Understanding how youth football players base their game interactions may constitute a solid criterion for fine-tuning the training process and, ultimately, to achieve better individual and team performances during competition. The present study aims to explore how passing networks and positioning variables can be linked to the match outcome in youth elite association football. The participants included 44 male elite players from under-15 and under-17 age groups. A passing network approach within positioning-derived variables was computed to identify the contributions of individual players for the overall team behaviour outcome during a simulated match. Results suggested that lower team passing dependency for a given player (expressed by lower betweenness network centrality scores) and high intra-team well-connected passing relations (expressed by higher closeness network centrality scores) were related to better outcomes. The correlation between the dyads’ positioning regularity and the passing density showed a most likely higher correlation in under-15 (moderate effect), indicating a possible more dependence of the ball position rather than in the under-17 teams (small/unclear effects). Overall, this study emphasizes the potential of coupling notational analyses with spatial-temporal relations to produce a more functional and holistic understanding of teams’ sports performance. Also, the social network analysis allowed to reveal novel key determinants of collective performance.

The purpose of this study was to analyse the validity and reliability of a novel iPhone app (named: PowerLift) for the measurement of mean velocity on the bench-press exercise. Additionally, the accuracy of the estimation of the 1-Repetition maximum (1RM) using the load–velocity relationship was tested. To do this, 10 powerlifters (Mean (SD): age = 26.5 ± 6.5 years; bench press 1RM · kg⁻¹ = 1.34 ± 0.25) completed an incremental test on the bench-press exercise with 5 different loads (75–100% 1RM), while the mean velocity of the barbell was registered using a linear transducer (LT) and Powerlift. Results showed a very high correlation between the LT and the app (r = 0.94, SEE = 0.028 m · s⁻¹) for the measurement of mean velocity. Bland–Altman plots (R² = 0.011) and intraclass correlation coefficient (ICC = 0.965) revealed a very high agreement between both devices. A systematic bias by which the app registered slightly higher values than the LT (P < 0.05; mean difference (SD) between instruments = 0.008 ± 0.03 m · s⁻¹). Finally, actual and estimated 1RM using the app were highly correlated (r = 0.98, mean difference (SD) = 5.5 ± 9.6 kg, P < 0.05). The app was found to be highly valid and reliable in comparison with a LT. These findings could have valuable practical applications for strength and conditioning coaches who wish to measure barbell velocity in the bench-press exercise.

The purpose of this study was to analyze the validity, reliability and accuracy of new wearable and smartphone-based technology for the measurement of barbell velocity in resistance training exercises. To do this, 10 highly trained powerlifters (age = 26.1 3.9yrs.) performed 11 repetitions with loads ranging 50-100% of the 1-Repetition maximum in the bench-press, full-squat and hip-thrust exercises while barbell velocity was simultaneously measured using a linear transducer (LT), two Beast wearable devices (one placed on the subjects’ wrist –BW–, and the other one directly attached to the barbell –BB–) and the iOS PowerLift app. Results showed a very high correlation between the LT and BW (r = 0.94-0.98, SEE = 0.04-0.07 m/s), BB (r = 0.97-0.98, SEE = 0.04-0.05 m/s) and the PowerLift app (r = 0.97-0.98, SEE = 0.03-0.05 m/s) for the measurement of barbell velocity in the three exercises. Paired samples T-test revealed systematic biases between the LT and BW, BB and the app in the hip-thrust, between the LT and BW in the full-squat and between the LT and BB in the bench-press exercise (p < 0.001). Moreover, the analysis of the linear regression on the Bland-Altman plots showed that the differences between the LT and BW (R2 = 0.004-0.03), BB (R2 = 0.007-0.01) and the app (R2 = 0.001-0.03) were stable across the whole range of velocities analyzed. Finally, the reliability of the BW (ICC = 0.910-0.988; Cronbach’s α = 0.952-0.975), BB (ICC = 0.922-0.990; Cronbach’s α = 0.958-0.981) and the app (ICC = 0.928-0.989; Cronbach’s α = 0.961-0.981) for the measurement of the two repetitions performed with each load were almost the same than that observed with the LT (ICC = 0.937-0.990; Cronbach’s α = 0.961-0.981). Both the Beast wearable device and the PowerLift app were highly valid, reliable and accurate for the measurement of barbell velocity in the bench-press, full-squat and hip-thrust exercises. These results could have potential practical applications for strength & conditioning coaches who wish to measure barbell velocity during resistance training.

Ball screens are one of the most frequently used tactical behaviour in elite basketball games. The aim of the present study was to identify their predictors of success related to time, space, players, and tasks performed. The sample was composed of 818 ball screens corresponding to 20 close games (mean differences in score of 3.1 ± 0.8 points) randomly selected from the playoff games of the Spanish Basketball League (2008-2011). Classification tree analysis (CHAID) was used to analyse which variable or combination of variables, better predicts effectiveness during ball screens. The main results allowed identifying interactions with dribbler actions after the screen and the orientation of the screen on the ball. The results showed no interaction with game quarter and quarter minute temporal-related variables in both analyses. The present findings allow improving coaches' strategic plans that involve selecting the most appropriate offensive approach when performing ball screens.

The talent development processes in youth football are both complex and multidimensional. The purpose of this two-fold study was to apply a multidisciplinary, machine learning approach to examine: (a) the developmental characteristics of under-9 to under-16 academy players (n = 98; Study 1), and (b) the characteristics of selected and deselected under-18 academy players (n = 18; Study 2). A combined total of 53 factors cumulated from eight data collection methods across two seasons were analysed. A cross-validated Lasso regression was implemented, using the glmnet package in R, to analyse the factors that contributed to: (a) player review ratings (Study 1), and (b) achieving a professional contract (Study 2). Results showed non-zero coefficients for improvement in subjective performance in 15 out of the 53 analysed features, with key findings revealing advanced percentage of predicted adult height (0.196), greater lob pass (0.160) and average dribble completion percentage (0.124), more total match-play hours (0.145), and an older relative age (BQ1 vs. BQ2: −0.133; BQ1 vs. BQ4: −0.060) were the most important features that contributed towards player review ratings. Moreover, PCDEQ Factor 3 and an ability to organise and engage in quality practice (PCDEQ Factor 4) were important contributing factors towards achieving a professional contract. Overall, it appears the key factors associated with positive developmental outcomes are not always technical and tactical in nature, where coaches often have their expertise. Indeed, the relative importance of these factors is likely to change over time, and with age, although psychological attributes appear to be key to reaching potential across the academy journey. The methodological techniques used here also serve as an impetus for researchers to adopt a machine learning approach when analysing multidimensional databases.

Caffeine (1,3,7-trimethylxanthine) is one of the most common substances used by athletes to enhance their performance during competition. Evidence suggests that the performance-enhancing properties of caffeine can be obtained by employing several forms of administration, namely, capsules/tablets, caffeinated drinks (energy drinks and sports drinks), beverages (coffee), and chewing gum. However, caffeinated drinks have become the main form of caffeine administration in sport due to the wide presence of these products in the market. The objective of this systematic review is to evaluate the different effects of caffeinated drinks on physical performance in various sports categories such as endurance, power-based sports, team sports, and skill-based sports. A systematic review of published studies was performed on scientific databases for studies published from 2000 to 2020. All studies included had blinded and cross-over experimental designs, in which the ingestion of a caffeinated drink was compared to a placebo/control trial. The total number of studies included in this review was 37. The analysis of the included studies revealed that both sports drinks with caffeine and energy drinks were effective in increasing several aspects of sports performance when the amount of drink provides at least 3 mg of caffeine per kg of body mass. Due to their composition, caffeinated sports drinks seem to be more beneficial to consume during long-duration exercise, when the drinks are used for both rehydration and caffeine supplementation. Energy drinks may be more appropriate for providing caffeine before exercise. Lastly, the magnitude of the ergogenic benefits obtained with caffeinated drinks seems similar in women and men athletes. Overall, the current systematic review provides evidence of the efficacy of caffeinated drinks as a valid form for caffeine supplementation in sport.