Franco Impellizzeri is Rob’s guest on this week’s Pacey Performance Podcast. He’s been Professor in Sport and Exercise Science and Medicine at the UTS Faculty of Health since 2018, with around 170 publications in the area of sport science. However, Franco is much more than just an academic – he has hands-on experience in training several elite track and field athletes in various disciplines (including two gold medallists at the 2000 Sydney Olympic Games). More recently, Franco has been the fitness coach of the Swiss Fencing Team (2012 to 2016) for the preparation of the Olympic Games in Rio de Janeiro.
Franco is here to mainly discuss the Nordic hamstring exercise, including the controversy around his recent meta-analysis. With information that could be invaluable to other trainers and coaches, Franco uncovered several errors in another recent meta-analysis in this area, and shares what his re-analysis means for practitioners. This includes statistical errors and the high risk of biases in such research.
There’s also a discussion into where trainers and coaches can find higher quality research, and what Franco will be up to next. In addition, Franco also tells us about his thoughts on the acute chronic workload ratio, and how it should be used in practice whilst being mindful of the problems what can be encountered using it. Finally, he talks about the issues with using the worst-case scenario as a training benchmark and that can be counteracted. For all this insight, hit the play button now.
This week’s topics:
- The errors in a recent meta-analysis on the Nordic hamstring exercise
- Statistical errors and the high risk of bias in Nordic research
- Why Franco re-analysed Nordic hamstring for his recent meta-analysis
- What Franco’s re-analysis means for practitioners
- Why the statistic that Nordics reduces injury by a given percentage is unrealistic
- How the chronic workload ratio is used in practice
- Problems encountered when using ratios
- The issues with using the worst-case scenario as a training benchmark
