Dwarfs on the Shoulders of Giants: Bayesian Analysis With Informative Priors in Elite Sports Research and Decision Making

A kinetic analysis of four high velocity, horizontally focused step-up variations for acceleration training

Step-up variations are frequently used in sports performance to develop coordinated and powerful movements that transfer to running. This study aimed to quantify the kinetic characteristics of the first foot contact of four different step-up variations. Ten professional rugby league players participated in this study and performed the Barbell One Box Step-Up with Catch (BB1), Barbell Two Box Step-Up (BB2), Vest Two Box Run (VEST) and Step-Up Jump (JUMP) as part of routine in-season strength training sessions during one season. Peak force, total impulse and maximal rate of force development (RFD) were measured from first foot contact on the step-up box. Significantly greater peak force and RFD were observed in JUMP than any other variation (standardized mean difference; SMD: 3.9-5.5; p < 0.001). Total impulse was equal between JUMP and BB1, and significantly greater in JUMP than BB2 and VEST (SMD: 1.3-2.3; p < 0.001), and in BB1 than BB2 and VEST (SMD: 1.8-2.8; p < 0.001). Significantly larger peak force and RFD were observed in BB2 and VEST than BB1 (SMD: 0.6-0.7) and in total impulse in BB2 than VEST (SMD: 1.6) (p < 0.05). The results of this study highlight that step-up exercise variations maximize different kinetic characteristics, which may transfer differently to athlete running performance.

Why Humble Farmers May in Fact Grow Bigger Potatoes: A Call for Street-Smart Decision-Making in Sport

BACKGROUND

The main task of applied sport science is to inform decision-making in sports practice, that is, enabling practitioners to compare the expectable outcomes of different options (e.g. training programs).

MAIN BODY

The "evidence" provided may range from group averages to multivariable prediction models. By contrast, many decisions are still largely based on the subjective, experience-based judgement of athletes and coaches. While for the research scientist this may seem "unscientific" and even "irrational", it is important to realize the different perspectives: science values novelty, universal validity, methodological rigor, and contributions towards long-term advancement. Practitioners are judged by the performance outcomes of contemporary, specific athletes. This makes out-of-sample predictive accuracy and robustness decisive requirements for useful decision support. At this point, researchers must concede that under the framework conditions of sport (small samples, multifactorial outcomes etc.) near certainty is unattainable, even with cutting-edge methods that might theoretically enable near-perfect accuracy. Rather, the sport ecosystem favors simpler rules, learning by experience, human judgement, and integration across different sources of knowledge. In other words, the focus of practitioners on experience and human judgement, complemented-but not superseded-by scientific evidence is probably street-smart after all. A major downside of this human-driven approach is the lack of science-grade evaluation and transparency. However, methods are available to merge the assets of data- and human-driven strategies and mitigate biases.

SHORT CONCLUSION

This work presents the challenges of learning, forecasting and decision-making in sport as well as specific opportunities for turning the prevailing "evidence vs. eminence" contrast into a synergy.

The effectiveness of traditional vs. velocity-based strength training on explosive and maximal strength performance: A network meta-analysis

This network meta-analysis aimed at evaluating the effectiveness of different velocity-based (VBT) and traditional 1RM-based resistance training (TRT) interventions on strength and power indices in healthy participants. The research was conducted until December 2021 using the online electronic databases PubMed, Web of Science, PsycNet, and SPORTDiscus for studies with the following inclusion criteria: 1) controlled VBT trials, 2) strength and/or jump and/or sprint parameters as outcomes (c), participants aged between 18 and 40 years, and 4) peer-reviewed and published in English. Standardized mean differences (SMD) using a random effects models were calculated. Fourteen studies with 311 healthy participants were selected and 3 networks (strength, jump, and sprint) were achieved. VBT, TRT, repetitions in reserve (RIR), low velocity loss (lowVL), and high velocity loss (highVL) were ranked for each network. Based on P-score rankings, lowVL (P-score ≥ 0.59; SMD ≥ 0.33) and highVL (P-score ≥ 0.50; SMD ≥ 0.12) revealed favorable effects on strength, jump, and sprint performance compared to VBT (P-score ≤ 0.47; SMD ≤0.01), TRT (P-score ≤0.46; SMD ≤ 0.00), and RIR (P-score ≤ 0.46; SMD ≤ 0.12). In conclusion, lowVL and highVL showed notable effects on strength, jump, and sprint performance. In particular for jump performance, lowVL induced favorable improvements compared to all other resistance training approaches.