Individual Metrics to Characterize the Players

Clemente, Filipe Manuel; Sequeiros, João Bernardo; Correia, Acácio F. P. P.; Silva, Frutuoso G. M.; Martins, Fernando Manuel Lourenço

doi:10.1007/978-3-319-59029-5_3

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…This GNSS-IMU wearable sensor was chosen because it is already used in soccer training for performance and training monitoring ( Clemente et al, 2018 ). This sensor, with a sampling frequency of 200 Hz for the IMU and 10 Hz for the GNSS unit, was used in the “airborne <4 g” configuration of the in-built u-blox GNSS module.…”

Section: Materials and Equipmentmentioning

confidence: 99%

A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint

Apte

Meyer

Grémeaux

et al. 2020

Front. Bioeng. Biotechnol.

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Power-Force-Velocity profile obtained during a sprint test is crucial for designing personalized training and evaluating injury risks. Estimation of instantaneous velocity is requisite for developing these profiles and the predominant method for this estimation assumes it to have a first order exponential behavior. While this method remains appropriate for maximal sprints, the sprint velocity profile may not always show a first-order exponential behavior. Alternately, velocity profile has been estimated using inertial sensors, with a speed radar, or a smartphone application. Existing methods either relied on the exponential behavior or timing gates for drift removal, or estimated only the mean velocity. Thus, there is a need for a more flexible and appropriate approach, allowing for instantaneous velocity estimation during sprint tests. The proposed method aims to solve this problem using a sensor fusion approach, by combining the signals from wearable Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) sensors. We collected data from nine elite sprinters, equipped with a wearable GNSS-IMU sensor, who ran two trials each of 60 and 30/40 m sprints. We developed an algorithm using a gradient descent-based orientation filter, which simplified our model to a linear one-dimensional model, thus allowing us to use a simple Kalman filter (KF) for velocity estimation. We used two cascaded KFs, to segment the sprint data precisely, and to estimate the velocity and the sprint duration, respectively. We validated the estimated velocity and duration with speed radar and photocell data as reference. The median RMS error for the estimated velocity ranged from 6 to 8%, while that for the estimated sprint duration lied between 0.1 and −6.0%. The Bland–Altman plot showed close agreement between the estimated and the reference values of maximum velocity. Examination of fitting errors indicated a second order exponential behavior for the sprint velocity profile, unlike the first order behavior previously suggested in literature. The proposed sensor-fusion algorithm is valid to compute an accurate velocity profile with respect to the radar; it can compensate for and improve upon the accuracy of the individual IMU and GNSS velocities. This method thus enables the use of wearable sensors in the analysis of sprint test.

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Section: Materials and Equipmentmentioning

confidence: 99%

A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint

Apte

Meyer

Grémeaux

et al. 2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…Usually in match analyses conducted on soccer, there is a strong tendency to code, collect and report evidence about the variation of different performance indicators (e.g., successful passes, shots, recoveries) between different contextual factors (e.g., match status, location of the match, competitive level of the opponents, zone of the actions) (Lago-Peñas and Lago-Ballesteros, 2011; Tenga et al, 2017). However, some other performance indicators, such as tactical behavior (Barreira et al, 2013) or collective patterns based on position data (Clemente et al, 2017; Memmert et al, 2017), can be analyzed based on different contextual factors. Each derived piece of information presents its strong and weak points, i.e., the performance indicators based on notational analysis provide important information about the final outcomes of the match, although without considering the mechanisms and processes that justified such outcomes (Vilar et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Variations of Network Centralities Between Playing Positions in Favorable and Unfavorable Close and Unbalanced Scores During the 2018 FIFA World Cup

et al. 2019

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The purpose of this study is twofold: (i) analyze the variations of network centralities between close (difference of goals equal to one) and unbalanced (difference of goals equal to or greater than two) scores; and (ii) compare the centrality levels between playing positions. The passing sequences that occurred during the 64 matches played by the 32 national teams that participated in the 2018 FIFA World Cup were analyzed and coded. The network centralities of degree prestige and degree centrality were calculated based on the weighted adjacency matrices built from the passing sequences. The results reveal that higher degree centralities of midfielders occurred in unfavorable (lost) unbalanced scores ( p = 0.046; ES (effect size) = 0.472). Moreover, in favorable (won) matches the higher values of degree centrality of central defenders ( p = 0.014; ES: 0.458) and defensive midfielders ( p = 0.004; ES: 0.715) were also found in unbalanced scores. The comparisons between positions revealed that the highest and significant degree prestige levels were found in defensive midfielders in both close (12.10%) and unbalanced scores (10.95%). In conclusion, it is possible to observe that winning by an unbalanced score significantly increased the centrality levels of the wingers and forwards in comparison to close scores. Moreover, it was also found that independent of the final score or the unbalanced score level, the defensive midfielders were the most prominent or recruited players during the passing sequences.

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Individual Metrics to Characterize the Players

Cited by 2 publications

References 14 publications

A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint

A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint

Variations of Network Centralities Between Playing Positions in Favorable and Unfavorable Close and Unbalanced Scores During the 2018 FIFA World Cup

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