Inflammatory responses and muscle damage indices were compared between four popular team sports at an elite level. Seventy two male elite players of four team sports: soccer (n = 18), basketball (n = 18), volleyball (n = 18) and handball (n = 18), completed an official match, while 18 non-athletes served as controls. Blood samples were drawn before, immediately after and 13 and 37 h post-match. Soccer produced the greatest increase in inflammatory cytokines (tumor necrosis factor-alpha and interleukin-6), which were increased by 3–4 fold immediately after the game, as well as in C-reactive protein, which was increased by threefold in the next morning after the match. Metabolic stress (urea, ammonia and cortisol) and muscle damage indices (creatine kinase and lactate dehydrogenase) were also higher after soccer, with creatine kinase responses being almost 2–3 times higher than the other sports. Volleyball showed the smallest increase in inflammation and muscle damage markers compared with the other three sports.

Team offense in basketball games consists of a set of offensive actions carried out with the cooperation of two or more players. Of these actions, the most commonly used in the last decade is the on-ball screen called the “pick and roll.” The aim of this study was to analyze all of the pick and rolls conducted in the Euroleague championship from all of the 24 participating teams and to investigate the possible relationships between success in the pick and roll and overall success of the teams. For this purpose, 12,376 pick and rolls from 502 matches were analyzed and classified in categories according to the end result of the offensive possession. The results showed that the most effective type of pick and roll offense was when a shot was attempted after 2 passes from the pick and roll occurrence, followed by the screener’s shot when he rolled to the basket. Additionally, linear regression analysis confirmed that pick and roll effectiveness could predict the final classification of the teams. Conclusively, coaches of the high level European clubs should focus on training the players to the most efficient phases of the pick and roll offense, so that the chances of winning the championship to be maximized.

Background: This study examined the effects of 8 weeks of plyometric training on jumping, sprinting, and change of direction (COD) performance. Methods: Fifty female 7–9-year-old gymnasts were randomly assigned to a plyometric training group (PG; n = 33), that performed supplementary plyometric training twice per week, and a control group (CG; n = 17) that continued regular training. The following tests were performed before and after the intervention: 10 and 20 m sprints, 5 + 5 m and 10 + 10 m COD tests, one-leg and two-leg countermovement jump (CMJ), drop jump (DJ), squat jump (SJ), and standing long jump (SLJ). Results: Only a main effect for time was found for all jumping performance parameters (p = 0.001). However, the improvement of one- and two-leg CMJ in PG had a greater effect size than CG (0.72 and 0.67 vs. 0.34 and 0.18, respectively). Group × time interactions were found for 10 and 20 m sprint tests (p = 0.018 and p = 0.011, respectively) and for 10 + 10 m COD (p = 0.008) with the post hoc test showing improvement only for the PG (p = 0.001, 0.001, and 0.003 and d = 1.1, 1.14, and 0.6, respectively). Conclusions: Supplementary plyometric training increased sprint and COD performance more than regular gymnastics training, while jumping performance was equally improved in both groups.

The lipid profile of elite basketball and soccer athletes was evaluated and compared with that of inactive individuals. Total cholesterol (T-C), low and high density lipoprotein cholesterol (LDL-C and HDL-C), and triglyceride (TG) concentration were measured in the morning and after a soccer or a basketball match. All parameters of lipid profile measured at a fasted and resting state, except HDL-C, were lower in the athletes compared with the controls (p < 0.01). The soccer match resulted in a greater decrease in TG (78.3 ± 6.7 to 70.7 ± 6.3, p < 0.01), T-C (179.3 ± 10.7 to 171.6 ± 9.6, p < 0.01), LDL-C (110.9 ± 8.9 to 103.5 ± 7.5, p < 0.01) compared with the basketball match that resulted only in a decrease in LDL-C (126.8 ± 9.5 to 117.3 ± 9.1, p < 0.01) and an increase in HDL-C that was similar to that observed after the soccer match (9–12%). These findings support the beneficial effects of basketball and soccer on cardiovascular health.

Basketball is a combined game demanding elevated physical and technical skills, mental characteristics and tactical behaviour. These characteristics cannot be evaluated only by skill tests, which are considered inadequate to record the mental ability of the athletes. For the complete evaluation of the athletes’ performance observation methods have been proposed, which record all of their efforts during the game. The aim of the present study was to evaluate the athletes’ efficacy in passing skill, with two different methods: (a) the skill test and (b) game observation and correlate the results with the total game performance. For this purpose three basketball teams composed by 33 athletes, aged 14-15 years old, participated in the study. Initially, the athletes performed the passing test and afterwards in order to evaluate their passing efficacy and their overall performance, three games between the teams were recorded. Results indicated that performance in passing skill test was not correlated with passing efficacy or with total performance in the games. On the contrary, passing efficacy in the games was significantly correlated and could clearly predict the overall performance of the athlete. The athletes’ performance in passing skill should not be evaluated only by application of test, but additionally by observation of the games.

There have been numerous efforts from basketball stakeholders to produce an index that will evaluate the performance of a basketball player. Data derived from the box score are highly available in many leagues across the world and are the primary source for such indexes. The aim of this study was to produce an all-inclusive performance metric that is valid and reliable. For this purpose, data of 442 players across the seasons 2016–17 and 2017–18, both directly and box score derived, were incorporated into the new Total Performance Index (TPI). The index consisted of the offensive and defensive part. The latter included the Points Offered and the Points Received parts. TPI was compared with the predominantly used Performance Index Rating index (PIR). Results from the linear regression analysis indicated that a statistically significant part of the variation of the winning percentage was explained by TPI. In addition, the future performance of a player can be predicted using TPI, independently if the players have played for the same or different team. Both the validity and the reliability of TPI are better than the ones of the officially used PIR. As a conclusion, basketball stakeholders could use TPI as a valid and reliable players’ evaluation tool.