Training Theory: What is Periodisation?

Many athletes and coaches ask whether periodisation is evidence informed and how it was derived. This is an important question, and some of the answers may be useful in deciding on the overall approach to training you adopt. Periodisation is the name given to training planning used initially in the Eastern Block (Russia and East Germany) in the 1970s and 80s. There were subsequent proponents in other western countries (e.g., Canada) who popularised the term and the approach. If one thinks back to that era, things were very different. For example, the number of scientific studies on training adaptations and responses were limited, and the technology in laboratories was fairly basic, not to mention the complete absence of wearable technology able to monitor movement and physiological responses in real time in the field. Also, culturally in the Eastern Bloc, it was seemingly acceptable to train athletes very hard to the point where only a few were able to tolerate/survive it. There were many successes, but at the expense of far more athletes for whom the training was unsuitable. In general terms, periodisation is simply a method of planning athletes’ seasons, and even four-year Olympic cycles. Although the detail was great, giving an impression of a scientific approach, there was virtually no empirical evidence to support the approach; rather, it was derived from trial and error, with plenty of error! In the most basic terms, the approach was to split the season into three phases: a preparation, a pre-competition or training phase, and a competition phase. These were then followed by an off season. At this level, this is all sensible and intuitive, allowing athletes to prepare for the main training phase by building a baseline level of conditioning, then a training phase, leading into the main competitions of the season where training is reduced in volume while maintaining intensity. At the next level, terms such as macro-, meso-, and micro-cycles were used, breaking these phases down into smaller planning periods. Micro-cycles were the smallest weekly cycle, etc. Everything was geared to eliciting the greatest training adaptations and performance gains by loading the athlete with training sessions and promoting recovery. Of course, that era was also known as being one where drugs were used to enhance recovery and adaptation (e.g., anabolic steroids). Little was known about overtraining, and the dangers of chronic overtraining syndrome, with large numbers of athletes experiencing the serious symptoms we now know well. There are, however, some enduring sensible aspects that are now better understood and form the basis for training theory. At the lower level, athletes benefit from short cycles where training load is high, followed by a rest day. Within these cycles, the training is arranged to get the more intense sessions completed early in the cycle to ensure quality, with the lower intensity training later in the cycle. At the higher level, tapering is a well understood and evidence informed approach building up to race day, where intensity and quality is maintained, but volume is significantly reduced, allowing the athlete to recover fully but remain ‘sharp’ for the race. In many cases, athletes are completing more than one race in a series, and volume is kept low for a few weeks. Progression is a critical factor, especially in the early preparation phase, in avoiding injury and illness while ensuring conditioning and fitness improves. Such factors are the mainstay of modern training theory. Technology and understanding is moving on rapidly, and the ‘big data’ collected, including on an individual athlete basis, through wearable technology, enables new insight into what works and what doesn’t for each individual. So, the overall, higher level principles remain the same, but new insight into individualised responses to exercise sessions and training cycles now allows coaches to tailor the training to the individual. As well as individual parameters (e.g., Heart Rate Variability) giving insight in real time, we are now in an era where multiple parameter responses are integrated and analysed in real time to give overall indicators (e.g., fitness, fatigue, etc.). Machine Learning will increasingly be able to ‘learn’ from the individual athlete responses and performances, and improve the ability to ‘predict’.