In soccer, the assessment of the load imposed by training and a match is recognized as a fundamental task at any competitive level. The objective of this study is to carry out a systematic review on internal and external load monitoring during training and/or a match, identifying the measures used. In addition, we wish to make recommendations that make it possible to standardize the classification and use of the different measures. The systematic review was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search was conducted through the electronic database Web of Science, using the keywords “soccer” and “football”, each one with the terms “internal load”, “external load”, and “workload”. Of the 1223 studies initially identified, 82 were thoroughly analyzed and are part of this systematic review. Of these, 25 articles only report internal load data, 20 report only external load data, and 37 studies report both internal and external load measures. There is a huge number of load measures, which requires that soccer coaches select and focus their attention on the most useful and specific measures. Standardizing the classification of the different measures is vital in the organization of this task, as well as when it is intended to compare the results obtained in different investigations.

The aim of the present paper was to examine the differences in game‐related statistics between basketball guards, forwards and centres playing in three professional leagues: National Basketball Association (NBA, superior level) in the USA, Associación de Clubs de Baloncesto (ACB, one of the best European leagues) in Spain and Liga de Clubes de Basquetebol (LCB, inferior level) in Portugal. We reasoned that the knowledge of these differences could allow the coaches to better establish and monitor playing patterns and increase the effectiveness of the player recruitment process. Archival data was gathered for the 2000–2001 play‐off final series of the NBA (five games), ACB (three games) and LCB (four games). For players in each league, discriminant analysis was able to identify game‐related statistics that maximized mean differences between playing positions (p<0.05). The interpretation of the obtained discriminant functions was based on examination of the structure coefficients greater than ∣0.30∣. In the LCB league, centres and guards were discriminated mainly in terms of defensive tasks, with emphasis on blocks (structure coefficient, SC=0.35) and defensive rebounds (SC=0.43) and a de‐emphasis on unsuccessful 3‐point field‐goals (SC= − 0.37). In the ACB, centres and guards were discriminated by offensive tasks, with emphasis on assists (SC=0.52) and 3‐point field‐goals, both successful (SC=0.35) and unsuccessful (SC=0.35), and a de‐emphasis on offensive rebounds (SC= − 0.44). Finally, in the NBA league guards and centres were discriminated by offensive tasks, with emphasis on offensive rebounds (SC=0.31) and a de‐emphasis on assists (SC= − 0.37) and unsuccessful 3‐point field‐goals (SC= − 0.34). These three analyses provided high overall percentages of successful classification (86% for the LCB league, 74% for the ACB and 85% for the NBA). Generally, the players’ game‐related statistics varied according to playing position, probably because of the well‐known differences in the players’ anthropometric characteristics that conditioned the distance they play from the basket. Coaches can use these results to reinforce the importance of relying on different players’ contributions to team performance and evaluate players’ game performance according to their playing position. Conversely, these discriminant models could help in player recruitment and improve training programmes.

The aim of the present study was to identify the game‐related statistics that discriminate between season‐long successful and unsuccessful basketball teams participating in the Spanish Basketball League (LEB1). The sample included all 145 average records per season from the 870 games played between the 2000–2001 and the 2005–2006 regular seasons. The following game‐related statistics were gathered from the official box scores of the Spanish Basketball Federation: 2‐ and 3‐point field‐goal attempts (both successful and unsuccessful), free‐throws (both successful and unsuccessful), defensive and offensive rebounds, assists, steals, turnovers, blocks (both made and received), and fouls (both committed and received). To control for season variability, all results were normalized to minutes played each season and then converted to z‐scores. The results allowed discrimination between best and worst teams’ performances through the following game‐related statistics: assists (SC=0.47), steals (SC=0.34), and blocks (SC=0.30). The function obtained correctly classified 82.4% of the cases. In conclusion, season‐long performance may be supported by players’ and teams’ passing skills and defensive preparation.

The aim of the present study was to identify basketball game performance indicators which best discriminate winners and losers in regular season and playoffs. The sample used was composed by 323 games of ACB Spanish Basketball League from the regular season (n=306) and from the playoffs (n=17). A previous cluster analysis allowed splitting the sample in balanced (equal or below 12 points), unbalanced (between 13 and 28 points) and very unbalanced games (above 28 points). A discriminant analysis was used to identify the performance indicators either in regular season and playoff games. In regular season games, the winning teams dominated in assists, defensive rebounds, successful 2 and 3-point field-goals. However, in playoff games the winning teams’ superiority was only in defensive rebounding. In practical applications, these results may help the coaches to accurately design training programs to reflect the importance of having different offensive set plays and also have specific conditioning programs to prepare for defensive rebounding.

The purpose of this study was to assess the accuracy of positional data and the inter-unit reliability of an ultra-wide-band (UWB) tracking system. Four well-trained males performed five courses designed for the analysis of x- and y-coordinate accuracy analysis, specifically related to the positional distance variation between the UWB data and the fixed reference lines of a basketball court. This was achieved using geographic information system (GIS) mapping software that calculated, for each interval and participant, the distance from the main axis of displacement and from the opposite side of the court each 0.5 s (x and y coordinate). The accuracy of the results was satisfactory, with a mean absolute error of all estimations for the x-position of 5.2 ± 3.1 cm and for the y-position of 5.8 ± 2.3 cm. Regarding inter-unit reliability, the intra-class correlation coefficient (ICC) value was high for the x-coordinate (0.65) and very high for the y-coordinate (0.85). The main findings of the study were: (i) The accuracy of UWB tracking systems can be considered suitable for practical applications in sport analyses; (ii) position estimations are very precise and acceptable for tactical analyses; (iii) the error of the position estimations does not change significantly across different courses; and (iv) the use of different devices does not significantly affect the measurement error.

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 with starting and ending zone, and screens used during the middle thirty minutes. The results show no interaction with situational variables in men's basketball, while league stage was important during the middle thirty minutes and last five minutes in women's basketball, whereas match status was only important during the last five minutes.