The objective of the present study was to analyze the differences in rugby game statistics between winning and losing teams. The data from 58 games of round robin play from the Six Nations tournament from the 2003-2006 seasons were analyzed. The groups of variables studied were: number of points scored, way in which the points were scored; way teams obtained the ball and how the team used it; and technical and tactical aspects of the game. A univariate (t-test) and multivariate (discriminant) analysis of data was done. Winning teams had average values that were significantly higher in points scored, conversions, successful drops, mauls won, line breaks, possessions kicked, tackles completed, and turnovers won. Losing teams had significantly higher averages for the variables scrums lost and line-outs lost. The results showed that: a) in the phases of obtaining the ball and more specifically in scrummage and line-out, winning teams lose fewer balls than losing teams (winning teams have an efficacy of 90% in both actions); b) the winning team tends to play more with their feet when they obtain the ball, to utilize the maul as a way of attacking, and to break the defensive line more often than the losing team does; and c) On defence, winning teams recovered more balls and completed more tackles than losing teams, and the percentage of tackles completed by winning teams was 94%. The value presented could be used as a reference for practice and competition in peak performance teams. Key pointsThis paper increases the knowledge about rugby match analysis.Give normative values to establish practice and match goals.Give applications ideas to connect research with coaches practice.

https://www.jssm.org/hfpdf.php?volume=08&issue=4&page=523

Differences in game statistics between winning and losing rugby teams in the six nations tournament.

Recent player tracking technology provides new information about basketball game performance. The aim of this study was to (i) compare the game performances of all-star and non all-star basketball players from the National Basketball Association (NBA), and (ii) describe the different basketball game performance profiles based on the different game roles. Archival data were obtained from all 2013-2014 regular season games (n = 1230). The variables analyzed included the points per game, minutes played and the game actions recorded by the player tracking system. To accomplish the first aim, the performance per minute of play was analyzed using a descriptive discriminant analysis to identify which variables best predict the all-star and non all-star playing categories. The all-star players showed slower velocities in defense and performed better in elbow touches, defensive rebounds, close touches, close points and pull-up points, possibly due to optimized attention processes that are key for perceiving the required appropriate environmental information. The second aim was addressed using a k-means cluster analysis, with the aim of creating maximal different performance profile groupings. Afterwards, a descriptive discriminant analysis identified which variables best predict the different playing clusters. The results identified different playing profile of performers, particularly related to the game roles of scoring, passing, defensive and all-round game behavior. Coaching staffs may apply this information to different players, while accounting for individual differences and functional variability, to optimize practice planning and, consequently, the game performances of individuals and teams.

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Exploring Game Performance in the National Basketball Association Using Player Tracking Data.

The aim of the current study was to identify the Rugby gamerelated statistics that discriminated between winning and losing teams in IRB and S12 close games. Archival data reported to game-related statistics from 120 IRB games and 204 Super Twelve games played between 2003 and 2006. Afterwards, a cluster analysis was conducted to establish, according to game final score differences, three different match groups. Only the close games group was selected for further analysis (IRB n = 64 under 15 points difference and Super Twelve n = 95 under 11 points difference). An analysis to the structure coefficients (SC) obtained through a discriminant analysis allowed to identify the most powerful game-related statistics in discriminating between winning and losing teams. The discriminant functions were statistically significant for Super Twelve games (Chi-square = 33.8, p < 0.01), but not for IRB games (Chi-square = 9.4, p = n.s.). In the first case, winners and losers were discriminated by possessions kicked (SC = 0.48), tackles made (SC = 0.45), rucks and pass (SC = -0.40), passes completed (SC = 0.39), mauls won (SC = -0.36), turnovers won (SC = -0.33), kicks to touch (SC = 0.32) and errors made (SC = -0.32). The minus sign denotes higher values in losing teams. Rugby game-related statistics were able to discriminate between winners and losers in Super Twelve close games and suggest that a kicking based game supported by an effective defensive structure is more likely to win matches than a possession based one.

https://www.jssm.org/hfpdf.php?volume=09&issue=1&page=51

Rugby Game-Related Statistics that Discriminate Between Winning and Losing Teams in Irb and Super Twelve Close Games.

The aim of the present study was to identify the game-related statistics which discriminate between winning and losing teams in under-16 years old male basketball games. The sample gathered all 122 games in the 2004 and 2005 Under-16 European Championships. The game-related statistics analysed were the free-throws (both successful and unsuccessful), 2- and 3-points field-goals (both successful and unsuccessful) offensive and defensive rebounds, blocks, assists, fouls, turnovers and steals. The winning teams exhibited lower ball possessions per game and better offensive and defensive efficacy coefficients than the losing teams. Results from discriminant analysis were statistically significant and allowed to emphasize several structure coefficients (SC). In close games (final score differences below 9 points), the discriminant variables were the turnovers (SC = -0.47) and the assists (SC = 0.33). In balanced games (final score differences between 10 and 29 points), the variables that discriminated between the groups were the successful 2-point field-goals (SC = -0.34) and defensive rebounds (SC = -0. 36); and in unbalanced games (final score differences above 30 points) the variables that best discriminated both groups were the successful 2-point field-goals (SC = 0.37). These results allowed understanding that these players' specific characteristics result in a different game-related statistical profile and helped to point out the importance of the perceptive and decision making process in practice and in competition. Key pointsThe players' game-related statistical profile varied according to game type, game outcome and in formative categories in basketball.The results of this work help to point out the different player's performance described in U-16 men's basketball teams compared with senior and professional men's basketball teams.The results obtained enhance the importance of the perceptive and decision making process in practice and in competition.

Game Related Statistics Which Discriminate Between Winning and Losing Under-16 Male Basketball Games

It is appropriate to suppose that all psychology ought to fit together somehow. It should possess internal consistency. It should not be a galaxy of confusing and conflicting endeavors and understandings. An effort toward unity and coherence is entirely a legitimate one. A good starting place is in the initial contacts made in the classroom. A discipline, be it psychology or anything else, is simply a cluster of problems. These differ from discipline to discipline. The discipline consists of efforts to solve these problems, and to do so requires reliable information gained by the experimental method. The problems specific to a given discipline cannot be solved by gathering only one kind of information. Several natural caregoties are generally, if not always, involved. In psychology, to answer its ultimate problems, these categories are, for example, data regarding the energies that affect sense organs (stimuli), data regarding body processes, and data describing an organism's action as a person There is so much to be studied and discovered, that men specialize, and this specialization becomes so extreme and so narrow that the job of integrating information from several categories is often bypassed. It is as if this is left up to students to accomplish as they read textbooks and go to class from day to day if it is considered at all. This job seldom gets attention, and results are unsatisfactory. Improvement in this direction may well come from a little time spent even in introductory courses in considering the problem of relationships between kinds of information. The first step is to ask what the major problem areas in psychology are. An answer to this question is given in the following: (1) The problem of action and its direction. Centuries ago, the question was asked, "Why does anything happen, and when it does what are its rules? The men who asked this question were not psychologists, but philosophers and those asking questions about physical situations. This same question is dealt with in psychology by those who deal with motivation and seek to account for it. This requires knowing something about physics and physiological processes, but the problem pertains to action of the organism at the personalistic level. (2) Contact of the organism with the surrounds. This second problem area involves the study of sense organs and sense modalities, and of course, perception. Here the interaction between the organism and the physical (energistic) environment is dealt with. (3) The kinds of changes that occur in the human (as a function of time and practice). Learning, changes in physical fitness, acclimatization, tolerance, addiction, ageing comprise the third area. (4) Internal organization and conflict. Some parts of the nervous system are organized for homeostasis and survival, whereas others carry on directly with other realities and awareness. The two are, of course, interrelated. (5) Demand and the ability to meet it may include fatigue and impairment, boredom, anxiety, frustration.

Perceptual and Motor Skills, 1982,55, 122. @ Perceptual and Motor Skills 1982

The aim of the present study was to identify the importance of basketball performance indicators in predicting the effectiveness of ball possessions in men's and women's basketball, when controlling for situational variables and game periods. The sample consisted of 7234 ball possessions, corresponding to 40 games from the Spanish professional leagues. The effects of the predictor variables on successful ball possessions according to game period were analysed using binary logistic regressions. Results from men's teams show interactions with number of passes and ending player during the first five minutes, with starting and ending zone, defensive systems, screens used and possession duration during the middle thirty minutes, and there were interactions with passes used, possession duration and players involved during the last five minutes. Results from women's teams show interactions with starting and ending zone, passes used, defensive systems and ending player during the first five minutes, and ...

/pdf/ball-possession-effectiveness-in-men-s-and-women-s-elite-a8bz1nr6br.pdf

Ball possession effectiveness in men's and women's elite basketball according to situational variables in different game periods.

The aim of the present study was to identify the game-related statistics that allow discriminating between starters and nonstarter players in women's basketball when related to winning or losing games and best or worst teams. The sample comprised all 216 regular season games from the 2005 Women's National Basketball Association League (WNBA). The game-related statistics included were 2- and 3- point field-goals (both successful and unsuccessful), free-throws (both successful and unsuccessful), defensive and offensive rebounds, assists, blocks, fouls, steals, turnovers and minutes played. Results from multivariate analysis showed that when best teams won, the discriminant game-related statistics were successful 2-point field-goals (SC = 0.47), successful free-throws (SC = 0.44), fouls (SC = -0.41), assists (SC = 0.37), and defensive rebounds (SC = 0.37). When the worst teams won, the discriminant game-related statistics were successful 2-point field- goals (SC = 0.37), successful free-throws (SC = 0.45), assists (SC = 0.58), and steals (SC = 0.35). The results showed that the successful 2-point field-goals, successful free-throws and the assists were the most powerful variables discriminating between starters and nonstarters. These specific characteristics helped to point out the importance of starters' players shooting and passing ability during competitions. Key pointsThe players' game-related statistical profile varied according to team status, game outcome and team quality in women's basketball.The results of this work help to point out the different player's performance described in women's basketball compared with men's basketball.The results obtained enhance the importance of starters and nonstarters contribution to team's performance in different game contexts.Results showed the power of successful 2-point field-goals, successful free-throws and assists discriminating between starters and nonstarters in all the analyses.

https://www.jssm.org/hfpdf.php?volume=08&issue=2&page=278

Sergio-José Ibañez

Papers

Ball possession effectiveness in men's and women's elite basketball according to situational variables in different game periods.

Game Related Statistics Discriminating Between Starters and Nonstarters Players in Women’S National Basketball Association League (WNBA)