Not All About the Effort? A Comparison of Playing Intensities During Winning and Losing Game Quarters in Basketball

Contextual factors influencing basketball training and competition demands: a systematic review

This systematic review described the effects of contextual factors on basketball training and competition demands. A comprehensive search and process led to the inclusion of 28 articles representing 646 basketball players. Fourteen contextual factors were identified. A decrease in external load variables was evident in the fourth quarter of games compared to the first quarter (effect sizes: small-large). The impact of game locations on load variables was inconsistent. Game outcomes did not influence external or internal loads. Conversely, close games were associated with higher physical and physiological demands than unbalanced games (effect sizes: moderate-very large). Higher external game loads were found in won quarters (effect size: small) and during scoring streaks (effect size: moderate). In youth male teams, those with superior performance covered less distances and exhibited better locomotor ratios compared to lower-level counterparts. Weekly external and internal training loads were adjusted according to the opponent's level in adult males. Internal game loads were found to be consistent across different season phases. Weekly total loads were higher during periods of congested schedules (effect sizes: moderate-very large), with training loads being reduced to offset the increased demands of game loads (effect size: moderate). This review offers basketball practitioners' insights into the external and internal loads that can be anticipated based on the contextual factors of training and competition.

Female basketball game performance is influenced by menstrual cycle phase, age, perceived demands and game-related contextual factors

This study evaluated the influence of physical and perceived game demands, menstrual cycle phase, perceived recovery, individual and game-related contextual factors on competitive performance in professional, female basketball players.11 professional female players (age: 20.6 ± 2.7 years) were monitored for game-related statistics (Performance Index Rating, PIR; rebounds, REB; effective field goal %, eFG%; turnovers, TO), objective (PlayerLoad per minute, PL·min-1) and subjective (RPE) game loads, pre-game perceived recovery (Total Quality Recovery, TQRpre), menstrual phase (follicular; luteal) and game-related contextual factors (game location; game outcome; score differential; opponent level) during 12 official games. Separate linear mixed models were used to evaluate the influence of RPE, PL·min-1, TQRpre, menstrual phase, contextual factors, and individual characteristics (age; playing position) on game-related statistics.Higher PIR and eFG% were found for older players and those who reported higher RPE (all p < 0.05). Higher age also led to less TO (p = 0.042). eFG% was higher when players reported higher TQRpre ;(p = 0.010). Better shooting (eFG%) and rebounding (REB) performances were found during the follicular menstrual phase (p < 0.05). More REB were collected in won games (p = 0.002).This study suggests that the co-influences of perceptual, menstrual-related, individual and game-related contextual factors should be considered to optimise female basketball players' performance.

Intensity Zones and Intensity Thresholds Used to Quantify External Load in Competitive Basketball: A Systematic Review

BACKGROUND

Despite widespread use of intensity zones to quantify external load variables in basketball research, the consistency in identifying zones and accompanying intensity thresholds using predominant monitoring approaches in training and games remains unclear.

OBJECTIVES

The purpose of this work was to examine the external load intensity zones and thresholds adopted across basketball studies using video-based time-motion analysis (TMA), microsensors, and local positioning systems (LPS).

METHODS

PubMed, MEDLINE, and SPORTDiscus databases were searched from inception until 31 January 2023 for studies using intensity zones to quantify external load during basketball training sessions or games. Studies were excluded if they examined players participating in recreational or wheelchair basketball, were reviews or meta-analyses, or utilized monitoring approaches other than video-based TMA, microsensors, or LPS.

RESULTS

Following screening, 86 studies were included. Video-based TMA studies consistently classified jogging, running, sprinting, and jumping as intensity zones, but demonstrated considerable variation in classifying low-intensity (standing and walking) and basketball-specific activities. Microsensor studies mostly utilized a single, and rather consistent, threshold to identify only high-intensity activities (> 3.5 m·s-2 for accelerations, decelerations, and changes-in-direction or > 40 cm for jumps), not separately quantifying lower intensity zones. Similarly, LPS studies predominantly quantified only high-intensity activities in a relatively consistent manner for speed (> 18.0 m·s-1) and acceleration/deceleration zones (> 2.0 m·s-2); however, the thresholds adopted for various intensity zones differed greatly to those used in TMA and microsensor research.

CONCLUSIONS

Notable inconsistencies were mostly evident for low-intensity activities, basketball-specific activities, and between the different monitoring approaches. Accordingly, we recommend further research to inform the development of consensus guidelines outlining suitable approaches when setting external load intensity zones and accompanying thresholds in research and practice.

Analysis of In-Season External Load and Sport Performance in Women's Collegiate Basketball

ABSTRACT

Brown, FSA, Fields, JB, Jagim, AR, Baker, RE, and Jones, MT. Analysis of in-season external load and sport performance in women's collegiate basketball. J Strength Cond Res 38(2): 318-324, 2024-Quantifying and monitoring athlete workload throughout a competitive season is a means to manage player readiness. Therefore, the purpose of the current study was to quantify practice and game external loads and to assess the relationship between such loads and basketball-specific performance metrics across a women's collegiate basketball season. Thirteen National Collegiate Athletic Association Division I women basketball athletes (age 20.08 ± 1.55 years) wore Global Positioning Systems sensors equipped with triaxial accelerometers for 29 games and 66 practices during the 2019-20 season. A multivariate analysis of variance was used to assess differences in external load between high- and low-minute players and across quarters within games ( p < 0.05). Bivariate Pearson correlation coefficients were run to determine relationships between external loads and metrics of basketball performance. Findings indicated that high- and low-minute athletes experienced different loads during games and practices ( p < 0.001). External loads differed by quarter, such that player load (PL) was highest in Q4 ( p = 0.007), PL·min -1 was highest in Q1 and lowest in Q4 ( p < 0.001), and explosive ratio (i.e., ratio of PL and explosive efforts) was lowest in Q3 ( p = 0.45). Relationships existed between PL·min -1 and field goals ( r = 0.41; p = 0.02) and between the explosive ratio and free throws ( r = 0.377 p = 0.04). These results can be used to inform design of training sessions with the intent to prepare athletes for the demands of the competitive season. It is recommended that future research continue to explore the relationship of sport-specific performance metrics and athlete external load.

A Systematic Review on the Physical, Physiological, Perceptual, and Technical-Tactical Demands of Official 3 × 3 Basketball Games

PURPOSE

To systematically review the physical, physiological, perceptual, and technical-tactical demands of official 3 × 3 basketball games.

METHODS

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. Three electronic databases (PubMed, Scopus, and Web of Science) were systematically searched to identify studies assessing physical, physiological, perceptual, and technical-tactical demands of 3 × 3 games. Data were also coded according to player sex and tournament phase. Quality assessment of the included studies was performed using a modified Downs and Black checklist.

RESULTS

Thirteen articles were finally included, with a mean quality of 8.6 (1.1) out of 11. Three-by-three basketball games have an intermittent profile (1:1 work-rest ratio), with a duration of ∼15 minutes, and are characterized by short (6-8 s) ball possessions and considerable physical (17-33 accelerations, 24-44 decelerations, 62-94 changes of directions, and 17-24 jumps per game) and physiological (lactate: ∼6.2 mmol·L-1) demands. Overall, the game performance profile is similar in males and females, with minor changes happening across tournament phases. Several key technical-tactical indicators were identified as discriminating winning and losing teams, such as better shooting and defensive efficiency, low number of turnovers, and implementing tactical actions involving more players, passing first, and ending possessions with shots from outside of the arch from the top of the key.

CONCLUSIONS

Three-by-three basketball is an intermittent, physically demanding sport characterized by quick plays and specific tactical constraints. This review provides information that should be considered by performance staff to improve training prescription, game tactical plans, and for player selection and talent identification.

A Narrative Review of the Most Demanding Scenarios in Basketball: Current Trends and Future Directions

Since the analysis of most demanding scenarios (MDS) in basketball has improved the practical knowledge about match demands and possible impacts for the training process, it seems important to summarize the scientific evidence providing useful information and future directions related to MDS. This review assesses the results reflected in the available literature about the MDS in basketball, synthesizing and discussing data from scientific papers, and then providing relevant insights about terminology, sex and sample size, competition category, workload variables recorded, technology used, method of calculation, time windows analyzed, and activities evaluated related to MDS. Therefore, the present narrative review would be of practical use for coaches, scientists, athletes as well as strength and conditioning trainers exploring the current trends and future directions related to MDS in basketball.

Investigating External and Internal Loads in Male Older Adult Basketball Players during Official Games

This study aimed at assessing the external [Player Load (PL), acceleration (ACC), changes of direction (COD), JUMP, and their relative values (PL/min; ACC/min; COD/min and JUMP/min)] and internal [percentage of the peak heart rate (%HRpeak) and the training load calculated with the session rating of perceived exertion (sRPE) method (sRPE-load)] loads of masters (senior citizen) basketball players during official games. Thirteen male basketball masters players (age: 66.6 ± 2.1 years; body mass: 89.9 ± 8.7 kg; stature: 183.7 ± 4.6 cm) were monitored during an official Lietuvos Krepsinio Veteranu Lyga (LKVL) 65-year game. Beside descriptive analysis, a chi-square goodness of fit test was adopted to assess the differences in the distribution within JUMP, ACC and COD classes of intensities (i.e., low, medium and high). The results revealed PL = 269.9 ± 83.3 AU and PL/min = 6.54 ± 1.29 AU/min. Moreover, significant differences (p < 0.001) in the distribution of the intensity classes were found for JUMP, ACC, and COD, with the lowest intensities as the most frequent. Finally, HRpeak = 81.7 ± 8.1% and sRPE-load = 148.9 ± 69.7 AU were found, with sRPE = ~3 AU. In conclusion, a low external load during an official basketball game was found compared to other basketball populations. Moreover, a high objective internal load did not correspond to a low perceived demand, which might increase the training adherence and motivation during long-term studies.

On-Court Activity and Game-Related Statistics during Scoring Streaks in Basketball: Applied Use of Accelerometers

The aim of this observational study was to determine if on-court activity and match statistics differed between periods of scoring streaks and regular play in basketball. Thirty-seven basketballers including professional women, semi-professional women and semi-professional men wore accelerometers during competitive matches throughout a season. Accelerometry-derived live-time individual on-court exercise intensity and team game-related statistics were compared between scoring streaks (periods of play where the teams participating in the study scored at least three times in a row), streaks against (periods of play where the opposition teams scored at least three times in a row) and regular play. Few differences existed in the average exercise intensity between streak types. During streaks against, there was a 5–15% lower proportion of 2-point attempts, 0.8–1.3 fewer defensive rebounds per minute and 0.3–1.6 fewer shot attempts per minute compared to regular play and scoring streaks, and there were 0.3 fewer offensive rebounds per minute compared to regular play. During scoring streaks, there were 0.5 more defensive rebounds per minute, 1.3 more shot attempts per minute, a 43% greater shooting percentage and a 10% lower proportion of 3-point attempts compared to regular play. To reduce the chances of streaks against, teams should focus on facilitating 2-point shot attempts and consider implementing a 3:1 ratio of 2-point to 3-point attempts to maximize scoring success, and they should focus on winning rebounds to facilitate more shot attempts.

Simplifying External Load Data in NCAA Division-I Men's Basketball Competitions: A Principal Component Analysis

The primary purpose was to simplify external load data obtained during Division-I (DI) basketball competitions via principal component analysis (PCA). A secondary purpose was to determine if the PCA results were sensitive to load demands of different positional groups (POS). Data comprised 229 observations obtained from 10 men's basketball athletes participating in NCAA DI competitions. Each athlete donned an inertial measurement unit that was affixed to the same location on their shorts prior to competition. The PCA revealed two factors that possessed eigenvalues >1.0 and explained 81.42% of the total variance. The first factor comprised total decelerations (totDEC, 0.94), average speed (avgSPD, 0.90), total accelerations (totACC, 0.85), total mechanical load (totMECH, 0.84), and total jump load (totJUMP, 0.78). Maximum speed (maxSPD, 0.94) was the lone contributor to the second factor. Based on the PCA, external load variables were included in a multinomial logistic regression that predicted POS (Overall model, p < 0.0001; AUC_centers = 0.93, AUC_guards = 0.88, AUC_forwards = 0.80), but only maxSPD, totDEC, totJUMP, and totMECH were significant contributors to the model's success (p < 0.0001 for each). Even with the high significance, the model still had some issues differentiating between guards and forwards, as in-game demands often overlap between the two positions. Nevertheless, the PCA was effective at simplifying a large external load dataset collected on NCAA DI men's basketball athletes. These data revealed that maxSPD, totDEC, totJUMP, and totMECH were the most sensitive to positional differences during competitions. To best characterize competition demands, such variables may be used to individualize training and recovery regimens most effectively.

Return to Basketball Play Following COVID-19 Lockdown

Due to concerns regarding the spread of coronavirus (COVID-19), major sporting events and activities have been temporarily suspended or postponed, and a new radical sports protocol has emerged. For most sports there are few recommendations based on scientific evidence for returning to team-game activities following the lifting of COVID-19 restrictions, the extended duration of lockdown, and self-training or detraining in the COVID-19 environment, and this is especially true for basketball. A post-lockdown return to the basketball court ultimately depends on the teams-coaches, trainers, players, and medical staff. Nevertheless, our current scientific knowledge is evidently insufficient as far as safety and return-to-play timing are concerned. This situation presents a major challenge to basketball competition in terms of organization, prioritization, maintaining physical fitness, and decision-making. While preparing an adequate basketball return program, the players' health is the major priority. In this article we briefly discuss the topic and propose multiple strategies.