Decision Support Systems for Time Series in Sport: Literature Review and Applied Example of Changepoint-Based Most Demanding Scenario Analysis in Basketball

Abstract

Decision Support Systems (DSSs) are increasingly shaping high-performance sport by translating complex time series data into actionable insights for coaches and practitioners. This paper outlines a structured, five-stage DSS development pipeline, grounded in the Schelling and Robertson framework, and demonstrates its application in professional basketball. Using changepoint analysis, we present a novel approach to dynamically quantify Most Demanding Scenarios (MDSs) using high-resolution optical tracking data in this context. Unlike fixed-window methods, this approach adapts scenario duration to real performance, improving the ecological validity and practical interpretation of MDS metrics for athlete profiling, benchmarking, and training prescription. The system is realized as an interactive web dashboard, providing intuitive visualizations and individualized feedback by integrating validated workload metrics with contextual game information. Practitioners can rapidly distinguish normative from outlier performance periods, guiding recovery and conditioning strategies, and more accurately replicating game demands in training. While illustrated in basketball, the pipeline and principles are broadly transferable, offering a replicable blueprint for integrating context-aware analytics and enhancing data-driven decision-making in elite sport.

1. Introduction

Decision-making in professional sports is high-stakes and increasingly complex, requiring coaches and supporting staff to optimize team and player outcomes under conditions of time pressure, uncertainty, and rapidly growing data volume [1,2]. Technological advances have greatly expanded the range of available information, from real-time data streams like GPS tracking and heart rate monitoring to periodic sources such as optical tracking, biomarker assessments, and wellness reports. These datasets span both quantitative measures (e.g., movement kinematics, force plate kinetics, biomarker levels) and qualitative assessments (e.g., athlete self-reports, coaching evaluations), reflecting the multifaceted nature of modern sport [1,3,4].

Because much of the relevant information in sport is dynamic and sequential, time series data have become integral to performance decision-making. Temporal patterns must be considered at both macro levels (e.g., seasonal training cycles, athlete careers) and micro levels within games (e.g., quarters, high-intensity intervals, tactical substitutions). As new time series data sources and monitoring modalities proliferate, practitioners face an escalating challenge: integrating and interpreting a diversity of evolving data streams, often under significant time constraints and with considerable variability in data quality and access [2]. This complexity is amplified by well-established characteristics of human decision-making: judgments are shaped by the volume and reliability of available information (sometimes insufficient, sometimes overwhelming or inaccessible), time pressure, and innate cognitive limitations such as processing capacity and bias [5]. These realities underscore how difficult it is for coaches and staff to keep pace with the expanding sources of information, while balancing immediate competitive pressures with long-term development goals.

Decision Support Systems (DSSs) offer a solution to these challenges by integrating diverse data streams, applying advanced analytics, and employing user-centered design to deliver automated, context-relevant outputs [2]. Effective DSS development also recognizes the important distinction between descriptive (explaining past patterns) and predictive (forecasting future states) modeling in sports analytics, as each requires distinct design priorities, validation strategies, and use-case considerations [6]. Every stage in the DSS pipeline, from initial signal acquisition and data cleaning to feature engineering, context-aware pattern recognition, and practitioner-focused output, presents unique challenges that must be thoughtfully addressed, drawing on both the current literature and practical experience.

To provide a generalizable and scalable approach, this paper builds on the foundational principles of the DSS development framework outlined by Schelling and Robertson (2020) [2]. We extend these principles through a detailed five-step process specifically designed for handling time series data in sport: (1) data collection, (2) data conditioning, (3) feature extraction and integration, (4) pattern recognition and forecasting, and (5) DSS output (see Figure 1). By providing targeted methodological guidance and sport-specific examples at each stage, this paper offers practical steps for developing and deploying DSS that harness the full potential of time series data in high-performance sport. For each step, we synthesize methodological considerations, review supporting literature and applications in sport, and demonstrate core concepts through a worked example centered on the analysis of Most Demanding Scenarios (MDSs) in basketball. MDS are brief passages where external-load metrics approach individual maxima, identified via rolling-peak windows (e.g., 30–300 s) and operationalized as values near a player’s maximum or within upper percentiles (e.g., top 10–20%) across variables like accelerations and high-speed distance [7]. This example illustrates how a complete DSS, spanning high-resolution optical tracking, changepoint-driven peak detection, and interactive dashboard output, can translate complex data into actionable recommendations for practitioners.

This paper reviews key considerations for leveraging time series data in sports performance applications and illustrates their practical use through the design of a DSS that integrates player tracking data. It emphasizes how time series methods can be applied to monitor, interpret, and forecast athlete and team performance, whether capturing real-time metrics (e.g., movement speeds, heart rate, workload during competition) or analyzing retrospective datasets (e.g., wellness reports, force-plate outputs). While the provided example employs player tracking data, the principles and framework described are broadly applicable to any sports performance context where continuous or sequential measurements inform decision-making. Specifically, we address the methodological and practical requirements of each stage:

• Data collection: Selecting appropriate sampling rates for diverse signal types to ensure high data quality and minimize system error.
• Data conditioning: Applying filtering techniques to manage noise, enhance signal quality, and prepare data for quantitative analysis.
• Feature extraction and integration: Combining multi-source continuous and discrete signals to provide a holistic, context-aware athlete profile.
• Pattern recognition and forecasting: Employing both classical statistical and machine learning tools to detect trends, anomalies, and predict future performance.
• DSS output: Translating analysis into actionable recommendations via practitioner-facing interfaces, supporting both immediate decision-making and longer-term strategy.

2. Data Collection

In developing a DSS for time series data, selecting appropriate sampling rates across integrated sources is a foundational design choice. The sampling rate directly sets the temporal resolution, affecting the system’s ability to detect meaningful patterns and rapid performance changes [8]. According to the Nyquist–Shannon theorem, this rate should be at least twice the highest frequency present, ensuring fast-changing events are accurately captured and preventing misleading aliasing artifacts in the analysis [9,10]. Careful adherence to these principles is essential for high-quality, reliable downstream metrics.

Table 1. Sampling rates and anti-aliasing considerations for various data sources in sports analytics. Values are general guidelines; optimal choices should be tailored to each specific application and measurement goal. GPS (global positioning system), LPS (local positioning system), IMU (inertial measurement unit), HR (heart rate), HRV (heart rate variability), ECG (electrocardiogram), RFD (rate of force development), RPE (rating of perceived exertion), Hz (hertz), LF/HF (low frequency/high frequency power), rMSSD (root mean square of successive differences).

Data Stream	Typical Sampling Rate	Rationale and Processing
GPS Position/Velocity	10 Hz (pro systems up to 15–20 Hz)	Enough to capture player movement changes (quick bursts, direction changes have frequency components ~1–5 Hz). Higher rates improve accuracy of distance/speed calculations. Often combined with IMU to interpolate between GPS data points [11].
Accelerometer	100–1000 Hz	Captures impacts and high-frequency vibration. High-rate raw data usually filtered (e.g., 50 Hz low-pass) and downsampled for analysis. Key for detecting collisions, jumps, and stride dynamics [12].
Heart Rate (processed)	1 Hz	Sufficient for tracking gradual HR changes during play. Underlying ECG/pulse sampled at higher rate (250+ Hz) to derive accurate R-R intervals and HRV metrics, and then condensed to 1 Hz or per-beat values [13].
Heart Rate Variability (HRV)	1000 Hz (ECG or pulse) during test, results reported as 5 min averages or spectral measures	Short-term HRV requires high-fidelity capture of each heartbeat interval (ms precision). Data is then summarized into frequency-domain (LF/HF power) or time-domain (rMSSD) features for decision-making [13].
Force Plate (jump/landing)	500–1000 Hz	Needed to capture peak forces and rate of force development (which can have significant content > 100 Hz). Data often averaged or key metrics extracted (peak force, impulse) to integrate with other lower-rate data [14].
Video (optical tracking)	25–50 Hz (standard video); 120–240 Hz (high-speed for technique analysis)	Standard video at ~30 fps is used for tactical review (human vision can interpret that rate). Specialized high-speed cameras at 120+ fps are used for detailed biomechanical analysis (e.g., pitching mechanics) to avoid motion blur and aliasing of fast limb movements [15].
Player Self-Report Metrics (RPE, wellness)	1 per day (questionnaire) or 1 per session	Subjective scores change slowly; sampling more frequently adds noise. Ensure sampling is consistent (e.g., every morning) to track trends. Variations are meaningful across days/weeks, not minutes [16].

summarizes sport-specific sampling rate guidelines and anti-aliasing considerations.

Cyclical or rhythmic signals (such as circadian rhythms) require particular care in sampling strategy. Sampling precisely at the cycle frequency (for instance, exactly every 24 h) can mask meaningful intra-cycle variations (a specialized aliasing problem). Increasing sampling frequency slightly or staggering collection times can help reveal within-cycle variability. Practitioners must balance the value of baseline consistency with the need to detect transient dynamics; in practice, this may involve combining regular fixed-time measurements with strategically timed additional samples.

While high-resolution data collection can be valuable, oversampling beyond what is necessary increases processing and storage demands without yielding further insight. For example, Bardella et al. (2016) found that sampling weight-lifting velocity above ~25 Hz introduced no additional accuracy for certain resistance exercises, underscoring that more data do not always mean better results [17]. As a practical approach to anti-aliasing, high-frequency raw data (e.g., 100 Hz from an accelerometer) are filtered, either in hardware or software, to remove frequencies above the Nyquist limit prior to analysis [18] (see also Section 2).

Aligning sampling rates with each signal’s physiological or mechanical characteristics optimizes data quality and reduces system error, as emphasized by Schelling and Robertson (2020) in the DSS framework [2] (see Figure 1). This approach ensures that downstream processing and calculations, such as velocity, acceleration, or workload estimation, are built on a solid data foundation, ultimately enabling robust, context-aware decision-making.

To illustrate practical data collection, we present a worked example: MDS analysis in basketball within a DSS context [19,20]. MDS identifies peak periods of physical demand during games, using rolling averages of metrics such as distance covered, speed, or accelerations over defined time windows (typically 30 s to 5 min) [7]. Unlike traditional averages, this approach pinpoints the most intense passages of play, which is critical for tailoring training loads and accurately reflecting in-game workload.

In this application, optical tracking is performed using a high-definition camera array, capturing the full basketball court at 25 frames per second (25 Hz). The system localizes all players and the ball in two and three dimensions using synchronized, multi-angle feeds, generating time-stamped positional coordinates for each frame [21]. These are processed to quantify peak physical demands within each player’s on-court stint, with changepoint analysis used to identify periods of maximal activity [22,23]. Analyses used historical, de-identified tracking data under standard cybersecurity policies and HIPAA-aligned safeguards in biomedical research [24] (ethics approval: Victoria University HRE 20–204). Reproducibility of our methodology is supported by a fully specified acquisition-to-output workflow; applying the same thought process to equivalent licensed tracking data with standard libraries (e.g., Python 3.12.5 package ruptures) should yield comparable results, although source code and raw data are contract-restricted.

Because MDS metrics rely on rolling averages or sums of time series variables [7,19,25] (e.g., mean velocity over 12-s windows), sampling beyond a certain frequency is not necessary to preserve analytical fidelity [26]. Here, data are downsampled to 5 Hz, preserving the accuracy of smoothed velocity estimates (R² = 0.9998) while dramatically improving computation efficiency. Velocity is computed from mean displacement over a 0.4-s window (±0.2 s, or ±5 frames at 25 Hz), yielding nearly identical results whether starting from 25 Hz or 5 Hz data. This downsampling reduces computation time by approximately a factor of five for velocity estimation (840 ms vs. 167 ms per player) and more than threefold for metres-per-12-s calculations (408 µs vs. 111 µs per player), with no detectable loss of accuracy. Such efficiency is essential for real-time or near-real-time DSS processing, enabling the timely generation of actionable, high-demand scenario insights.

3. Data Conditioning

Signal filtering is an essential step in sports data pipelines, critical for distinguishing true physiological events, such as peak acceleration or rate of force development (RFD), from background variation and sensor noise. For a DSS, trustworthy filtering underpins user confidence, ensuring that performance metrics accurately reflect real changes and not artifacts [27]. Filtering also strengthens validation and reproducibility by enabling outputs to be cross-checked against reference standards, such as aligning filtered force plate data with high-speed motion capture or a validated accelerometer. However, as Ellens et al. (2024) demonstrate, inappropriate filter settings can disregard genuine acceleration events in Global Navigation Satellite System (GNSS) data, reducing the reliability of DSS outputs [28].

No single filtering method is optimal for every sports data stream; techniques differ in mathematical foundation, computational cost, and their ability to preserve sharp events or suppress noise. The choice, among Butterworth [27], moving average [27], exponentially weighted moving average (EWMA) [29], Kalman [30,31], and others, should reflect both the signal’s properties and the desired balance between noise reduction and retention of important features [27].

Table 2. Summary of common filtering methods used in sport performance. For detailed explanations and practical guidelines on signal filtering techniques, refer to Crenna et al. (2021) [27], Winter (2009) [32], and Smith (1997) [33].

Type	Main Use Case	Key Advantage	Limitation
Butterworth [27]	Biomechanics, force plates, GPS	Smooth, no ripple, easy to tune	Requires cutoff tuning
Moving Average [27]	Simple smoothing	Easy to use	Delays signal, poor at sharp events
Exponentially Weighted Moving Average [29]	Wellness/load trends	Light, fast, responsive	Not suited for rapid changes
Kalman [30,31]	Sensor fusion, tracking (GPS + IMU)	Real-time estimation, adaptive	Requires models, tuning
Savitzky–Golay [27]	Shape preservation (e.g., RFD)	Maintains peaks, allows derivatives	Complex tuning
Median [32]	Spike removal	Good for artifact cleaning	Distorts smooth signals
Gaussian [33]	General smoothing	Natural, bell-shaped weighting	Computationally heavier
Wavelet [34]	Non-stationary signals (gait, jumps)	Multi-scale, time-frequency detail	Complex requires expertise
Chebyshev/Elliptic [33]	Sharp cutoff needs	High selectivity	Ripple, more complex
Lowess/Loess [35]	Exploratory, nonlinear trendlines	Handles irregular data well	Slow, not real-time
Bandpass/Notch [36]	Isolating or removing known frequencies	Targeted frequency suppression	Narrow application

summarizes common filtering approaches and their main applications in sport.

Robust filtering and sampling are prerequisites for effective feature extraction in time series analysis. Downsampling without pre-filtering introduces aliasing (misrepresentation of high-frequency content) [8]. Retrospective analyses often use zero-lag filters to preserve event timing, while real-time DSS depends on causal filters that may introduce minor signal lag. Excessive filtering may erode genuine performance spikes, whereas insufficient filtering can amplify noise. Transparent, detailed reporting of all filtering protocols is vital for reproducibility [28], especially as commercial device pipelines are frequently undocumented, sometimes leading to inconsistencies when combining raw and manufacturer-smoothed data [26].

When calculating rates of change, the selection of the time window is also crucial. Short windows, such as 50 ms for early RFD [37] capture rapid phenomena but amplify noise and sampling limitations [28]; longer ones (e.g., 200 ms for RFD or three weeks for workload ratios) smooth variability, improving reliability but potentially masking brief, significant events [38,39,40,41]. The most appropriate window depends on the dynamics of the measured process, required sensitivity, and prevailing signal-to-noise ratio. Implausibly high rates often signal suboptimal filters or window choices, reinforcing the need for careful selection and rigorous filtering, especially during downsampling.

Within Schelling and Robertson’s (2020) DSS framework, filtering is fundamental to achieving data quality and process efficiency (see Figure 1). Effective filtering minimizes wasted computational effort, improves output reliability, builds practitioner trust, and ensures that all data sources are prepared consistently for advanced analytics and integration.

In our practical basketball DSS application, robust data conditioning was essential to ensure reliability and actionability in the velocity and acceleration metrics that drive MDS assessment. As shown in Figure 2, raw velocity time series data were smoothed using a 12-s moving average, a window chosen based on the typical possession durations in professional basketball (i.e., mean chance length in a professional basketball game [42,43,44]). This approach facilitates the detection of meaningful peak demands, though ongoing debate exists regarding optimal window length [40].

To further enhance transparency and reproducibility, our methodology explicitly computes velocity as the scaled mean displacement over a 0.4-s window (±0.2 s from the frame of interest) [40] (see Formula (1)), effectively suppressing high-frequency noise while maintaining responsiveness, even in contrast to the proprietary, often undocumented filtering pipelines used by some commercial vendors. Figure 3 compares velocity time series computed using different smoothing intervals, demonstrating the impact of methodological choices on outcome sensitivity.

$$v_t=5\times {\displaystyle \frac{\sqrt{{\left(x_{t-0.2}-x_t\right)}^2+{\left(y_{t-0.2}-y_t\right)}^2}+\sqrt{{\left(x_t-x_{t+0.2}\right)}^2+{\left(y_t-y_{t+0.2}\right)}^2}}{2}}$$

(1)

Acceleration is derived as the difference between consecutive velocity samples, and acceleration load is calculated as a rolling sum of absolute accelerations over each 12-s interval, following established performance analysis practices [45]. Aggregating over larger temporal windows, coupled with transparent reporting of all filter and smoothing steps, minimizes vendor-driven variability and enhances reproducibility, providing a robust foundation for MDS benchmarking. This highlights the critical link between filtering decisions, feature extraction, and the practical utility of time series analytics in DSS-based sport performance decision-making.

4. Feature Extraction and Integration

Integrating multi-source data into a cohesive DSS in sport presents practical, computational, and methodological challenges [46]. Performance datasets commonly include both continuous signals (such as GPS trajectories, accelerometer outputs, and physiological metrics) and discrete events (such as substitutions, fouls, injuries, or context-specific actions). An effective combination of these heterogeneous streams requires robust synchronization, alignment, and data fusion methodologies, beyond just optimizing sampling rates and filtering techniques.

Accurate synchronization of data streams is fundamental for meaningful integration [47]. This ensures, for example, that heart rate and HRV can be interpreted alongside session workloads in endurance sports [48], or that force plate outputs are precisely linked to movement phases in strength training [49]. Similarly, aligning wellness questionnaires and biomarker data enables early detection of recovery issues before they manifest as performance declines [50]. Synchronization challenges arise from differing sensor sampling rates, clock drift, data-packet delays, and risks of aliasing, especially when downsampling high-frequency metrics to match lower-frequency streams [30,51], as discussed in Section 1 and Section 2. Solutions include precise time-stamping at data capture (e.g., GPS time, unified system clocks) and the use of interpolation or resampling. For example, high-frequency accelerometer data may be summarized into 1-s intervals, while lower-frequency measures like wellness scores are interpolated or carried forward [52]. These strategies ensure that physiological or biomechanical events (e.g., heart rate peaks) align accurately with gameplay or tactical episodes [53]. Advanced methods, such as event-driven synchronization using accelerometer-detected collisions or detailed play-by-play markers, can further improve multimodal data alignment [54].

Beyond synchronization, integrating continuous signals (such as player position, heart rate, or IMU data) with discrete contextual information (substitutions, environmental conditions, coaching decisions) requires specialized data fusion approaches. Hybrid feature sets, developed by tagging continuous streams with discrete event labels (such as mapping GPS data to offensive vs. defensive phases), enrich context-driven analysis. Multi-level sensor fusion algorithms, including Kalman and Bayesian filters, are capable of merging information from different abstraction levels, from raw signals to features and final decisions. This enables robust athlete state estimation, even in the presence of noise or missing data [30].

Multi-source integration in real-time environments introduces further challenges, including computational overhead, network latency, and the need for precise sensor synchronization. Efficient integration leverages advanced algorithms and edge computing, with data processed locally on wearable devices or field-side workstations to minimize delay. System load can be reduced by downsampling to the minimum required rate (see Section 1 and Section 2) and using event-driven processing that triggers computation only when necessary. When integrating data from diverse stakeholders (medical staff, analysts, coaches, box score providers), it is crucial to use standardized formats, consistent units, and robust data-sharing protocols. Well-designed Application Programming Interfaces (APIs) are essential for secure, automated data exchange and ensuring seamless interoperability across departments and platforms [2,55].

Effective multi-source integration directly improves context satisfaction by leveraging all available contextual information and process efficiency, delivering timely, reliable insights within the DSS [2] (see Figure 1). When implemented holistically, this approach creates a comprehensive athlete profile, reduces conflicting recommendations, and builds practitioner trust by ensuring decision-making reflects the full spectrum of relevant information rather than isolated metrics.

In our MDS application, seamless integration of multi-modal datasets is achieved through unified timestamps and universally unique identifiers (UUIDs) for players and games, facilitating precise alignment of physical tracking data with event logs, player designation, and contextual game variables. Player tracking (25 Hz) and play-by-play (25 Hz) data streams, delivered with consistent frame numbers, game-clock values (milliseconds), and ISO 8601-formatted UTC timestamps, support accurate synchronization across modalities. While scoring data are often aggregated at the game or quarter level, detailed play-by-play shot records enable the construction of continuous scoring time series, such as score margin (downsampled to 5 Hz) and minute-by-minute differentials, all retaining consistent frame numbers and timestamps from the original datasets.

This integration framework enables precise overlay of physical demands (e.g., total distance, velocity, or acceleration load) with play-by-play actions, tactical assignments, and contextual variables, including quarter, score margin, offense/defense status, and possession. Supplementary inputs, such as video review, injury or substitution markers, and post-game RPE or wellness assessments, further enrich the contextual understanding of each MDS episode. By synchronizing and mapping all relevant information to a unified temporal reference, the DSS can characterize high-demand periods not only by physical load but also in relation to tactical, contextual, and athlete state variables, supporting truly comprehensive and context-aware decision-making.

5. Pattern Recognition and Forecasting

Robust time series methods are essential for enabling decision-makers to detect trends, identify anomalies, and forecast future outcomes with confidence [56]. Classical approaches remain foundational and highly interpretable for pattern detection and performance forecasting. Frequency-domain techniques like Fourier analysis decompose signals into separate frequency components, making them effective for identifying recurring patterns, such as weekly workload cycles or seasonal fluctuations [27,28]. Wavelet analysis complements this by revealing localized, time-varying frequency changes, which are particularly valuable for assessing non-stationary biomechanical signals, such as shifts in force output during fatigue or altered gait patterns. For example, wavelet-based techniques have successfully detected subtle changes in athletes with chronic ankle instability [29,30].

Time-domain models also play a key role. ARIMA (Auto-Regressive Integrated Moving Average) models forecast future values by capturing trends and autocorrelations after ensuring data stationarity, providing clear interpretability through slope and seasonal pattern estimates. Bayesian changepoint detection extends these methods by statistically identifying abrupt shifts in measured metrics [57], such as sudden drops in running velocity that may indicate fatigue, injury onset, or tactical adjustments. While these classical tools are generally linear and assume consistent data structure, making them efficient and easy to use, especially with smaller datasets or in coach-facing outputs, they often struggle with the complex, nonlinear interactions and sudden regime changes that characterize real-world sports data, limiting their predictive power in more dynamic contexts [58,59].

Modern machine learning (ML) models excel at capturing the nonlinear and complex relationships found in sports data, where numerous interdependent factors, such as training load, injury history, and tactical context, can interact unpredictably [59]. Deep learning architectures like Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, are well-suited for modeling temporal dependencies in sequential performance data. LSTMs have been applied to predict injuries [60] and performance outcomes [61] from training load histories, learning intricate autoregressive patterns beyond the reach of linear models (though requiring large datasets and careful tuning) [62]. Transformers, which use self-attention mechanisms, have recently demonstrated promise in time series forecasting by learning long-range dependencies [63,64]; for instance, they can relate historical workload patterns and recent recovery trends to current injury risk [65]. Temporal Convolutional Networks (TCNs) also efficiently capture extended temporal dependencies, often with reduced computational complexity compared to RNNs [66].

Unsupervised models, such as autoencoders, including sequence and variational autoencoders, automatically learn lower-dimensional latent representations of time series data [67], simplifying multi-dimensional performance histories and facilitating anomaly detection when current behavior deviates from established patterns. In team sport analytics, Graph Neural Networks (GNNs) have been introduced to model player interactions, enabling the analysis of relational dynamics, such as passing structures or synchronized group movements, that go beyond what sequential models can capture [68].

Despite the promise of advanced ML techniques, current evidence indicates that a hybrid analytical approach is often most effective [69]. In many cases, simple models, such as logistic regression or ARIMA, perform as well as, or better than, complex ML algorithms for tasks like injury prediction, chiefly because they offer greater interpretability, transparency, and lower risk of overfitting [56]. For robust decision support, practitioners are encouraged to use traditional statistical models to establish baseline forecasts and to layer more sophisticated ML approaches for detecting nuanced anomalies or modeling nonlinear risk interactions [56].

To illustrate the value of combining multiple analytical techniques, Figure 4 presents a suite of time series analyses applied to daily player load data. This example demonstrates how raw fluctuations, smoothing methods, periodicity detection, localized frequency analysis, probabilistic forecasting, and structural change identification combine to provide a comprehensive picture of both short- and long-term training load dynamics.

Regardless of the analytical approach, all forecasting, trend analysis, and anomaly detection tools must be contextualized within the specific athlete and team environment. To maintain output quality and practitioner trust, models should be regularly validated, recalibrated, and audited for ongoing relevance. Outputs, such as team win probabilities or player readiness metrics, should include confidence intervals, actionable thresholds, and transparent explanations of key drivers to enhance decisional guidance and on-field usability. Incorporating adaptive modeling frameworks that allow the DSS to update dynamically with new data and practitioner feedback further supports ongoing context satisfaction [2].

In our MDS application, pattern detection and forecasting are achieved by segmenting each player’s time series using changepoint detection. The example DSS employs the Python library ruptures to identify shifts [22] in physical output, such as changes in acceleration load or velocity, within individual player stints. Each stint is partitioned into segments of at least 12 s, aligning with the moving average window defined in Section 2. Practitioners can tailor the segmentation process by specifying the number of segments or by selecting a penalty function, which acts as a regularization mechanism to avoid overfitting by assigning a cost to each additional changepoint. For our analysis, a fixed penalty is used to ensure consistent fitting across all scenarios, enabling fair comparisons between player stints.

Figure 5 illustrates changepoint detection in a single basketball stint, showing how the method divides the stint into shorter segments for clearer interpretation and performance review. These segments are summarized to identify the Most Demanding Scenario within each stint, defined as the period where the weighted average of a player’s total acceleration load and velocity is maximized. Unlike studies limited to fixed-duration windows, changepoint detection produces scenarios of varying duration, allowing a more nuanced depiction of high-demand periods in team sports [70].

To aid interpretation, unsupervised machine learning techniques are used to cluster changepoint-based segments by duration and key performance metrics, such as total acceleration load (TAL) and very high-speed distance (VHSD, defined as distance covered above 18 km/h) [71]. Gaussian Mixture Models (GMMs) are used for clustering, since their probabilistic assignments more accurately represent segments with mixed characteristics [72]. Descriptive labels are then added to each cluster to enhance interpretability and facilitate targeted queries for further analysis. This approach enables practitioners to identify MDS segments with distinct profiles, such as those characterized by high-intensity, mechanical loads or those dominated by elevated VHSD, thus supporting more granular and actionable insights.

6. Decision Support System Output

To be effective, a DSS output must demonstrate context satisfaction, output quality, and process efficiency [2] (see Figure 1). Context satisfaction is achieved when insights are tailored to the practical, evolving needs of practitioners. For example, an Australian football DSS might recommend substitutions by factoring in player fatigue, fouls, opponent matchups, and the current game situation, giving coaches a data-driven means to optimize lineups during key moments [73]. In soccer, systems that integrate player workload, opposition style, and weather can generate individualized tapering or hydration recommendations, offering acute and strategic guidance [74].

Output quality hinges on accuracy, interpretability, and transparency. For instance, an American football DSS might provide play-choice probabilities as risk-adjusted, data-driven visualizations with confidence intervals and supporting rationale, helping coaches make informed decisions [75]. In elite cycling, readiness dashboards that merge force-velocity profiling and HRV data can flag under-recovery with clear probability and uncertainty indicators, enabling precise adaptation of training and competition plans [76].

Process efficiency demands insights that are timely and actionable, delivered in formats that enhance workflow. Modern DSS platforms leverage real-time dashboards, customizable reports, and alerting, such as live-streamed pitching analytics in baseball for immediate intervention [77]. Integrating hydration sensors and daily wellness questionnaires can flag elevated risk for dehydration or overtraining in time for preventive support [78]. In winter sports, merging power output with environmental data enables real-time adjustments to pacing and equipment as conditions change [79]. Tennis DSS now offers instant, data-backed tactical briefings between games, synthesizing tracking and physiology with opponent patterning [80].

Collectively, these examples underscore the practical value and momentum of sport DSS. The next step is a systematic integration of interactive tools into daily workflows, fully embedding actionable, context-sensitive recommendations when and where they are most needed. The example presented in this manuscript corresponds to a male professional basketball player, with data collected during an official in-game situation using an optical tracking system. Although only one player is shown here for illustrative purposes, this approach has already been applied across thousands of players and matches in professional basketball. Therefore, the present example should not be understood as a representative sample but rather as a practical demonstration of the procedure.

A practical deployment of our DSS for MDS analysis is embodied in a browser-based dashboard built as a single-page application using Vue.js for a responsive user interface and D3.js for dynamic, customizable visualizations. Data are securely served via a cloud-hosted REST API, enabling filtered, up-to-date access without needing local software. The interface supports focused queries by game date, team, player, quarter, and stint, with quick toggles for key physical metrics, MDS outcomes, and benchmarking, helping users rapidly isolate relevant workloads and contextual features (see Figure 6).

The dashboard’s central “Physical Time Series” panel displays TAL and distance covered by a player throughout an entire game, clearly identifying each stint. It also highlights detected MDS (shaded regions), while panel headers summarize total playing time, and the average load (TAL) and distance (m/12 s) for the highest MDS. Directly below, a score differential time series situates player load within the broader narrative of team performance. The contextual event raster aligns key technical-tactical actions, including 3-point attempts, steals, rebounds, blocks, and fouls, so users can connect workload peaks to game moments. Bottom panels compare stint durations and intensities (TAL/min, m/12 s) against historical player benchmarks using density and scatter plots, facilitating rapid identification of outliers and normative trends. The statistical summary table enables immediate lookup and export of all key calculations, supporting post-game review and ongoing athlete monitoring.

Throughout, the dashboard interface is designed for maximum clarity and utility, employing a minimal and uncluttered layout, with contextual event highlighting and interactive features like zoom and tooltips for detailed exploration. The modular sidebar allows efficient selection and filtering by team, player, period, and physical metric, supporting both targeted and holistic analysis. This architecture supports accessible, team-wide review while streamlining post-game workflow, effectively bridging complex time series insights with actionable, individualized feedback for training design and periodization. By translating advanced analytics into clear, context-sensitive decisions, the dashboard functions as a robust evidence-based tool for modern sport practitioners.

7. Limitations of the Study

These limitations pertain to the worked example, whose primary purpose was to demonstrate the application of the proposed time series DSS concepts rather than to produce definitive, generalizable estimates. The case focuses on a single sport and organization, which enhances ecological validity but limits external generalization across leagues and contexts. Despite efficiency gains (e.g., downsampling), ingesting high-frequency tracking data, performing changepoint segmentation, and clustering segments remain computationally demanding for near-real-time use without suitable infrastructure. Findings are also sensitive to design choices (e.g., changepoint penalty and minimum segment length; variable selection and weighting in the MDS score), and formal sensitivity analyses were beyond scope. Effective deployment requires practitioner training to interpret segmented stints and MDS outputs. Contractual restrictions on raw tracking data and code constrain open replication, although methods and parameters are fully specified. Future work should extend validation to other sports and organizations, conduct robustness and longitudinal impact studies, optimize pipelines for live workflows, and enhance explainability and usability while releasing anonymized feature tables and configuration files where permissible.

8. Conclusions

This study advances time series-driven DSS by refining a five-stage pipeline that links rigorous data handling to context-aware pattern detection and practitioner-oriented delivery. A novel application of changepoint analysis to MDS assessment adapts scenario duration to the data rather than imposing fixed windows, improving ecological validity and yielding actionable insights for post-game review and individualized training.

For practitioners, the main message is pragmatic: a dashboarded, changepoint-based MDS workflow can translate complex tracking streams into timely, interpretable guidance that aligns with staff workflows.

For researchers, the central contribution is conceptual and methodological: a sport-agnostic pipeline and rationale for changepoint-based MDS, including explicit parameterization and reproducibility notes, that can be tested across settings and linked to performance and health outcomes.

Together, the framework and the basketball-MDS DSS provide a replicable blueprint for embedding adaptive, transparent time series analytics into elite-sport decision making.