Journal of Physical Education and Sport ® (JPES), Vol. 24 (issue 8), Art 214, pp. 1927 - 1932, August 2024
online ISSN: 2247 - 806X; p-ISSN: 2247 – 8051; ISSN - L = 2247 - 8051 © JPES

Original Article
Athlete performance prediction using intelligent reporting and regression model
analysis: A generative approach for training planning.
MOHAMED REBBOUJ1, SAID LOTFI2
1,2,
Multidisciplinary Laboratory In Education Sciences and Training Engeneering (LMSEIF). Sport Science
Assessment and Physical Activity Didactic. Normal Higher School (ENS-C), Hassan II University ofCasablanca, MOROCCO.
Published online: August 31, 2024
Accepted for publication : August 15, 2024
DOI:10.7752/jpes.2024.08214
Abstract:
This study explores the use of reported data and predictive analysis as a long-term generative approach forathlete training planning. The data collected from 607 higher education students (mean age = 16.86; Std = 1.22),
includes measurements from physical tests and activity records. The dataset comprises 29 variables, which were
profiled and feature-engineered to enhance predictive accuracy for training procedures. We leveraged Microsoft
Azure Machine Learning to determine the significance of features on outcomes and utilized Power BI tovisualize the impact of aggregate features on running distance. Initial findings indicates that the optimal age
range for focusing training efforts is between 16 and 17 years old. This result is supported by a Spearman
correlation coefficient of 0.42, stipulating a moderate positive relationship between the age group and thepredicted performance outcomes based on key aggregate features. In particular, four key features significantly
impact performance, while other variables have a minor influence. The study highlights the significance of theseaggregate features in predicting training success. In conclusion, the study underscores the importance of a robust
reporting process and the use of predictive analysis in developing training programs. It identifies four criticalfeatures that have a substantial impact on realized performance. While these four features are paramount, thestudy also acknowledges that other variables, though less influential, may still affect the outcomes if considered.
This comprehensive approach to data collection and analysis provides a solid foundation for optimizing athlete
training programs, ensuring that training efforts are both targeted and effective. The findings offer valuable
insights for coaches and sports scientists aiming to enhance athletic performance through data-driven training
strategies.
Keywords: Performance Optimization, Sports analytics, Data-Driven Training.
Introduction
Analysing the sport performance is a prevalent practice in the sport industry, where clubs and market
bettors employ statistical methods and advanced predictive analysis to define the game or championship winner.
Consequently, numerous models have been developed to enhance the prediction of a sport event outcomes(Hubáček et al., 2019; Maszczyk et al., 2014; McCabe & Trevathan, 2008; Zhang et al., 2022). These models
rely on data from athletes, teams, or from external sources as betting odds, prognostics, and audience feedback.
The collected information must be analysed in short time to facilitate decision-making through reporting,analysing, and predicting solutions.
Intelligent data analysis, a technology-driven process to derive a method, process, or action plan fromdata (Berthold & Hand, 2007). Is commonly used in reporting for decision-making process in various industry
fields. To revolutionize the industry, a digital transformation is required for real-time reporting and prompt
decision-making (Panchal et al., 2024). In the health sector Business Intelligence (BI) is used to obtain real-timedata for visualization and decision-making toward medication trends (Frestel et al., 2023). Meanwhile inmarketing, data analysis process generates reports to better position the product by segmenting customer choices.
Business applications and software are often cloud-based solutions; hence, industries choose cloud solutions to
pay the services they need when they need them. Microsoft Azure Machine Learning offers a more specificoverview by providing data storage, computing power and predictive algorithms for industry-specific purpose,such as predicting next quarter’s unit sales or a building’s energy consumption (Shapi et al., 2021) . The healthsector benefits significantly from this solution, using it to manage data about sick children in hospitals andfacilitate the use of electronic health records (Guo et al., 2023). Additionally, the sports sector and relatedsciences use Azure Kinect for motion tracking and biomechanical analysis (Brambilla et al., 2023), and even tomeasure spatiotemporal parameters during walking (Guess et al., 2022).
Despite the extensive application of statistical methods and predictive analysis within the sports
industry, a significant challenge persists in integrating diverse data sources and technologies to improve realtime reporting, predictive accuracy, and decision-making efficiency (Szymanski, 2020). The rapid analysis of----------------------------------------------------------------------------------------------------------------------------------1927
Corresponding Author: MOHAMED REBBOUJ, E-mail: Mohamed.rebbouj@enscasa.ma

MOHAMED REBBOUJ, SAID LOTFI--------------------------------------------------------------------------------------------------------------------------------------data from athletes, teams, and external sources is crucial for determining game or championship outcomes.
However, existing models frequently fail to deliver comprehensive, real-time insights essential for effective
decision-making.
To effectively develop specific activities and target the necessary physical capacities to achieve desired
performance outcomes (Eratlı Şirin & Şahin, 2020), it is essential to select appropriate candidates for eachactivity. Furthermore, tailored training plans for distinct groups of athletes enable a focused approach to various
training methods, categorized by athlete group, age, or activity (Anderson & Hargreaves, 2016).
Our research seeks to address this challenge by leveraging advanced cloud-based solutions, specifically
Microsoft Azure Machine Learning and Power BI, to develop an integrated framework for sports performance
analysis. We utilize Azure’s capabilities for data storage, computational power, and predictive algorithms toanalyze physical performance data from athletes. Power BI is employed for dynamic reporting and visualizationof the impact of aggregate features on performance metrics. This approach will facilitate the development of agenerative solution for deferred decision-making, enabling coaches and physical education teachers to makeinformed decisions promptly based on real-time insights (Lath et al., 2021). By integrating these technologies,
we aim to revolutionize the sports industry through a digital transformation that enhances training planning andperformance prediction.
Materials and method
Data Collection
Data pertaining to 607 higher education students was meticulously collected through physical and sports
tests during the academic year of 2022-2023. Subsequently, this data was classified into categorical andnumerical forms to profile the dataset prior to conducting the analysis, as illustrated in the subsequent table.

Table 1. Dataset profile of 33 columns and 607 rows.
The table provides a statistical summary of various features related to athlete performance, including
sex, age, weight, height, and specific fitness test scores. Key points include the mean, standard deviation,
variance, skewness, and kurtosis for each feature. The data reveals moderate variability in weight and height,
significant variability in fitness test scores, and generally symmetrical distributions for most features. These
insights are crucial for tailoring training programs and understanding athlete capabilities.
Statistical analysis
The dataset was imported into Power BI as a CSV file, and an automated report was generated to visualize thesignificant and correlated attributes. Simultaneously, the dataset was loaded into Azure ML to train a regression
model, which helped identify the key features influencing sports performance (Rebbouj & Said, 2022). The datatransformation process included steps such as preprocessing, feature engineering, application of scaling
techniques, and the use of various algorithms to construct the model.

Figure 1. Data transformation process in Azure ML
1928---------------------------------------------------------------------------------------------------------------------------JPES ® www.efsupit.roMOHAMED REBBOUJ, SAID LOTFI--------------------------------------------------------------------------------------------------------------------------------------The flowchart provides a structured approach to determining the appropriate analytical method based on
the nature of the data source. It helps in selecting the right analysis technique, whether the data is categorical,
numeric, or categorizable, thereby facilitating more accurate and efficient data analysis. This structured
categorization is crucial for ensuring that the chosen analytical methods align with the data characteristics,
ultimately enhancing the quality of insights derived from the analysis.
Results
The automated analysis conducted via Power BI underscored the significance of the heart rate (FC)feature, particularly in its correlation with the Body Mass Index (IMC) and the strength of the lower limb
muscles (FMMI). In contrast, the aggregate feature importance derived from Azure ML identified the following
variables as significant: weight (poids), the distance covered in 12 minutes (D-12min), the strength of the lower
limb muscles (FMMI), and height (taille).

Figure 2. Aggregate feature importance for all data.
The graph helps identify which features are most critical for predicting outcomes, allowing for targeted
improvements in training and performance strategies. By focusing on the most important features, such as weightand endurance, coaches and analysts can optimize training programs to enhance athlete performance.
The initial overview of the feature set will be employed within Power BI to enhance data visualization,encompassing the significance of each feature that has been empirically demonstrated to influence performanceoutcomes. Consequently, the generated visualizations depict a correlation between the Fat Mass and Muscle
Index (FMMI), Weight, Height, and the performance achieved in a 12-minute distance run. This correlation
provides valuable insights into the impact of these health metrics on physical performance.

Figure 3 & 4. Impact of FMMI and the weight / Height on the Distance run in 12 min.
From the analysis of the two figures, it is inferred that a default decrease in muscle strength, as
quantified by the Fat-Free Mass Index (FFMI), detrimentally influences the performance in achieving the
maximum distance in the 12-minute Cooper test. This observation remains valid regardless of whether theathlete possesses the ideal weight or height required for the task. Importantly, age is considered a significant
variable due to its substantial impact, as depicted in the subsequent figure. This consideration underscores themultifaceted nature of athletic performance, where factors such as muscle strength, body composition, and age
collectively contribute to the outcome.
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Figure 5. Predicted vs true chart of the regression model in azure ML.
In the context of training planning, the age bracket of 15.5 to 17 years is identified as the optimal
range(Matušica & Peráček, 2023). This conclusion is drawn from the alignment of predicted and actual values as
illustrated in the preceding chart. The model’s metrics reveal a Spearman correlation of 0.42, signifying asubstantial monotonic relationship. The model, as detailed in the data transformation process, employs a Voting
Ensemble Algorithm. This approach underscores the importance of age in training planning and the effectivenessof ensemble methods in predictive modelling.
This graph is crucial for evaluating the accuracy of the predictive model. The closer the ‘Average
Predicted Value’ line is to the ‘Ideal’ line, the more accurate the model. The bar chart provides additional
context by showing the distribution of true values, helping to identify any patterns or biases in the model’s
predictions. Overall, this visualization aids in understanding the model’s performance and highlights areas where
improvements may be needed to enhance predictive accuracy.
Discussion
Upon the comprehensive analysis of data procured through Power BI and Microsoft Azure Machine
Learning, tools we regard as deferred decision-making solutions over an extended period, we strategicallyreduced the sample size of student athletes from 607 to 416. This reduction, while significant, still ensures arobust participant pool for the validation of data post-training regimen completion. The selected 416 students
have committed to undertaking the Cooper test, a 12-minute run, twice weekly. This commitment not only
demonstrates their dedication(Ono et al., 2022) but also enriches our dataset. The inclusion of date and time
variables in the dataset is a strategic move aimed at enhancing forecasting capabilities. This additional layer ofdata allows us to track progress over time and predict future performance trends, thereby refining the predictive
accuracy of our model. This approach underscores the importance of comprehensive data collection and theeffectiveness of machine learning tools in predictive modeling within the realm of athletic training.
The analysis of the data revealed several key insights. The bar graph titled “Aggregate Feature
Importance” highlighted that “Poids” (Weight) is the most influential factor in the predictive model, followed by“D-12Min” (likely a 12-minute run test), “Force musculaire membre inf” (lower limb muscle strength), and“Taille” (Height). This information is crucial for tailoring training programs to enhance athlete
performance(Tyshchenko et al., 2024).
Additionally, the “Predicted vs. True” graph provided a visual comparison between predicted values
and actual true values. The ‘Ideal’ line represents a perfect prediction scenario, while the ‘Average Predicted
Value’ line shows the actual performance of the predictive model. The slight fluctuations of the ‘Average
Predicted Value’ line around the ‘Ideal’ line suggest that the model performs reasonably well, but there are somedeviations. This visualization aids in understanding the model’s performance and highlights areas where
improvements may be needed to enhance predictive accuracy.
This methodology aims to establish a comprehensive framework for implementing a deferred
information system within an educational environment (Krishnaveni & Meenakumari, 2010; Leidner &Jarvenpaa, 1993, 1995; Matušica & Peráček, 2023). The integration of Information and Communication
Technology (ICT), particularly in the aftermath of the pandemic era, not only facilitates the digitization of theeducational landscape but also enhances the efficacy of platforms in fostering teacher skill
development(Ivanenko et al., 2020), streamlining analytical processes, and augmenting human-computerinteraction. By leveraging advanced data analysis and machine learning tools, we can significantly improve thepredictive modeling and decision-making processes in athletic training, ultimately leading to better performanceoutcomes for student athletes.
1930---------------------------------------------------------------------------------------------------------------------------JPES ® www.efsupit.roMOHAMED REBBOUJ, SAID LOTFI--------------------------------------------------------------------------------------------------------------------------------------Conclusion
The amalgamation of two distinct solutions to generate critical information is a common practice in theindustrial sector. This approach facilitates real-time reporting, expedites decision-making processes, andenhances financial outcomes by reducing time consumption. In contrast, the sports industry employs real-timedecision-making during live events by analyzing data streams such as videos or images. This enables timely
strategic modifications and provides crucial insights for success.
The objectives of our research were to optimize sports activities, enable students to achieve their peak
performance, and ensure their participation in regional and national competitive sports through the use ofadvanced data analysis and machine learning tools.
Our analysis identified “Poids” (Weight) as the most influential factor, followed by “D-12Min” (12minute run test), “Force musculaire membre inf” (lower limb muscle strength), and “Taille” (Height). This
insight allows us to focus training programs on these key areas to optimize performance. The “Predicted vs.
True” graph showed that our model performs reasonably well, with the ‘Average Predicted Value’ line closely
following the ‘Ideal’ line. This indicates that our predictive model is effective in forecasting performance trends,
which is crucial for optimizing training regimens.
By employing a deferred generative methodology, we focus on long-term decision-making in athletic
training. This approach ensures that athletes are well-prepared for regional and national competitions. The
integration of Information and Communication Technology (ICT) facilitates the digitization of the educationallandscape, enhancing the efficacy of training platforms and fostering skill development.
To further enhance our approach, we recommend prioritizing training programs that target weight management,
endurance (12-minute run test), and lower limb muscle strength, as these are the most influential factors
identified in our analysis. We should continue to collect comprehensive data, including date and time variables,
to improve forecasting capabilities and refine predictive models. Utilizing regression and time series forecasting
techniques in Power BI and Azure Machine Learning will enhance predictive accuracy and provide deeper
insights into performance trends. Encouraging athletes to maintain their commitment to regular training sessions,such as the Cooper test, will ensure continuous data enrichment and performance improvement.
References
Anderson, E., & Hargreaves, J. (2016). Routledge handbook of sport, gender and sexuality. Routledge London.
Berthold, M. R., & Hand, D. J. (2007). Intelligent data analysis: an introduction. Springer.
Brambilla, C., Marani, R., Romeo, L., Lavit Nicora, M., Storm, F. A., Reni, G., Malosio, M., D’Orazio, T., &Scano, A. (2023). Azure Kinect performance evaluation for human motion and upper limb biomechanicalanalysis. Heliyon, 9(11), e21606. https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e21606
Eratlı Şirin, Y., & Şahin, M. (2020). Investigation of factors affecting the achievement of university students
with logistic regression analysis: School of physical education and sport example. Sage Open, 10(1),2158244020902082.
Frestel, J., Teoh, S. W. K., Broderick, C., Dao, A., & Sajogo, M. (2023). A health integrated platform forpharmacy clinical intervention data management and intelligent visual analytics and reporting.
Exploratory
ResearchinClinicalandSocialPharmacy,
12,100332.
https://doi.org/https://doi.org/10.1016/j.rcsop.2023.100332
Guess, T. M., Bliss, R., Hall, J. B., & Kiselica, A. M. (2022). Comparison of Azure Kinect overground gaitspatiotemporal parameters to marker based optical motion capture. Gait & Posture, 96, 130–136.
https://doi.org/https://doi.org/10.1016/j.gaitpost.2022.05.021
Guo, L. L., Calligan, M., Vettese, E., Cook, S., Gagnidze, G., Han, O., Inoue, J., Lemmon, J., Li, J., Roshdi, M.,
Sadovy, B., Wallace, S., & Sung, L. (2023). Development and validation of the SickKids Enterprise-wide
Data
in
Azure
Repository(SEDAR).
Heliyon,9(11),
e21586.
https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e21586
Hubáček, O., Šourek, G., & Železný, F. (2019). Exploiting sports-betting market using machine learning.
International Journal of Forecasting, 35(2), 783–796.
Ivanenko, S., Tyshchenko, V., Pityn, M., Hlukhov, I., Drobot, K., Dyadechko, I., Zhuravlov, I., Omelianenko,
H., & Sokolova, O. (2020). Analysis of the indicators of athletes at leading sports schools in swimming.
Journal of Physical Education and Sport, 20(4), 1721–1726.
Krishnaveni, R., & Meenakumari, J. (2010). Usage of ICT for Information Administration in Higher education
Institutions–A study.
Lath, F., Koopmann, T., Faber, I., Baker, J., & Schorer, J. (2021). Focusing on the coach’s eye; towards aworking model of coach decision-making in talent selection. Psychology of Sport and Exercise, 56,
102011.
Leidner, D. E., & Jarvenpaa, S. L. (1993). The information age confronts education: Case studies on electronicclassrooms. Information Systems Research, 4(1), 24–54.
Leidner, D. E., & Jarvenpaa, S. L. (1995). The use of information technology to enhance management school
education: A theoretical view. MIS Quarterly, 265–291.
----------------------------------------------------------------------------------------------------------------------------1931
JPES ® www.efsupit.roMOHAMED REBBOUJ, SAID LOTFI--------------------------------------------------------------------------------------------------------------------------------------Maszczyk, A., Gołaś, A., Pietraszewski, P., Roczniok, R., Zając, A., & Stanula, A. (2014). Application of neuraland regression models in sports results prediction. Procedia-Social and Behavioral Sciences, 117, 482–
487.
Matušica, M., & Peráček, P. (2023). The influence of relative age on the selection of players for elite events injunior football. Journal of Physical Education and Sport, 23(3), 790–799.
McCabe, A., & Trevathan, J. (2008). Artificial intelligence in sports prediction. Fifth International Conferenceon Information Technology: New Generations (Itng 2008), 1194–1197.
Ono, Y., Kaji, M., & Morita, T. (2022). A study of the worries that emerge in the career selection of Japanese
student athletes. Journal of Physical Education and Sport, 22(4), 1009–1017.
Panchal, G., Clegg, B., Koupaei, E. E., Masi, D., & Collis, I. (2024). Digital transformation and businessintelligence for a SME: systems thinking action research using PrOH modelling. Procedia Computer
Science, 232, 1809–1818. https://doi.org/https://doi.org/10.1016/j.procs.2024.02.003
Rebbouj, M., & Said, L. (2022). Students’ Physical Education Performance Analysis Using Regression Model in
Machine Learning. The International Conference of Advanced Computing and Informatics, 682–692.
Shapi, M. K. M., Ramli, N. A., & Awalin, L. J. (2021). Energy consumption prediction by using machinelearning for smart building: Case study in Malaysia. Developments in the Built Environment, 5, 100037.
https://doi.org/https://doi.org/10.1016/j.dibe.2020.100037
Szymanski, S. (2020). Sport analytics: Science or alchemy? Kinesiology Review, 9(1), 57–63.
Tyshchenko, V., Tyshchenko, D., Andronov, V., Ivanenko, S., Adamchuk, V., Hlukhov, I., & Drobot, K. (2024).
Comprehensive evaluation of efficiency to identify deficiencies in muscle activity in different modes inteam sports. Wiadomości Lekarskie Medical Advances, 77(2), 194–200.
Zhang, Q., Zhang, X., Hu, H., Li, C., Lin, Y., & Ma, R. (2022). Sports match prediction model for training andexercise using attention-based LSTM network. Digital Communications and Networks, 8(4), 508–515.

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