After 8 weeks, the game-based group: Field goal % up 3.3% Assist-to-turnover up 0.19 PlayerLoad up 9.6%, accelerations up 13.1% Lower fatigue, higher enjoyment

⚽ ¿Preparador físico de fútbol? ¡Todo gratis para vos! 💪 Artículos, videos y programación 👉 pf-tnt-strength-and-conditio… 🎥 YT: @PF_TNT 📲 IG: @hugotinti 🤝 LinkedIn: Hugo Tinti #StrengthAndConditioning #LoadManagement #TrainingLoad #PlayerLoad #GPSSoccer #ControlDeCarga

PlayerLoad is everywhere in sport science, but this paper makes a compelling case that widespread use should not be mistaken for scientific strength. Why this paper deserves attention 1. It clearly separates reliability from validity PlayerLoad may be highly reliable, but that does not automatically mean it is measuring the right thing in the right way. 2. It exposes foundational problems with the metric The authors argue that PlayerLoad is built on inconsistent definitions, arbitrary units, opaque proprietary processing, and weak theoretical grounding. 3. It shows why signal processing cannot be ignored Sampling frequency, dynamic range, filtering, gravitational removal, and sensor placement all shape the output. If those details are unclear, interpretation becomes shaky. 4. It challenges the logic behind many validity claims Correlating PlayerLoad with heart rate, TRIMP, or session RPE is not enough to prove construct validity, especially when those comparison measures carry their own limitations. 5. It connects the science to real-world decision making Misinterpreting PlayerLoad can affect programming, cross-athlete comparisons, and return-to-play decisions in ways that matter. The key takeaway This paper is not saying practitioners are foolish for using PlayerLoad. It is saying the field needs to be far more honest about what the metric can and cannot tell us. The biggest methodological concerns 1. Inconsistent definitions Different studies are not always using the same formula or even describing the metric the same way. 2. Proprietary opacity If the computational pathway is hidden, scientific interpretation and cross-study comparison become much harder. 3. Units and terminology The metric is closely tied to change in acceleration, yet it is reported in arbitrary units rather than in a way that aligns cleanly with physics. 4. Sensitivity to orientation and noise Device rotation, noise amplification, and sample-rate issues can distort the output. 5. Sensor placement Upper-back placement is practical, but that does not make it equivalent to measuring center-of-mass behavior. Why this review stands out The authors do not just complain about PlayerLoad. They build the argument step by step through accelerometry fundamentals, signal processing, validity theory, attachment location, and alternative metrics. That makes this far more than a surface-level critique. Bottom line Reliable does not mean valid. Convenient does not mean interpretable. Popular does not mean scientifically secure. That is why this paper matters.

1) a few weeks ago the following paper from @craig_staunton & colleagues was released which discusses the use of the Catapult PlayerLoad variable for measuring external load/intensity frontiersin.org/journals/spo…

What if one of the most common “performance” metrics in sport is not actually telling us what we think it is? That is the hook of this paper. Staunton and colleagues take a hard look at PlayerLoad™, one of the most widely used accelerometer-derived metrics in sport, and argue that its scientific foundation is much shakier than its popularity suggests. Right from the opening page, they note concerns with inconsistent definitions, arbitrary units, opaque filtering methods, questionable theoretical underpinnings, and imprecise mechanical terminology, and state plainly that the construct validity of PlayerLoad remains unverified. They also note that evidence for meaningful dose–response relationships with performance outcomes remains weak. In other words, the paper is not saying monitoring is useless. It is saying we need to be more careful when a widely used KPI starts being treated like the performance itself. One of the strongest parts of the review is that it explains why this matters. The authors show that PlayerLoad is highly sensitive to sensor location, sampling frequency, filtering choices, and even natural changes in device orientation, which can create fictitious increases in the metric that do not actually reflect more physical effort. They also point out that manufacturer software appears to produce values about 15% lower than manual calculations, suggesting undisclosed processing in the background. On page 7, Figure 2 makes this especially clear by showing how the same raw triaxial acceleration data can produce very different outputs depending on the metric being used. The practical implication is important: practitioners may mistake a higher KPI for greater mechanical stress, make misleading cross-athlete comparisons, or assume an athlete has “matched” prior demands in return-to-play when the underlying mechanical exposure is actually different. What makes the paper valuable is that it does not just criticize. It offers a better direction. The authors introduce alternatives like Accel’Rate and especially accelerometry-derived net force (FNet), which they describe as being built on clearer biomechanical principles, transparent signal processing, and outputs in SI units rather than arbitrary ones. Their conclusion is not anti-technology. It is anti black box thinking. They explicitly encourage practitioners to be skeptical of proprietary metrics until they are fully understood and validated, and argue for integrated monitoring frameworks that combine accelerometry with complementary mechanical, physiological, and perceptual indicators. That is the real takeaway. KPIs are useful, but they are not the engine. They are the check engine light. Helpful, yes. Worth watching, absolutely. But never the same thing as the actual performance we are trying to understand.

🆕"This review critically evaluated the validity & reliability of the PlayerLoad™️ metric (PL), highlighting the need for greater transparency & theoretical rigor in wearable player monitoring" 👉@craig_staunton @Peter_Edholm al, 2026 🇸🇪 📂Open Access: frontiersin.org/journals/spo…

How can GPS metrics be grouped by what they're telling us? Research shows that some metrics are associated with one another (e.g. total distance and PlayerLoad). As you can see in this breakdown by @catapultsports, they also fall into the same category bit.ly/4bLzmUS

A2) We break it into Volume & Intensity: Volume = Daily Workload % Daily Load ÷ (max game/practice ×2) → % of workload. Intensity = Status PlayerLoad ÷ sprint count → shows how hard the work was. Goals for both to balance and guide prep / recovery. #ironspeedchat

Final Takeaways. ✅ GPS is a brilliant tool
❌ But total distance ≠ performance
❌ PlayerLoad ≠ game impact Track what matters:
✔️ Explosiveness
✔️ High-speed efforts
✔️ Position-specific intensity
✔️ Recovery capacity

Makalenin linki: martin-buchheit.net/wp-conte… YAPILAN ARASTIRMANIN KISA BIR OZETI Zaman, Futbolda Bir KPI Olarak Koşuyu Bırakma Zamanı 📌 Özet Elit futbol dünyasında başarı, oyuncuların ne kadar hızlı veya ne kadar koştuğuna değil, futbol aksiyonlarına bağlıdır. Buna rağmen, geleneksel spor bilimi, koşu metriklerini anahtar performans göstergesi (KPI) olarak fazlasıyla vurgulamıştır. Bu çalışma, bu eski görüşü, makine öğrenimi ve yerleşik izleme gibi modern veri odaklı bir yaklaşımla (Spor Bilimi 3.0) sorgulamaktadır. 🎯 Çalışmanın Amacı Hazırlık—futbol kondisyonu ve nöromasküler tazelik kombinasyonu olarak tanımlanan—ile maç koşu aktivitesi arasındaki etkileşimi ve bu faktörlerin maç sonuçlarıyla ilişkisini incelemek. 🧪 Metodoloji Katılımcılar: İtalyan profesyonel bir takımdan 25 elit oyuncu, 3 sezon boyunca (2022–2025). Veriler: GPS (WIMU Pro) ve kalp atış hızı izleme ile takip edilen 73 maç. Endeksler: Kondisyon Endeksi (FI): Antrenmana kalp atış hızı tepkisi üzerine. Daha düşük HR = daha iyi kondisyon. Tazelik Endeksi (FR): Mekanik yük (PlayerLoad) üzerine. Daha düşük gerilim = daha iyi tazelik. Maç Koşu Aktivitesi: Oyuncu tarafından standartlaştırılmış toplam mesafe (TD). İstatistiksel analiz, FI, FR ve TD'nin galibiyet, beraberlik ve mağlubiyetle ilişkilerini incelemek için çoknominal lojistik regresyon ve etkileşim modellemeyi kullandı. 📊 Ana Bulgular Daha fazla koşmak, galibiyet anlamına gelmedi: Daha yüksek toplam mesafe (TD), galibiyetten çok beraberlik ile ilişkilendirildi. Galibiyet için en iyi senaryo: ✅ Yüksek Kondisyon (FI) ✅ Yüksek Tazelik (FR) 🚫 Düşük Koşu Aktivitesi (TD) → Bu üçlü, galibiyet için en yüksek ihtimali sağladı (OR = 2.6). Kondisyon/tazelik olmadan aşırı koşu ters sonuç verdi. Koşu, maç sonuçlarının bir nedeni değil, bir sonucu—taktikler, rakip ve bağlam tarafından yönlendirilir. 🔑 Çıkarılması Gereken Mesajlar 🚫 Koşuyu bağımsız bir KPI olarak kullanmayı bırakın. Bu, bağlama bağımlıdır ve yanıltıcıdır. 🧠 Futbol hazırlığı (tazelik kondisyon), performans için daha önemlidir. ⚙️ Koşu, futbol aksiyonlarının bir çıktısı olarak anlaşılmalı, bir hedef olarak değil. ⚖️ Daha düşük iç yük, daha iyi karar verme ve oyun kalitesini destekleyebilir. 🧩 Taktikler, rakip kalitesi, takım rotasyonu gibi birçok değişken maç sonuçlarını etkiler—koşu her şeyi açıklamaz. Referans: "Zaman, elit futbolda bir KPI olarak koşuyu bırakma zamanı: maç günü önkoşulları olarak futbol kondisyonu ve tazelik" Mauro Mandorino, Mathieu Lacome, Raymond Verheijen & Martin Buchheit (2025)

39名のエリート・オーストラリアン・フットボール（AF）のマッチプレーにおける精神的疲労（MF），パフォーマンスを25週間にわたり評価 MFの増加は試合中の走強度，低速度での走行，PlayerLoad™強度の減少と関連 pubmed.ncbi.nlm.nih.gov/4021… 続く

PlayerLoad This is a metric that can give you a lot of 'bang for your buck' It can have strong correlations with total distance, depending on how much running is involved versus other actions such as collisions Watch this course on GPS Devices 👇 bit.ly/3X6EBcT

Metrics that matter here: 1. Time on feet/total volume 2. HSD (>90%) 3. Explosive efforts (H Accel HDecel H COD R H COD L) 4. Playerload 5. Band 70-90% (can break it up even further with 70-80 and 80-90 if you want)

How can GPS metrics be grouped by what they're telling us? Research shows that some metrics are associated with one another (e.g. total distance and PlayerLoad). As you can see in this breakdown by @catapultsports, they also fall into the same category bit.ly/4bLzmUS

Our latest paper is now online! 💫 Authors ✍: Laura Dawson, @MarcoBeato1, @dr_g_devereux, and @biomechstu Read about the validity and reliability of PlayerLoad, Body Load, and Dynamic Stress Load metrics 🦾 Link here🔗 rb.gy/9kc4tu #LoadMonitoring #WearableTech

"GPS units with IMUs can detect changes in movement strategy caused by fatigue. Examining individual accelerometer vectors, like PlayerLoad, helps identify fatigue that doesn't show in distance or speed metrics" @BenHorsley89 sportsmith.co/articles/can-w…

PlayerLoad This is a metric that can give you a lot of 'bang for your buck' It can have strong correlations with total distance, depending on how much running is involved versus other actions such as collisions Watch this new course on GPS Devices 👇 bit.ly/3X6EBcT

47% of practitioners report finding accelerometer-derived metrics (e.g. PlayerLoad, Dynamic Stress Load) easy to understand But only 6% could provide a definition judged to be 'good' Those giving 'incorrect' or 'poor' definitions still rated their understanding as 6-7 out of 10

How can GPS metrics be grouped by what they're telling us? Research shows that some metrics are associated with one another (e.g. total distance and PlayerLoad). As you can see in this breakdown by @catapultsports, they also fall into the same category bit.ly/4bLzmUS

sRPE load vs HRZ vs PlayerLoad for measuring training load in young team handball players #workload #athletemonitoring journals.lww.com/nsca-jscr/a…