Analysis of Relationship between Training Load and Recovery Status in Adult Soccer Players: a Machine Learning Approach

Abstract Periods of intensified training may increase athletes' fatigue and impair their recovery status. Therefore, understanding internal and external load markers-related to fatigue is crucial to optimize their weekly training loads. The current investigation aimed to adopt machine learning (ML) techniques to understand the impact of training load parameters on the recovery status of athletes. Twenty-six adult soccer players were monitored for six months, during which internal and external load parameters were daily collected. Players' recovery status was assessed through the 10-point total quality recovery (TQR) scale. Then, different ML algorithms were employed to predict players' recovery status in the subsequent training session (S-TQR). The goodness of the models was evaluated through the root mean squared error (RMSE), mean absolute error (MAE), and Pearson's Correlation Coefficient (r). Random forest regression model produced the best performance (RMSE=1.32, MAE=1.04, r = 0.52). TQR, age of players, total decelerations, average speed, and S-RPE recorded in the previous training were recognized by the model as the most relevant features. Thus, ML techniques may help coaches and physical trainers to identify those factors connected to players' recovery status and, consequently, driving them toward a correct management of the weekly training loads

Retrofitting Cost Modeling in Aircraft Design

Aircraft retrofitting is a challenging task involving multiple scenarios and stakeholders. Providing a strategy to retrofit an existing platform needs detailed knowledge of multiple aspects, ranging from aircraft performance and emissions, development and conversion costs to the projected operating costs. This paper proposes a methodology to account for retrofitting costs at an industrial level, explaining the activities related to such a process. Costs are mainly derived from three contributions: development costs, conversion costs and equipment acquisition costs. Different retrofitting packages, such as engine conversion and onboard systems electrification, are applied in the retrofitting of an existing 90 PAX regional turbofan aircraft, highlighting the impact on both aircraft performance and industrial costs. Multiple variables and scenarios are considered regarding trade-offs and decision-making, including the number of aircraft to be retrofitted, the heritage of an aircraft and its utilization, the fuel price and the airport charges. The results show that a reduction of 15% in fuel demand and emissions are achievable, considering a fleet of 500 platforms, through a conspicuous investment of around EUR 20 million per aircraft (50% of the estimated price). Furthermore, depending on the scenarios driven by the regulatory authorities, governments or airlines, this paper provides a useful methodology to evaluate the feasibility of retrofitting activities

Validity and reliability of a photoelectric cells system for the evaluation of change of direction and lateral jumping abilities in collegiate basketball athletes

The validity and reliability of the Optojump system were investigated for jumping height and flight time in vertical jump tests. Conversely, the purpose of the present study was to investigate the validity and reliability of the Optojump system for measuring contact time and lateral displacement in change of direction and lateral jump tests. Thirty basketball collegiate athletes were tested on two 10 m sprints with a 60° (COD60) or 180° (COD180) change of direction, lateral controlled (CLRJ) and maximal (MLRJ) rebound jump, and lateral countermovement (LCMJ) and squat (LSJ) jump with the concomitant use of two force plates and the Optojump system for the measurement of contact time in COD60, COD180, CLRJ, MLRJ, and lateral jumping distance in all the lateral jump tests. Almost perfect coefficients (r ≥ 0.95) emerged for contact time in COD60, COD180, CLRJ, MLRJ, although a systematic bias was found for COD60 (-0.01 s). Good-to-excellent reliability was found for almost all the measurements of contact time and lateral jumping distance for change of direction and lateral jump tests. Therefore, the use of Optojump system for testing change of direction and lateral jumping abilities should be executed with caution, avoiding misinterpretation of data

Multidisciplinary design of a more electric regional aircraft including certification constraints

The use of electrified on-board systems is increasingly more required to reduce aircraft complexity, polluting emissions, and its life cycle cost. However, the more and all-electric aircraft configurations are still uncommon in the civil aviation context and their certifiability has yet to be proven in some aircraft segments. The aim of the present paper is to define a multidisciplinary design problem which includes some disciplines pertaining to the certification domain. In particular, the study is focused on the preliminary design of a 19 passengers small regional turboprop aircraft. Different on-board systems architectures with increasing electrification levels are considered. These architectures imply the use of bleedless technologies including electrified ice protection and environmental control systems. The use of electric actuators for secondary surfaces and landing gear are also considered. The aircraft design, which includes aerodynamic, structural, systems and propulsion domains, is then assessed by some certification disciplines. In particular, minimum performance, external noise and safety assessments are included in the workflow giving some insights on the aircraft certifiability. The results show a reduction of 3% of MTOM and 3% of fuel mass depending on the systems architecture selected. From the certification side, the design has proven to be certifiable and the margins with the certification constraint can be controlled to improve the overall design

Collaborative Design of a Business Jet Family Using the AGILE 4.0 MBSE Environment

This paper presents the collaborative model-based design of a business jet family. In family design, a trade-off is made between aircraft performance, reducing fuel burn, and commonality, reducing manufacturing costs. The family is designed using Model-Based Systems Engineering (MBSE) methods developed in the AGILE 4.0 project. The EC-funded AGILE 4.0 project extends the scope of the preliminary aircraft design process to also include systems engineering phases and new design domains like manufacturing, maintenance, and certification. Stakeholders, needs, requirements, and architecture models of the business jet family are presented. Then, the collaborative Multidisciplinary Design Analysis and Optimization (MDAO) capabilities are used to integrate various aircraft design disciplines, including overall aircraft design, onboard systems design, wing structural sizing, tailplane sizing, mission analysis, and cost estimation. Decisions regarding the degree of commonality are implemented by optionally fixing the design of a shared component when sizing an aircraft

Multidisciplinary Design and Optimization of Regional Jet Retrofitting Activity

A retrofit analysis on a 90 passengers regional jet aircraft is performed through a multidisciplinary collaborative aircraft design and optimization highlighting the impact on costs and performance. Two different activities are accounted for selecting the best aircraft retrofit solution: a re-engining operation that allows to substitute a conventional power-plant platform with advanced geared turbofan and an on-board-systems architecture modernization, considering different levels of electrification. Besides the variables that are directly dependent from these activities, also scenario variables are considered during the optimization such as the fuel price, the fleet size and the years of utilization of the upgraded systems. The optimization is led by impacts of the retrofitting process on emissions, capital costs and saving costs, computed at industrial level. Overall aircraft design competences (aerodynamics, masses, performance, noise, and emissions) have been computed increasing the level of fidelity and reliability. The whole process is implemented in the framework of the AGILE 4.0 research project in a collaborative remote multidisciplinary approach. Results show that the engine retrofitting can be a profitable solution for both manufacturers and airliners. Conversely, the on-board-system electrification seems to be not convenient in a retrofitting process due to the high capital costs. Depending on the operative scenario, involved stakeholders can properly orient their decision on a retrofitting strategy

A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players

Predicting and avoiding an injury is a challenging task. By exploiting data mining techniques, this paper aims to identify existing relationships between modifiable and non-modifiable risk factors, with the final goal of predicting non-contact injuries. Twenty-three young soccer players were monitored during an entire season, with a total of fifty-seven non-contact injuries identified. Anthropometric data were collected, and the maturity offset was calculated for each player. To quantify internal training/match load and recovery status of the players, we daily employed the session-RPE method and the total quality recovery (TQR) scale. Cumulative workloads and the acute: chronic workload ratio (ACWR) were calculated. To explore the relationship between the various risk factors and the onset of non-contact injuries, we performed a classification tree analysis. The classification tree model exhibited an acceptable discrimination (AUC=0.76), after receiver operating characteristic curve (ROC) analysis. A low state of recovery, a rapid increase in the training load, cumulative workload, and maturity offset were recognized by the data mining algorithm as the most important injury risk factors

A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players

Predicting and avoiding an injury is a challenging task. By exploiting data mining techniques, this paper aims to identify existing relationships between modifiable and non-modifiable risk factors, with the final goal of predicting non-contact injuries. Twenty-three young soccer players were monitored during an entire season, with a total of fifty-seven non-contact injuries identified. Anthropometric data were collected, and the maturity offset was calculated for each player. To quantify internal training/match load and recovery status of the players, we daily employed the session-RPE method and the total quality recovery (TQR) scale. Cumulative workloads and the acute: chronic workload ratio (ACWR) were calculated. To explore the relationship between the various risk factors and the onset of non-contact injuries, we performed a classification tree analysis. The classification tree model exhibited an acceptable discrimination (AUC=0.76), after receiver operating characteristic curve (ROC) analysis. A low state of recovery, a rapid increase in the training load, cumulative workload, and maturity offset were recognized by the data mining algorithm as the most important injury risk factors. © 2021 M. Mandorino et al., published by Sciendo