A 4-Year Profile of Game Demands in Elite Women's Division I College Basketball

GPS External Load Metric Data and Game Performance in NCAA Division I Women's Lacrosse Athletes: A Longitudinal Study

This study investigates the relationship between GPS-derived external load metrics and game performance (win/loss) in NCAA Division I women's lacrosse athletes. Utilizing data from three seasons (2022-2024), the study analyzed 1,687 observations from 54 players to identify key performance indicators correlating with game outcomes. GPS metrics including Total Distance (TD), High-Speed Distance (HSD), Very High-Speed Efforts (VHSE), Total Player Load (TPL), High Inertial Movement Analysis (High IMAs), and Total Acceleration Load (TAL) were assessed. Multivariate logistic regression results indicate that VHSE is the most significant predictor of game success, with VHSE showing a positive correlation with winning outcomes (p = 0.007; OR = 1.017, 95% CI [1.005, 1.030]). Although other metrics like TD and TPL were significant in univariate models, their impact diminished in multivariate analysis, suggesting their effects are intertwined with other performance factors. The study highlights the importance of high-intensity efforts in game outcomes and provides insights for optimizing training strategies for female lacrosse athletes. These findings underscore the need for continued research into female athlete performance to better inform sport-specific training programs and enhance competitive success.

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.

Athlete External Load Measures Across a Competitive Season in High School Basketball

ABSTRACT

Askow, AT, Jennings, W, Jagim, AR, Fields, JB, Beaudoin, RG, Sanchez, GM, Weeks, JE, Oliver, JM, and Jones, MT. Athlete external load measures across a competitive season in high school basketball. J Strength Cond Res 37(11): 2206-2212, 2023-The purpose of this retrospective analysis was to quantify in-season external load and to determine if relationships existed between load metrics and basketball performance. Eleven male high school varsity basketball athletes (n = 11; mass 80.5 ± 9.6 kg, height 190.2 ± 9.4 cm, age 17.6 ± 0.7 years) were monitored across a season. PlayerLoad (PL), PL per minute (PL·min -1 ), total jumps, and explosive movements (EMs) were quantified using a commercially available local positioning unit. Basketball-specific performance metrics, including points scored, points allowed, point differentials, and shooting percentages for each quarter and game, were compiled. Data were analyzed using repeated-measure analysis of variance to evaluate differences in load by starting status, session type, game outcome, and game type. Pearson's correlation coefficients were used to assess relationships between load metrics and basketball performance. Statistical significance was set at p < 0.05. The mean values across 23 games for PL, PL·min -1 , total jumps, and EMs were 457 ± 104 AU, 10.9 ± 1.6 AU, 42.6 ± 9.6, and 46.7 ± 7.2, respectively. Relationships were observed ( p < 0.05) between PL and points scored ( r = 0.38) and free throw percentage ( r = 0.21). Further relationships were observed between PL·min -1 and free throw shooting percentage ( r = -0.27), and between points scored and total jumps ( r = 0.28), and EMs ( r = 0.26). Notable differences in game demands were observed for playing status. Meaningful differences in measures of external load were observed between each quarter of play, with the highest measures evident in quarters 1 and 3. Guards and forwards experienced minimal differences in external load during gameplay, and game outcome did not result in differences. Higher point totals corresponded with higher PL, total jumps, and EM.

External Load Monitoring in Female Basketball: A Systematic Review

The primary aim of this systematic review was to summarize the current state of research in relation to external load monitoring in female basketball. The review was conducted according to the PRISMA-P® statement. Publications included in the review: 1) were original research, 2) evaluated healthy female basketball players, and 3) monitored basketball practice and competition. The STROBE scale was used to assess quality. A total of 40 publications were included. The external load was assessed during practice (n = 9), competition (n = 11) or both events (n = 8). Also, time-motion analysis was implemented in practice (n = 2), competition (n = 9), or both events (n = 1). Accelerometry (n = 28) and time-motion (n = 12) analysis were the most frequently used methods. However, a wide range in methods and variables were used to quantify the external load. Placement of devices on the upper back and measuring with a sampling frequency of 100 Hz were most common. Player Load (PL) values increased with the competitive level of players and were higher in competition compared to training. Small-sided games can be used to gradually increase loads in female basketball (PL 5v5: 34.8 ± 8, PL 3v3: 47.6 ± 7.4, TD 5v5: 209.2 ± 35.8 m, and TD 3v3: 249.3 ± 2.8 m). Tasks without defense seemed to be less demanding. More research is needed to reach a consensus on load control in women's basketball, on what data are important to collect, and how to use and transfer knowledge to stakeholders.

Longitudinal monitoring of workloads in women's division I (DI) collegiate basketball across four training periods

Women's collegiate basketball is a fast-growing, dynamic sport that spans 8 or more months, with athletes competing in 30 + games in a season. The aim of this study was to quantify and profile the external load of practices and games during a Power-5 DI Women's Collegiate Basketball season. Specifically, Average PlayerLoad (PL), PlayerLoad per minute (PL*min^-1), High Inertial Movement Analysis (High-IMA), and Jumps were quantified using Catapult Openfield software during four distinct training periods of the year: 8-hour preseason, 20-hour preseason, non-conference, and conference game play. Weekly variations and acute to chronic workload ratios (ACWR) were also examined. Eleven subjects participated in daily external load monitoring during practice and games via Catapult's ClearSky T6 inertial measurement units (IMU). Averages, standard deviations, and confidence intervals were calculated for training period comparisons, and Cohen's d was calculated as a measure of effect size. Findings include normative values to provide context for the demands experienced across an entire season. PL was significantly higher during non-conference play than during any of the other three training periods (p < 0.05). Descriptive data enumerate percent change and ACRW variations throughout the season. These data can be used to describe the physical demands across a season and provide physical profile guidelines for coaches.

External and Internal Load Variables Encountered During Training and Games in Female Basketball Players According to Playing Level and Playing Position: A Systematic Review

Background

Despite the growing global participation of females in basketball and number of studies conducted on the topic, no research has summarized the external and internal load variables encountered by female basketball players during training and games.

Objective

To collate existing literature investigating external and internal load variables during training and games in female basketball players according to playing level (club, high-school, representative, collegiate, semi-professional, and professional) and playing position (backcourt and frontcourt players).

Methods

A systematic review of the literature was performed using PubMed, SPORTDiscus, and Web of Science to identify studies published from database inception until June 11, 2021. Studies eligible for inclusion were observational and cross-sectional studies, published in English, reporting external and/or internal load variables during training sessions and/or games. Methodological quality and bias were assessed for each study prior to data extraction using a modified Downs and Black checklist. Weighted means according to playing level and playing position were calculated and compared if a load variable was reported across two or more player samples and were consistent regarding key methodological procedures including the seasonal phase monitored, minimum exposure time set for including player data (playing time during games), approach to measure session duration, and approach to measure session intensity.

Results

The search yielded 5513 studies of which 1541 studies were duplicates. A further 3929 studies were excluded based on title and abstract review, with 11 more studies excluded based on full-text review. Consequently, 32 studies were included in our review. Due to the wide array of methodological approaches utilized across studies for examined variables, comparisons could only be made according to playing level for blood lactate concentration during games, revealing backcourt players experienced higher lactate responses than frontcourt players (5.2 ± 1.9 mmol·L−1 vs. 4.4 ± 1.8 mmol·L−1).

Conclusions

Inconsistencies in the methods utilized to measure common load variables across studies limited our ability to report and compare typical external and internal loads during training and games according to playing level and position in female basketball players. It is essential that standardized methodological approaches are established for including player data as well as measuring session duration (e.g., total time, live time) and intensity (e.g., consistent rating of perceived exertion scales, intensity zone cut points) in future female basketball research to permit meaningful interpretation and comparisons of load monitoring data across studies.

Current and Future Trends in Strength and Conditioning for Female Athletes

Participation by female athletes in competitive sport has increased dramatically since the inception of Title IX, although female athletes are represented significantly less than their male counterparts in strength and conditioning (S&C) literature. This is apparent when examining current identified trends in the field, such as implementation of blood flow restriction (BFR) training, functional assessments to predict injuries, or the ever-increasing use of technology in sports. The aim of this review is to examine three prevalent trends in contemporary S&C literature as they relate to female athletes in order to expose areas lacking in research. We conducted journal and database searches to progressively deepen our examination of available research, starting first with broad emerging themes within S&C, followed next by an inquiry into literature concerning S&C practices in females, ending finally with a review of emerging topics concerning female athletes. To this end, 534 articles were reviewed from PubMed, Academic Search Complete, Google Scholar, CINAHL, MEDLINE, and Web of Science. Results demonstrate the utility of implementing BFR, functional movement assessments, and various technologies among this population to expand representation of female athletes in S&C literature, improve athletic capabilities and performance, and decrease potential for injury over time.

Quantifying Training and Game Demands of a National Basketball Association Season

Purpose: There are currently no data describing combined practice and game load demands throughout a National Basketball Association (NBA) season. The primary objective of this study was to integrate external load data garnered from all on-court activity throughout an NBA season, according to different activity and player characteristics. Methods: Data from 14 professional male basketball players (mean ± SD; age, 27.3 ± 4.8 years; height, 201.0 ± 7.2 cm; body mass, 104.9 ± 10.6 kg) playing for the same club during the 2017-2018 NBA season were retrospectively analyzed. Game and training data were integrated to create a consolidated external load measure, which was termed integrated load. Players were categorized by years of NBA experience (1-2y, 3-5y, 6-9y, and 10 + y), position (frontcourt and backcourt), and playing rotation status (starter, rotation, and bench). Results: Total weekly duration was significantly different (p < 0.001) between years of NBA playing experience, with duration highest in 3-5 year players, compared with 6-9 (d = 0.46) and 10+ (d = 0.78) year players. Starters experienced the highest integrated load, compared with bench (d = 0.77) players. There were no significant differences in integrated load or duration between positions. Conclusion: This is the first study to describe the seasonal training loads of NBA players for an entire season and shows that a most training load is accumulated in non-game activities. This study highlights the need for integrated and unobtrusive training load monitoring, with engagement of all stakeholders to develop well-informed individualized training prescription to optimize preparation of NBA players.

Weekly Training Demands Increase, but Game Demands Remain Consistent Across Early, Middle, and Late Phases of the Regular Season in Semiprofessional Basketball Players

PURPOSE

To compare weekly training, game, and overall (training and games) demands across phases of the regular season in basketball.

METHODS

Seven semiprofessional, male basketball players were monitored during all on-court team-based training sessions and games during the regular season. External monitoring variables included PlayerLoad™ and inertial movement analysis events per minute. Internal monitoring variables included a modified summated heart rate zones model calculated per minute and rating of perceived exertion. Linear mixed models were used to compare training, game, and overall demands between 5-week phases (early, middle, and late) of the regular season with significance set at P ≤ .05. Effect sizes were calculated between phases and interpreted as: trivial, <0.20; small, 0.20 to 0.59; moderate, 0.60 to 1.19; large, 1.20 to 1.99; very large, ≥2.00.

RESULTS

Greater (P > .05) overall inertial movement analysis events (moderate-very large) and rating of perceived exertion (moderate) were evident in the late phase compared with earlier phases. During training, more accelerations were evident in the middle (P = .01, moderate) and late (P = .05, moderate) phases compared with the early phase, while higher rating of perceived exertion (P = .04, moderate) was evident in the late phase compared with earlier phases. During games, nonsignificant, trivial-small differences in demands were apparent between phases.

CONCLUSIONS

Training and game demands should be interpreted in isolation and combined given overall player demands increased as the season progressed, predominantly due to modifications in training demands given the stability of game demands. Periodization strategies administered by coaching staff may have enabled players to train at greater intensities late in the season without compromising game intensity.

External Load and Muscle Activation Monitoring of NCAA Division I Basketball Team Using Smart Compression Shorts

There is scarce research into the use of Strive Sense3 smart compression shorts to measure external load with accelerometry and muscle load (i.e., muscle activations) with surface electromyography in basketball. Sixteen external load and muscle load variables were measured from 15 National Collegiate Athletic Association Division I men's basketball players with 1137 session records. The data were analyzed for player positions of Centers (n = 4), Forwards (n = 4), and Guards (n = 7). Nonparametric bootstrapping was used to find significant differences between training and game sessions. Significant differences were found in all variables except Number of Jumps and all muscle load variables for Guards, and all variables except Muscle Load for Forwards. For Centers, the Average Speed, Average Max Speed, and Total Hamstring, Glute, Left, and Right Muscle variables were significantly different (p < 0.05). Principal component analysis was conducted on the external load variables. Most of the variance was explained within two principal components (70.4% in the worst case). Variable loadings of principal components for each position were similar during training but differed during games, especially for the Forward position. Measuring muscle activation provides additional information in which the demands of each playing position can be differentiated during training and competition.

Multi-Location External Workload Profile in Women's Basketball Players. A Case Study at the Semiprofessional-Level

The external workload measured in one anatomical location does not determine the total load supported by the human body. Therefore, the purpose of the present study was to characterize the multi-location external workload through PlayerLoad_RT of 13 semi-professional women’s basketball players, as well as to analyze differences among anatomical locations (inter-scapulae line, lumbar region, 2× knee, 2× ankle) and laterality (left vs. right) during five tests that represent the most common movements in basketball—(a) linear locomotion, 30-15 IFT; (b) acceleration and deceleration, 16.25-m RSA (c) curvilinear locomotion, 6.75-m arc (d) jump, Abalakov test (e) small-sided game, 10’ 3 vs. 3 10 × 15-m. Statistical analysis was composed of a repeated-measures t-test and eta partial squared effect size. Regarding laterality, differences were found only in curvilinear locomotion, with a higher workload in the outer leg (p < 0.01; η_p² = 0.33–0.63). In the vertical profile, differences among anatomical locations were found in all tests (p < 0.01; η_p² = 0.56–0.98). The nearer location to ground contact showed higher values except between the scapulae and lumbar region during jumps (p = 0.83; η_p² = 0.00). In conclusion, the multi-location assessment of external workload through a previously validated test battery will make it possible to understand the individual effect of external workload in each anatomical location that depends on the type of locomotion. These results should be considered when designing specific strategies for training and injury prevention.

Assessing the External Load Associated With High-Intensity Activities Recorded During Official Basketball Games

Load monitoring in basketball is fundamental to develop training programs, maximizing performance while reducing injury risk. However, information regarding the load associated with specific activity patterns during competition is limited. This study aimed at assessing the external load associated with high-intensity activities recorded during official basketball games, with respect to different (1) activity patterns, (2) playing positions, and (3) activities performed with or without ball. Eleven male basketball players (six backcourt, five frontcourt, age: 20.5 ± 1.1 years, stature: 191.5 ± 8.7 cm, body mass: 86.5 ± 11.3 kg; experience: 8.5 ± 2.4 years) competing in the Lithuanian third division were recruited for this study. Three in-season games were assessed via time-motion analysis and microsensors. Specifically, the high-intensity activities including sprints, high-intensity specific movements (HSM) and jumps were identified and subsequently the external load [PlayerLoad™ (PL) and PlayerLoad™/min (PL/min)] of each activity was determined. Linear mixed models were used to examine differences in PL, PL/min and mean duration between activity pattern, playing positions and activities performed with or without ball. Results revealed PL was lower in jumps compared to sprints [p < 0.001, effect size (ES) = 0.68] and HSMs (p < 0.001, ES = 0.58), while PL/min was greater in sprints compared to jumps (p = 0.023, ES = 0.22). Jumps displayed shorter duration compared to sprints (p < 0.001, ES = 1.10) and HSMs (p < 0.001, ES = 0.81), with HSMs lasting longer than sprints (p = 0.002, ES = 0.17). Jumps duration was longer in backcourt than frontcourt players (p < 0.001, ES = 0.33). When considering activity patterns combined, PL (p < 0.001, ES = 0.28) and duration (p < 0.001, ES = 0.43) were greater without ball. Regarding HSMs, PL/min was higher with ball (p = 0.036, ES = 0.14), while duration was longer without ball (p < 0.001, ES = 0.34). The current findings suggest that external load differences in high-intensity activities exist among activity patterns and between activities performed with and without ball, while no differences were found between playing positions. Practitioners should consider these differences when designing training sessions.

Relationships Between Different Internal and External Training Load Variables and Elite International Women's Basketball Performance

PURPOSE

To investigate the relationships between internal and external training load (TL) metrics with elite international women's basketball performance.

METHODS

Sessional ratings of perceived exertion, PlayerLoad™/minute, and training duration were collected from 13 elite international-level female basketball athletes (age 29.0 [3.7] y, stature 186.0 [9.8] cm, body mass 77.9 [11.6] kg) during the 18 weeks prior to the International Basketball Federation Olympic qualifying event for the 2016 Rio Olympic Games. Training stress balance, differential load, and the training efficiency index were calculated with 3 different smoothing methods. These TL metrics and their change in the last 21 days prior to competition were examined for their relationship to competition performance as coach ratings of performance.

RESULTS

For a number of TL variables, there were consistent significant small to moderate correlations with performance and significant small to large differences between successful and unsuccessful performances. However, these differences were only evident for external TL when using exponentially weighted moving averages to calculate TL. The variable that seemed most sensitive to performance was the change in training efficiency index in the last 21 days prior to competition (performance r = .47-.56, P < .001 and difference between successful and unsuccessful performance P < .001, f2 = 0.305-0.431).

CONCLUSIONS

Internal and external TL variables were correlated with performance and distinguished between successful and unsuccessful performances among the same players during international women's basketball games. Manipulating TL in the last 3 weeks prior to competition may be worthwhile for basketball players' performance, especially in internal TL.

Measuring Physical Demands in Basketball: An Explorative Systematic Review of Practices

BACKGROUND

Measuring the physical work and resultant acute psychobiological responses of basketball can help to better understand and inform physical preparation models and improve overall athlete health and performance. Recent advancements in training load monitoring solutions have coincided with increases in the literature describing the physical demands of basketball, but there are currently no reviews that summarize all the available basketball research. Additionally, a thorough appraisal of the load monitoring methodologies and measures used in basketball is lacking in the current literature. This type of critical analysis would allow for consistent comparison between studies to better understand physical demands across the sport.

OBJECTIVES

The objective of this systematic review was to assess and critically evaluate the methods and technologies used for monitoring physical demands in competitive basketball athletes. We used the term 'training load' to encompass the physical demands of both training and game activities, with the latter assumed to provide a training stimulus as well. This review aimed to critique methodological inconsistencies, establish operational definitions specific to the sport, and make recommendations for basketball training load monitoring practice and reporting within the literature.

METHODS

A systematic review of the literature was performed using EBSCO, PubMed, SCOPUS, and Web of Science to identify studies through March 2020. Electronic databases were searched using terms related to basketball and training load. Records were included if they used a competitive basketball population and incorporated a measure of training load. This systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO Registration # CRD42019123603), and approved under the National Basketball Association (NBA) Health Related Research Policy.

RESULTS

Electronic and manual searches identified 122 papers that met the inclusion criteria. These studies reported the physical demands of basketball during training (n = 56), competition (n = 36), and both training and competition (n = 30). Physical demands were quantified with a measure of internal training load (n = 52), external training load (n = 29), or both internal and external measures (n = 41). These studies examined males (n = 76), females (n = 34), both male and female (n = 9), and a combination of youth (i.e. under 18 years, n = 37), adults (i.e. 18 years or older, n = 77), and both adults and youth (n = 4). Inconsistencies related to the reporting of competition level, methodology for recording duration, participant inclusion criteria, and validity of measurement systems were identified as key factors relating to the reporting of physical demands in basketball and summarized for each study.

CONCLUSIONS

This review comprehensively evaluated the current body of literature related to training load monitoring in basketball. Within this literature, there is a clear lack of alignment in applied practices and methodological framework, and with only small data sets and short study periods available at this time, it is not possible to draw definitive conclusions about the true physical demands of basketball. A detailed understanding of modern technologies in basketball is also lacking, and we provide specific guidelines for defining and applying duration measurement methodologies, vetting the validity and reliability of measurement tools, and classifying competition level in basketball to address some of the identified knowledge gaps. Creating alignment in best-practice basketball research methodology, terminology and reporting may lead to a more robust understanding of the physical demands associated with the sport, thereby allowing for exploration of other research areas (e.g. injury, performance), and improved understanding and decision making in applying these methods directly with basketball athletes.

Training load, recovery and game performance in semiprofessional male basketball: influence of individual characteristics and contextual factors

This study examined the effects of individual characteristics and contextual factors on training load, pre-game recovery and game performance in adult male semi-professional basketball. Fourteen players were monitored, across a whole competitive season, with the session-RPE method to calculate weekly training load, and the Total Quality Recovery Scale to obtain pre-game recovery scores. Additionally, game-related statistics were gathered during official games to calculate the Performance Index Rating (PIR). Individual characteristics and contextual factors were grouped using k-means cluster analyses. Separate mixed linear models for repeated measures were performed to evaluate the single and combined (interaction) effects of individual characteristics (playing experience; playing position; playing time) and contextual factors (season phase; recovery cycle; previous game outcome; previous and upcoming opponent level) on weekly training load, pre-game recovery and PIR. Weekly load was higher in guards and medium minute-per-game (MPG) players, and lower for medium-experienced players, before facing high-level opponents, during later season phases and short recovery cycles (all p < 0.05). Pre-game recovery was lower in centers and high-experience players (p < 0.05). Game performance was better in high-MPG players (p < 0.05) and when facing low and medium-level opponents (p < 0.001). Interestingly, players performed better in games when the previous week's training load was low (p = 0.042). This study suggests that several individual characteristics and contextual factors need to be considered when monitoring training load (playing experience, playing position, playing time, recovery cycle, upcoming opponent level), recovery (playing experience, playing position) and game performance (opponent level, weekly training load, pre-game recovery) in basketball players during the competitive season.

Associations Between Two Athlete Monitoring Systems Used to Quantify External Training Loads in Basketball Players

Monitoring external training load (eTL) has become popular for team sport for managing fatigue, optimizing performance, and guiding return-to-play protocols. During indoor sports, eTL can be measured via inertial measurement units (IMU) or indoor positioning systems (IPS). Though each device provides unique information, the relationships between devices has not been examined. Therefore, the purpose of this study was to assess the association of eTL between an IMU and IPS used to monitor eTL in team sport. Retrospective analyses were performed on 13 elite male National Collegiate Athletic Association (NCAA) Division I basketball players (age: 20.2 ± 1.2 years, height: 201.1 ± 7.6 cm, mass: 96.8 ± 8.8 kg) from three practices during the off-season training phase. A one-way analysis of variance was used to test differences in eTL across practices. Pearson’s correlation examined the association between the Distance traveled during practice captured by IPS compared to PlayerLoad (PL), PlayerLoad per Minute (PL/Min), 2-Dimensional PlayerLoad (PL^2D), 1-Dimensional PlayerLoad Forward (PL^1D-FWD), Side (PL^1D-SIDE), and Up (PL^1D-UP) captured from the IMU. Regression analyses were performed to predict PL from Distance traveled. The eTL characteristics during Practice 1: PL = 420.4 ± 102.9, PL/min = 5.8 ± 1.4, Distance = 1645.9 ± 377.0 m; Practice 2: PL = 472.8 ± 109.5, PL/min = 5.1 ± 1.2, Distance = 1940.0 ± 436.3 m; Practice 3: PL = 295.1 ± 57.8, PL/min = 5.3 ± 1.0, Distance = 1198.2 ± 219.2 m. Significant (p ≤ 0.05) differences were observed in PL, PL^2D, PL^1D-FWD, PL^1D-SIDE, PL^1D-UP, and Distance across practices. Significant correlations (p ≤ 0.001) existed between Distance and PL parameters (Practice 1: r = 0.799–0.891; Practice 2: r = 0.819–0.972; and Practice 3: 0.761–0.891). Predictive models using Distance traveled accounted for 73.5–89.7% of the variance in PL. Significant relationships and predictive capacities exists between systems. Nonetheless, each system also appears to capture unique information that may still be useful to performance practitioners regarding the understanding of eTL.