Contextual Variability of Team-Level High-Intensity Movement Profiles in the Turkish Super League: A 2024-2025 Season Analysis

Abstract

Purpose: High-intensity accelerations and decelerations are the external-load variables practitioners monitor most closely in elite football, yet the situational-variable literature rests almost entirely on speed-zone running, and no such data have been reported for the Turkish Super League. We examined how team-level change-of-speed load varies with match context, and how its variance is distributed between the match and the team.

Method: All 19 clubs were tracked with a semi-automated multi-camera optical system across the 2024–2025 season, for which eligible tracking data were available for 331 of the 342 scheduled fixtures (96.8%), yielding a near-complete season census. From these, 6,276 player-match observations from 427 outfield players completing at least 60 minutes were aggregated to 662 team-match observations across the 331 matches. Two per-minute outcomes were computed: high-intensity movement (HIM), the sum of acceleration (>3 m·s⁻²) and deceleration (<−3 m·s⁻²) distance, and the acceleration-to-deceleration ratio. Because each match contributes two observations, a crossed random-effects model with match and team was fitted, with variance components verified across five optimisers.

Results: Two-thirds of the variance in HIM was attributable to the match (66.3%) and only one-tenth to the team (10.1%). Neither match result (p = 0.546) nor match venue (p = 0.649) was associated with HIM, and equivalence testing bounded the venue and win–loss contrasts below 0.2 SD. Opponent quality was associated with both HIM, χ²(3) = 19.09, p < 0.001, and the ratio, χ²(3) = 12.23, p = 0.007, HIM being highest against second-quartile and lowest against third-quartile opponents (d = 0.42).  

Conclusion: High-intensity load was therefore primarily a property of the contest; opponent standard shifts its expected value without determining it. These findings caution against treating team-level physical output as a stable club characteristic and imply that analyses pooling team–match observations must model match-level clustering.

Keywords

• External Load
• Acceleration
• Deceleration
• Opponent Quality
• Variance Components

Supporting Institution

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Ethical Statement

This study was reviewed and approved by the Ethics Committee for Non-Interventional Health Sciences Research of İstanbul Yeni Yüzyıl University (approval no. 2026/09-1959; date: 1 September 2026). All procedures were performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and its subsequent amendments. As the study was based on anonymised, aggregated (team–match level) secondary performance data routinely collected during professional competition and contained no identifiable personal information, no intervention was involved and individual informed consent was not applicable.

Thanks

The author thanks all parties who facilitated access to the match-tracking data used in this study.

Türkiye Süper Ligi’nde Takım Düzeyinde Yüksek Şiddetli Hareket Profillerinin Bağlamsal Değişkenliği: 2024–2025 Sezon Analizi

Abstract

Amaç: Yüksek şiddetli hızlanmalar ve yavaşlamalar, elit futbolda uygulayıcıların en yakından takip ettiği dış yük değişkenleridir; ancak durumsal değişken literatürü neredeyse tamamen hız bölgelerine dayalı koşulara dayanmaktadır ve Türkiye Süper Ligi için bu tür herhangi bir veri rapor edilmemiştir. Takım düzeyindeki hız değişimi yükünün maç bağlamına göre nasıl değiştiğini ve varyansının maç ile takım arasında nasıl dağıldığını inceledik.

Yöntem: Tüm 19 kulüp, 2024–2025 sezonu boyunca yarı otomatik çok kameralı optik sistem ile takip edildi. Planlanan 342 müsabakanın 331'ine (%96,8) ait uygun takip verisi mevcut olup bu, neredeyse tam bir sezon sayımı (near-complete season census) niteliğindedir. Bu verilerden, en az 60 dakika süre alan 427 dış saha oyuncusundan elde edilen 6,276 oyuncu-maç gözlemi, söz konusu 331 maç boyunca 662 takım-maç gözleminde birleştirildi. Dakika başına iki sonuç değişkeni hesaplandı: yüksek şiddetli hareket (HIM), hızlanma (>3 m·s⁻²) ve yavaşlama (<−3 m·s⁻²) mesafelerinin toplamı ve hızlanma-yavaşlama oranı. Her maç iki gözlem içerdiğinden, varyans bileşenleri beş farklı optimizasyon yöntemi üzerinden doğrulanarak, maç ve takımın yer aldığı çaprazlanmış rastgele etkiler modeli oluşturuldu.

Bulgular: HIM varyansının üçte ikisi maça (%66,3) ve yalnızca onda biri takıma (%10,1) atfedildi. Ne maç sonucu (p = 0,546) ne de maçın oynandığı saha (p = 0,649) HIM ile ilişkiliydi ve eşdeğerlik testi, saha ve galibiyet-mağlubiyet karşılaştırmalarını 0,2 SD’nin altında sınırlandırdı. Rakip kalitesi hem HIM, χ²(3) = 19,09, p < 0,001, hem de oran, χ²(3) = 12,23, p = 0,007 ile ilişkiliydi; HIM, ikinci çeyreklik dilimdeki rakiplere karşı en yüksek ve üçüncü çeyreklik dilimdeki rakiplere karşı en düşük düzeydeydi (d = 0,42).

Sonuç: Yüksek şiddetli yük bu nedenle öncelikle mücadelenin bir özelliğiydi; rakip standardı beklenen değeri değiştirirken bunu belirlememektedir. Bu bulgular, takım düzeyindeki fiziksel çıktının istikrarlı bir kulüp özelliği olarak ele alınmasına karşı dikkatli olunması gerektiğini ortaya koymakta ve takım-maç gözlemlerini bir araya getiren analizlerin maç düzeyindeki kümelenmeyi modellemesi gerektiğini göstermektedir.

Keywords

• Dış Yük
• Hızlanma
• Yavaşlama
• Rakip Kalitesi
• Varyans Bileşenleri

Supporting Institution

Bu araştırma; kamu, ticari veya kâr amacı gütmeyen herhangi bir sektördeki finansman kuruluşundan özel bir destek almamıştır.

Ethical Statement

Bu çalışma, İstanbul Yeni Yüzyıl Üniversitesi Fen ve Tıbbi Müdahale Gerektirmeyen Sağlık Bilimleri Araştırmaları Etik Kurulu tarafından incelenmiş ve onaylanmıştır (karar no: 2026/09-1959; tarih: 01.09.2026). Tüm işlemler, 1964 Helsinki Bildirgesi ve sonraki güncellemelerinde belirtilen etik ilkelere uygun olarak yürütülmüştür. Çalışma, profesyonel müsabaka sırasında rutin olarak toplanan, anonimleştirilmiş ve toplulaştırılmış (takım–maç düzeyinde) ikincil performans verilerine dayandığından ve kimliği belirlenebilir hiçbir kişisel bilgi içermediğinden, herhangi bir müdahale uygulanmamış ve bireysel bilgilendirilmiş onam gerekliliği söz konusu olmamıştır.

Thanks

Yazar, bu çalışmada kullanılan müsabaka takip verilerine erişimi kolaylaştıran taraflara teşekkür eder.

References

1. Akenhead, R., & Nassis, G. P. (2016). Training load and player monitoring in high-level football: Current practice and perceptions. International Journal of Sports Physiology and Performance, 11(5), 587–593. https://doi.org/10.1123/ijspp.2015-0331
2. Andrzejewski, M., Chmura, P., Konefał, M., Kowalczuk, E., & Chmura, J. (2018). Match outcome and sprinting activities in match play by elite German soccer players. The Journal of Sports Medicine and Physical Fitness, 58(6), 785–792. https://doi.org/10.23736/S0022-4707.17.07352-2
3. Andrzejewski, M., Oliva-Lozano, J. M., Chmura, P., Chmura, J., Czarniecki, S., Kowalczuk, E., Rokita, A., Muyor, J. M., & Konefał, M. (2022). Analysis of team success based on match technical and running performance in a professional soccer league. BMC Sports Science, Medicine and Rehabilitation, 14(1), Article 82. https://doi.org/10.1186/s13102-022-00473-7
4. Bradley, P. S., & Ade, J. D. (2018). Are current physical match performance metrics in elite soccer fit for purpose or is the adoption of an integrated approach needed? International Journal of Sports Physiology and Performance, 13(5), 656–664. https://doi.org/10.1123/ijspp.2017-0433
5. Carling, C., Bradley, P., McCall, A., & Dupont, G. (2016). Match-to-match variability in high-speed running activity in a professional soccer team. Journal of Sports Sciences, 34(24), 2215–2223. https://doi.org/10.1080/02640414.2016.1176228
6. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
7. Coskun, B., & Torun, M. C. (2025). Evaluation of the differences between home and away matches depending on GPS data from a senior professional football team in the Turkish Super League. Applied Sciences, 15(20), Article 11275. https://doi.org/10.3390/app152011275
8. Felipe, J. L., Garcia-Unanue, J., Viejo-Romero, D., Navandar, A., & Sánchez-Sánchez, J. (2019). Validation of a video-based performance analysis system (Mediacoach) to analyze the physical demands during matches in LaLiga. Sensors, 19(19), Article 4113. https://doi.org/10.3390/s19194113

9. González-Rodenas, J., Nevado, F., López-Del Campo, R., Soler-Aguinaga, A., Agulló, F., Moreno-Pérez, V., & Del Coso, J. (2025). Validity of coupling TRACAB's Gen5 and Mediacoach systems to calculate accelerations and decelerations in professional football. Sensors, 25(6), Article 1804. https://doi.org/10.3390/s25061804
10. Gualtieri, A., Rampinini, E., Dello Iacono, A., & Beato, M. (2023). High-speed running and sprinting in professional adult soccer: Current thresholds definition, match demands and training strategies. A systematic review. Frontiers in Sports and Active Living, 5, Article 1116293. https://doi.org/10.3389/fspor.2023.1116293
11. Harper, D. J., Carling, C., & Kiely, J. (2019). High-intensity acceleration and deceleration demands in elite team sports competitive match play: A systematic review and meta-analysis of observational studies. Sports Medicine, 49(12), 1923–1947. https://doi.org/10.1007/s40279-019-01170-1
12. Harper, D. J., McBurnie, A. J., Dos'Santos, T., Eriksrud, O., Evans, M., Cohen, D. D., Rhodes, D., Carling, C., & Kiely, J. (2022). Biomechanical and neuromuscular performance requirements of horizontal deceleration: A review with implications for random intermittent multi-directional sports. Sports Medicine, 52(10), 2321–2354. https://doi.org/10.1007/s40279-022-01693-0
13. Hoppe, M. W., Slomka, M., Baumgart, C., Weber, H., & Freiwald, J. (2015). Match running performance and success across a season in German Bundesliga soccer teams. International Journal of Sports Medicine, 36(7), 563–566. https://doi.org/10.1055/s-0034-1398578
14. Ju, W., Hawkins, R., Doran, D., Gómez-Díaz, A., Martín-García, A., Evans, M., Laws, A., & Bradley, P. S. (2023). Tier-specific contextualised high-intensity running profiles in the English Premier League: More on-ball movement at the top. Biology of Sport, 40(2), 561–573. https://doi.org/10.5114/biolsport.2023.118020
15. Lago, C. (2009). The influence of match location, quality of opposition, and match status on possession strategies in professional association football. Journal of Sports Sciences, 27(13), 1463–1469. https://doi.org/10.1080/02640410903131681
16. Lago, C., Casais, L., Domínguez, E., & Sampaio, J. (2010). The effects of situational variables on distance covered at various speeds in elite soccer. European Journal of Sport Science, 10(2), 103–109. https://doi.org/10.1080/17461390903273994
17. Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, Article 863. https://doi.org/10.3389/fpsyg.2013.00863
18. Linke, D., Link, D., & Lames, M. (2018). Validation of electronic performance and tracking systems EPTS under field conditions. PLOS ONE, 13(7), Article e0199519. https://doi.org/10.1371/journal.pone.0199519
19. Linke, D., Link, D., & Lames, M. (2020). Football-specific validity of TRACAB's optical video tracking systems. PLOS ONE, 15(3), Article e0230179. https://doi.org/10.1371/journal.pone.0230179
20. Morgans, R., Di Michele, R., Ceylan, I. H., Ryan, B., Haslam, C., King, M., Zmijewski, P., & Oliveira, R. (2025). Physical match performance of elite soccer players from the English Championship League and the English Premier League: The effects of opponent ranking and positional differences. Biology of Sport, 42(1), 29–38. https://doi.org/10.5114/biolsport.2025.139079
21. Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R² from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x
22. Paul, D. J., Bradley, P. S., & Nassis, G. P. (2015). Factors affecting match running performance of elite soccer players: Shedding some light on the complexity. International Journal of Sports Physiology and Performance, 10(4), 516–519. https://doi.org/10.1123/IJSPP.2015-0029
23. Plakias, S., & Michailidis, Y. (2024). Factors affecting the running performance of soccer teams in the Turkish Super League. Sports, 12(7), Article 196. https://doi.org/10.3390/sports12070196
24. Plakias, S., Tasoulis, S., Kyranoudis, A. E., Kokkotis, C., & Moustakidis, S. (2025). The limited impact of running performance on football success in the Turkish Super League. Applied Sciences, 15(2), Article 637. https://doi.org/10.3390/app15020637
25. Pollard, R., & Gómez, M. A. (2014). Comparison of home advantage in men's and women's football leagues in Europe. European Journal of Sport Science, 14(sup1), S77–S83. https://doi.org/10.1080/17461391.2011.651490
26. Rampinini, E., Impellizzeri, F. M., Castagna, C., Coutts, A. J., & Wisløff, U. (2009). Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. Journal of Science and Medicine in Sport, 12(1), 227–233. https://doi.org/10.1016/j.jsams.2007.10.002
27. Rhodes, D., Valassakis, S., Bortnik, L., Eaves, R., Harper, D., & Alexander, J. (2021). The effect of high-intensity accelerations and decelerations on match outcome of an elite English League Two football team. International Journal of Environmental Research and Public Health, 18(18), Article 9913. https://doi.org/10.3390/ijerph18189913
28. Silva, H., Nakamura, F. Y., Beato, M., & Marcelino, R. (2022). Acceleration and deceleration demands during training sessions in football: A systematic review. Science and Medicine in Football. Advance online publication. https://doi.org/10.1080/24733938.2022.2090600
29. Trewin, J., Meylan, C., Varley, M. C., & Cronin, J. (2017). The influence of situational and environmental factors on match-running in soccer: A systematic review. Science and Medicine in Football, 1(2), 183–194. https://doi.org/10.1080/24733938.2017.1329589
30. von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2007). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. The Lancet, 370(9596), 1453–1457. https://doi.org/10.1016/S0140-6736(07)61602-X