The calculation of player speed from tracking data

Wu, Lucas Y; Swartz, Tim B.

doi:10.1177/17479541221124036

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…Current sprint modeling practices require continuous acceleration or velocity, or distance or time data. Acceleration values exported from GNSS manufacturer software may be derived from either embedded accelerometers or velocity and time samples (62). Velocity is estimated using 2 methods, positional differentiation (distance/ time) or Doppler shift, with the latter preferred because of superior accuracy (55).…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review and Meta-Analysis of Wearable Satellite System Technology for Linear Sprint Profiling: Technological Innovations and Practical Applications

Cormier,

Meylan,

Agar-Newman

et al. 2023

Journal of Strength and Conditioning Research

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Cormier, P, Meylan, C, Agar-Newman, D, Geneau, D, Epp-Stobbe, A, Lenetsky, S, and Klimstra, M. A systematic review and meta-analysis of wearable satellite system technology for linear sprint profiling: technological innovations and practical applications. J Strength Cond Res XX(X): 000–000, 2023—An emerging and promising practice is the use of global navigation satellite system (GNSS) technology to profile team-sports athletes in training and competition. Therefore, the purpose of this narrative systematic review with meta-analysis was to evaluate the literature regarding satellite system sensor usage for sprint modeling and to consolidate the findings to evaluate its validity and reliability. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, an electronic search of the databases, PubMed and SPORTDiscus (EBSCO), was conducted. Concurrent validity and reliability studies were considered, and 16 studies were retained for the review from the initial 1,485 studies identified. The effects on outcomes were expressed as standardized mean differences (SMDs, Cohen's d) for each outcome (i.e., maximal sprint speed [MSS], the acceleration constant [τ], maximal theoretical velocity [V 0], relative force [F 0], and relative power [Pmax]). Effect magnitudes represented the SMD between GNSS-derived and criterion-derived (i.e., radar and laser) and resulted in the following estimates: small for MSS (d = 0.22, 95% CI 0.01 to 0.42), τ (d = −0.18, 95% CI −0.60 to 0.23), V 0 (d = 0.14, 95% CI −0.08 to 0.36), relative F 0 (d = 0.15, 95% CI −0.25 to 0.55), and relative Pmax (d = 0.21, 95% CI −0.16 to 0.58). No publication bias was identified in meta-analyzed studies and moderator analysis revealed that several factors (sampling rate and sensor manufacturer) influenced the results. Heterogeneity between studies was considered moderate to high. This highlighted the differences between studies in sensor technology differences (i.e., sampling rate, sensor fusion, and satellite network acquisition), processing techniques, criterion technology used, sprint protocols, outcome reporting, and athlete characteristics. These findings may be useful in guiding improvements in sprint modeling using GNSS technology and enable more direct comparisons in future research. Implementation of all-out linear sprint efforts with GNSS technology can be integrated into sport-specific sessions for sprint modeling when robust and consistent data processing protocols are performed, which has important implications for fatigue monitoring, program design, systematic testing, and rehabilitation in individual and team sports.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review and Meta-Analysis of Wearable Satellite System Technology for Linear Sprint Profiling: Technological Innovations and Practical Applications

Cormier,

Meylan,

Agar-Newman

et al. 2023

Journal of Strength and Conditioning Research

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“…CSL tracking data have been used in various recent sport analytics investigations including Guan et al, 15 Wu and Swartz, 16 Epasinghege Dona and Swartz 17,18 and Wu et al 19 Using the CSL tracking data, we now consider the calculation of maximum acceleration for a particular player for a single match. The following development is based on Wu and Swartz 20 where there is an emphasis on the accuracy of calculations involving player tracking data. If (x(t), y(t)) denotes the location of a player at time t, then the player's velocity at time t in the x-coordinate direction is defined by…”

Section: Datamentioning

confidence: 99%

“…where Δ = 0.4 seconds is recommended for accuracy. 20 We measure acceleration as a scalar quantity, without a directional component. Therefore, following the above development, and using (3) and ( 4), we approximate a(t j ), the acceleration at time t j by…”

Section: Datamentioning

confidence: 99%

“…Using the CSL tracking data, we now consider the calculation of maximum acceleration for a particular player for a single match. The following development is based on Wu and Swartz 20 where there is an emphasis on the accuracy of calculations involving player tracking data. If false( x false( t false) , 0.25em y false( t false) false) denotes the location of a player at time t, then the player’s velocity at time t in the x-coordinate direction is defined by Similarly, in the y-coordinate direction, the player’s velocity at time t is defined by Formulae (1) and (2) are mathematical expressions involving limits, and are not quantities that can be calculated from data.…”

Section: Datamentioning

confidence: 99%

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Acceleration and age in soccer

Guan,

Swartz

2024

International Journal of Sports Science & Coaching

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This paper considers how player acceleration changes in soccer relative to age. A plot of average maximum acceleration versus age is produced. The construction of the plot is based on methods from functional data analysis and the availability of tracking data from the 2019 season of the Chinese Super League. For an individual player, we calculate his maximum acceleration for each single match of the 2019 season. Since the players’ maximum accelerations are observed only on a single season instead of their entire careers, we treat them as incomplete functional data, called functional snippets. The average maximum acceleration, i.e., the mean function of the functional snippets rather than full curves is estimated by a local linear smoothing method. The most important observation is that the shape of the acceleration curve closely resembles curves of soccer performance versus age. This observation has implications for predicting future performance since acceleration is more easily and more accurately measured than performance.

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“…It is also important to understand that a key part of the running load in football is the intensive load (acceleration and decelerations). Current optical tracking technologies are not capable of accurately determining acceleration and deceleration efforts (33), hence why they were not analysed in this study. However, research has suggested that intensive outputs may play a significant role in match play (2,34).…”

Section: Future Researchmentioning

confidence: 99%

Running more than before? The evolution of running load demands in the English Premier League

Allen

Taberner

Zhilkin

et al. 2023

International Journal of Sports Science & Coaching

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Purpose The purpose of this study was to assess running load trends (total distance (TD), high-intensity distance (HID; > 5.5ms−1) and sprinting distance (SprD; > 7ms−1)) in the English Premier League (EPL) between the 2014/2015 and 2018/2019 seasons using a semi-automated optical tracking system. Methods A total of 1634 games across 5 seasons (2014/2015–2018/2019) were analysed for team and positional TD, HID and SprD. All matches were tracked using TRACAB® Gen-4 HD motion cameras. Data were analysed to identify significant team and positional differences for each metric. Results Small to moderate increases were seen in team running load for TD ( p = 0.02; effect size (ES): 0.21), HID ( p < 0.001; ES: 0.88) and SprD ( p < 0.001; ES: 0.67) between 2014/2015 and 2018/2019. Positional analysis showed significant increases in TD for all except for defensive midfielders (DM), attacking midfielders (AM) and wide midfielders (WM) ( p < 0.001–0.009; ES: 0.14–0.36); HID for all positions excluding goalkeepers (GK) ( p < 0.001–0.007; ES: 0.2–0.54) and SprD for all positions except for GK, central midfielders (CM) and AM, ( p < 0.001–0.020; ES: 0.19–0.39). Trivial to small differences were seen in season-to-season comparisons for TD ( p < 0.001–0.023; ES: −0.16–0.52), HID ( p < 0.001–0.033; ES: 0.10–0.38) and SprD ( p < 0.001–0.025; ES: 0.13–0.27) for both team and positional differences. Conclusion Small to moderate increases in team TD, HID and SprD occurred in the EPL between 2014/2015 and 2018/2019, with trivial to small changes in season-to-season variations. Actual changes in running load over time appear to be dependent on playing position. Running load trends should be updated regularly and practitioners should be aware of the variability within their team, and programme accordingly.

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The calculation of player speed from tracking data

Cited by 7 publications

References 14 publications

A Systematic Review and Meta-Analysis of Wearable Satellite System Technology for Linear Sprint Profiling: Technological Innovations and Practical Applications

A Systematic Review and Meta-Analysis of Wearable Satellite System Technology for Linear Sprint Profiling: Technological Innovations and Practical Applications

Acceleration and age in soccer

Running more than before? The evolution of running load demands in the English Premier League

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