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Debate over the impact of team composition on the outcome of a contest has attracted sports enthusiasts and sports scientists for years. A commonly held belief regarding team success is the superstar effect; that is, including more talent improves the performance of a team 1 . However, studies of team sports have suggested that previous relations and shared experiences among team members improve the mutual understanding of individual habits, techniques and abilities and therefore enhance team coordination and strategy 2-9 . We explored the impact of within-team relationships on the outcome of competition between sports teams. Relations among teammates consist of two aspects: qualitative and quantitative. While quantitative aspects measure the number of times two teammates collaborated, qualitative aspects focus on 'prior shared success'; that is, whether teamwork succeeded or failed. We examined the association between qualitative team interactions and the probability of winning using historical records from professional sports-basketball in the National Basketball Association, football in the English Premier League, cricket in the Indian Premier League and baseball in Major League Baseball-and the multiplayer online battle game Defense of the Ancients 2. Our results show that prior shared success between team members significantly improves the odds of the team winning in all sports beyond the talents of individuals.

Explicitly monitoring one's own actions has been noted as detrimental to the performance of fine motor skills under duress. Offensive skills rather than defensive skills are typically studied in this context. Defensive techniques typically require skills such as footwork and continuous movement, as opposed to more precise, hand-eye coordinated action. Explicit monitoring theory may be less relevant for defensive skills than offensive skills when playing under pressure. Archival data (66 years) for teams and for individual players was compiled from the National Basketball Association (NBA) and the National Football League (NFL). For basketball (n=778) and football (n=515) teams, regular season offensive and defensive statistics similarly predicted success in the postseason, which was assumed to create more pressure. For individual basketball players (n=5,132), nine indices of offensive (FG, free throw and three-point shooting, offensive win shares, points, and assists) and defensive (defensive win shares, steals, and blocks) production were compared; among these, three-point shooting percentage was least correlated from season to postseason, suggesting it is especially variable under pressure. A balanced basketball or football team that focuses on both offense and defense may be most successful.

The aim of the present study was to identify game-related statistics that differentiate winning and losing teams according to game location. The sample included 306 games of the 2004-2005 regular season of the Spanish professional men's league (ACB League). The independent variables were game location (home or away) and game result (win or loss). The game-related statistics registered were free throws (successful and unsuccessful), 2- and 3-point field goals (successful and unsuccessful), offensive and defensive rebounds, blocks, assists, fouls, steals, and turnovers. Descriptive and inferential analyses were done (one-way analysis of variance and discriminate analysis). The multivariate analysis showed that winning teams differ from losing teams in defensive rebounds (SC = .42) and in assists (SC = .38). Similarly, winning teams differ from losing teams when they play at home in defensive rebounds (SC = .40) and in assists (SC = .41). On the other hand, winning teams differ from losing teams when they play away in defensive rebounds (SC = .44), assists (SC = .30), successful 2-point field goals (SC = .31), and unsuccessful 3-point field goals (SC = -.35). Defensive rebounds and assists were the only game-related statistics common to all three analyses.

The purpose of this study was to analyze the ball possessions of winning and losing basketball teams in formative years (16 years and under). The sample was 3,897 ball possessions from 24 games of the boys' 16-and-under finals of the Andalusion Championship, Spain. The variables studied were game outcome, participation, and the initiation and end of each ball possession. Winning teams used more fast breaks and fewer set offenses in their ball possessions. Winning teams had shorter ball possessions and more passes and participating players. Dynamic game styles are necessary in youth basketball, focusing on continuously off-balancing the opponent through fast breaks and a high number of passes in set offenses.

The aim of the present paper was to examine the differences in game-related statistics between basketball players who are selected for the starting five of the team (starters) and those who are not (nonstarters) when related to game outcome (winning and losing) and team quality (best teams, teams classified for the playoffs; and worst teams, teams who miss playoff classification). Archival data were gathered for all 2002-2003 regular season games from the Portuguese Professional League (N = 156). Discriminant analysis was used to identify the game-related statistics that differentiate between starters and nonstarters and interpreted by the examination of the structure coefficients (SC). When the best teams won the games, results described differences between starters and nonstarters with an emphasis on defensive rebounds (SC = .32), assists (SC = .32) and committed fouls (SC = -.68). When the worst teams won the games, results described differences between starters and nonstarters with an emphasis on 2-point field goals successful (SC = .47) and unsuccessful (SC = .48), defensive rebounds (SC =.39), successful free throws (SC =.32), and committed fouls (SC = -.55). An also important finding was that, in best teams, the nonstarters' performance was worse in the games that the team lost, whereas in worst teams, it was the starters' performance that was worse in the games that the team lost.

In this study, we assessed whether contextual factors related to where or when an athlete is born influence their likelihood of playing professional sport. The birthplace and birth month of all American players in the National Hockey League, National Basketball Association, Major League Baseball, and Professional Golfer's Association, and all Canadian players in the National Hockey League were collected from official websites. Monte Carlo simulations were used to verify if the birthplace of these professional athletes deviated in any systematic way from the official census population distribution, and chi-square analyses were conducted to determine whether the players' birth months were evenly distributed throughout the year. Results showed a birthplace bias towards smaller cities, with professional athletes being over-represented in cities of less than 500,000 and under-represented in cities of 500,000 and over. A birth month/relative age effect (in the form of a distinct bias towards elite athletes being relatively older than their peers) was found for hockey and baseball but not for basketball and golf. Comparative analyses suggested that contextual factors associated with place of birth contribute more influentially to the achievement of an elite level of sport performance than does relative age and that these factors are essentially independent in their influences on expertise development.