model, various game indicators were chosen, which are factorial manifestations. The obtained results revealed that W% was most influenced by the average turnovers and average rebounds per game. It was determined that the difference between the number of shots made per game under the basket and beyond the arc was insignificant (only 1-2 shots for some teams). In addition, a shooting map showed that some teams were more successful at shooting from outside the arc than from the middle range or behind the basket. To assess shooting activity, all final games were analyzed by video review. Results: The turnovers per game (TOPG) has the greatest influence on the share of wins, i.e., 56.4%. Nevertheless, the rebounds per game (REBPG) factor also has a significant influence, i.e., 23.7%. If we increase TOPG by 1%, W% decreases by 0.3%. Moreover, if REBPG increases by 1%, W% increases by 0.12%. The TOP-10 3×3 teams (according to FIBA 3×3 World Tour 2019) perform an average of 15.5 ± 1.7 attacks in the paint and from an average distance (one-point shots), 12.3 ± 1.7 2-point shooting attacks and 3.84 ± 0.6 free throws per game. On average, 8.9 ± 0.9 attacks from the paint zone and from an average distance were successful per game in the league (one-point goals). Conclusions: Our findings highlight the importance of long-range shots to win games in 3×3 basketball and improve our understanding on how teams offensively prepare themselves to beat their opponents. Coaches are recommended to pay more attention to throwing exercises beyond the arc with the resistance of a defender, from uncomfortable situations. A high proportion of training exercises should be aimed at working on blocking the opponent after the attack and fighting for the rebound of the ball. The pace and complexity of the throws should be as close as possible to the game situation. To achieve high results, it is also necessary to minimize the average turnovers per game.</description><identifier>ISSN: 2247-8051</identifier><identifier>EISSN: 2247-806X</identifier><identifier>DOI: 10.7752/jpes.2022.10326</identifier><language>eng</language><publisher>Pitesti: Universitatea din Pitesti</publisher><subject>Basketball ; Business metrics ; Factor Analysis ; Individual Characteristics ; Males ; Olympic games ; Physical Characteristics ; Physiology ; Regression (Statistics) ; Regression analysis ; Statistical Analysis ; Statistical Data ; Team Sports ; Tournaments & championships</subject><ispartof>Journal of Physical Education and Sport, 2022-10, Vol.22 (10), p.2575-2581</ispartof><rights>2022. This work is published under https://creativecommons.org/licenses/by-nc-nd/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Andrianova, Raisa I</creatorcontrib><creatorcontrib>Guimarães, Eduardo</creatorcontrib><creatorcontrib>Fedoseev, Dmitrii V</creatorcontrib><creatorcontrib>Isakov, Milan</creatorcontrib><title>Specific features of 3×3 basketball: factor analysis of the key performance indicators and their impact on game performance in the elite leagues</title><title>Journal of Physical Education and Sport</title><description>Background: Brought from the streets to the Olympics, the 3×3 basketball has gained relevance in worldwide sport over the last few years. Yet, available literature about its specific features is still scarce. We identified the specific features of elite 3×3 basketball and investigated the factors that determine the success of 3×3 basketball teams in official competitions. Method: This study analyzed 11 Masters stages and the final of the FIBA 3x3 World Tour (November 2-3, 2019, Utsunomiya, Japan), in which 56 teams participated. To assess the factors with the greatest impact on the percentage of wins of teams in the tournament, a regression analysis was performed. The percentage of wins (W%) in the total number of games played was taken as a performance indicator. To build a regression model, various game indicators were chosen, which are factorial manifestations. The obtained results revealed that W% was most influenced by the average turnovers and average rebounds per game. It was determined that the difference between the number of shots made per game under the basket and beyond the arc was insignificant (only 1-2 shots for some teams). In addition, a shooting map showed that some teams were more successful at shooting from outside the arc than from the middle range or behind the basket. To assess shooting activity, all final games were analyzed by video review. Results: The turnovers per game (TOPG) has the greatest influence on the share of wins, i.e., 56.4%. Nevertheless, the rebounds per game (REBPG) factor also has a significant influence, i.e., 23.7%. If we increase TOPG by 1%, W% decreases by 0.3%. Moreover, if REBPG increases by 1%, W% increases by 0.12%. The TOP-10 3×3 teams (according to FIBA 3×3 World Tour 2019) perform an average of 15.5 ± 1.7 attacks in the paint and from an average distance (one-point shots), 12.3 ± 1.7 2-point shooting attacks and 3.84 ± 0.6 free throws per game. On average, 8.9 ± 0.9 attacks from the paint zone and from an average distance were successful per game in the league (one-point goals). Conclusions: Our findings highlight the importance of long-range shots to win games in 3×3 basketball and improve our understanding on how teams offensively prepare themselves to beat their opponents. Coaches are recommended to pay more attention to throwing exercises beyond the arc with the resistance of a defender, from uncomfortable situations. A high proportion of training exercises should be aimed at working on blocking the opponent after the attack and fighting for the rebound of the ball. The pace and complexity of the throws should be as close as possible to the game situation. To achieve high results, it is also necessary to minimize the average turnovers per game.</description><subject>Basketball</subject><subject>Business metrics</subject><subject>Factor Analysis</subject><subject>Individual Characteristics</subject><subject>Males</subject><subject>Olympic games</subject><subject>Physical Characteristics</subject><subject>Physiology</subject><subject>Regression (Statistics)</subject><subject>Regression analysis</subject><subject>Statistical Analysis</subject><subject>Statistical Data</subject><subject>Team Sports</subject><subject>Tournaments & championships</subject><issn>2247-8051</issn><issn>2247-806X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpd0L1OwzAQB3ALgURVOrNaYk7xR2wnbKgCilSJgQ5slT_OxW2ahNgd-hhMPBAvhimIgelu-N3_dIfQJSVTpQS73vQQp4wwNqWEM3mCRoyVqqiIfDn96wU9R5MYN4SQrATn9Qi9P_dggw8We9BpP0DEncf884Njo-MWktFNc4O9tqkbsG51c4jhaNIr4C0ccA-D74adbi3g0LpgdZYxU_dNwoDDrs_TuGvxWu_gnz_GQBMS4Ab0eg_xAp153USY_NYxWt7fLWfzYvH08Di7XRR9XaWiNESYkljpqDFU-Lr0hHNPQOrKg6U1KUF4x8FJ5pmjVHkumDWEe6iscHyMrn5i-6F7y2vTatPth3xfXDEl8yulKhX_Amr0ayo</recordid><startdate>20221001</startdate><enddate>20221001</enddate><creator>Andrianova, Raisa I</creator><creator>Guimarães, Eduardo</creator><creator>Fedoseev, Dmitrii V</creator><creator>Isakov, Milan</creator><general>Universitatea din Pitesti</general><scope>0-V</scope><scope>3V.</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>88B</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BYOGL</scope><scope>CCPQU</scope><scope>CJNVE</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>K9-</scope><scope>K9.</scope><scope>M0P</scope><scope>M0R</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEDU</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20221001</creationdate><title>Specific features of 3×3 basketball: factor analysis of the key performance indicators and their impact on game performance in the elite leagues</title><author>Andrianova, Raisa I ; Guimarães, Eduardo ; Fedoseev, Dmitrii V ; Isakov, Milan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p98t-4b05b40c6d1bb15f94f033f0e6a8fec1904e5fd3ed62f2d117f352cb03fe8c5d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Basketball</topic><topic>Business metrics</topic><topic>Factor Analysis</topic><topic>Individual Characteristics</topic><topic>Males</topic><topic>Olympic games</topic><topic>Physical Characteristics</topic><topic>Physiology</topic><topic>Regression (Statistics)</topic><topic>Regression analysis</topic><topic>Statistical Analysis</topic><topic>Statistical Data</topic><topic>Team Sports</topic><topic>Tournaments & championships</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Andrianova, Raisa I</creatorcontrib><creatorcontrib>Guimarães, Eduardo</creatorcontrib><creatorcontrib>Fedoseev, Dmitrii V</creatorcontrib><creatorcontrib>Isakov, Milan</creatorcontrib><collection>ProQuest Social Sciences Premium Collection</collection><collection>ProQuest Central (Corporate)</collection><collection>Physical Education Index</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Education Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Social Science Premium Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>East Europe, Central Europe Database</collection><collection>ProQuest One Community College</collection><collection>Education Collection</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Education Database</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Education</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of Physical Education and Sport</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Andrianova, Raisa I</au><au>Guimarães, Eduardo</au><au>Fedoseev, Dmitrii V</au><au>Isakov, Milan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Specific features of 3×3 basketball: factor analysis of the key performance indicators and their impact on game performance in the elite leagues</atitle><jtitle>Journal of Physical Education and Sport</jtitle><date>2022-10-01</date><risdate>2022</risdate><volume>22</volume><issue>10</issue><spage>2575</spage><epage>2581</epage><pages>2575-2581</pages><issn>2247-8051</issn><eissn>2247-806X</eissn><abstract>Background: Brought from the streets to the Olympics, the 3×3 basketball has gained relevance in worldwide sport over the last few years. Yet, available literature about its specific features is still scarce. We identified the specific features of elite 3×3 basketball and investigated the factors that determine the success of 3×3 basketball teams in official competitions. Method: This study analyzed 11 Masters stages and the final of the FIBA 3x3 World Tour (November 2-3, 2019, Utsunomiya, Japan), in which 56 teams participated. To assess the factors with the greatest impact on the percentage of wins of teams in the tournament, a regression analysis was performed. The percentage of wins (W%) in the total number of games played was taken as a performance indicator. To build a regression model, various game indicators were chosen, which are factorial manifestations. The obtained results revealed that W% was most influenced by the average turnovers and average rebounds per game. It was determined that the difference between the number of shots made per game under the basket and beyond the arc was insignificant (only 1-2 shots for some teams). In addition, a shooting map showed that some teams were more successful at shooting from outside the arc than from the middle range or behind the basket. To assess shooting activity, all final games were analyzed by video review. Results: The turnovers per game (TOPG) has the greatest influence on the share of wins, i.e., 56.4%. Nevertheless, the rebounds per game (REBPG) factor also has a significant influence, i.e., 23.7%. If we increase TOPG by 1%, W% decreases by 0.3%. Moreover, if REBPG increases by 1%, W% increases by 0.12%. The TOP-10 3×3 teams (according to FIBA 3×3 World Tour 2019) perform an average of 15.5 ± 1.7 attacks in the paint and from an average distance (one-point shots), 12.3 ± 1.7 2-point shooting attacks and 3.84 ± 0.6 free throws per game. On average, 8.9 ± 0.9 attacks from the paint zone and from an average distance were successful per game in the league (one-point goals). Conclusions: Our findings highlight the importance of long-range shots to win games in 3×3 basketball and improve our understanding on how teams offensively prepare themselves to beat their opponents. Coaches are recommended to pay more attention to throwing exercises beyond the arc with the resistance of a defender, from uncomfortable situations. A high proportion of training exercises should be aimed at working on blocking the opponent after the attack and fighting for the rebound of the ball. The pace and complexity of the throws should be as close as possible to the game situation. To achieve high results, it is also necessary to minimize the average turnovers per game.</abstract><cop>Pitesti</cop><pub>Universitatea din Pitesti</pub><doi>10.7752/jpes.2022.10326</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Basketball Business metrics Factor Analysis Individual Characteristics Males Olympic games Physical Characteristics Physiology Regression (Statistics) Regression analysis Statistical Analysis Statistical Data Team Sports Tournaments & championships |
title | Specific features of 3×3 basketball: factor analysis of the key performance indicators and their impact on game performance in the elite leagues |
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