Statistical modelling of training and performance using power output and heart rate data collected in the field

This thesis develops statistical models of performance and training that make use ofpower output and heart rate data. These data were collected during training andcompetition, and were recorded every five seconds using a power meter and heart ratemonitor. Using these data, we estimate the parameters of the Banister model of trainingand performance. In principle, knowledge of these parameters allows one to providequantitative decision support for the scheduling of training in advance of a majorcompetition. The methodology proceeds in a number of steps. In the first, measures of both trainingand performance must be specified. The training experienced by an athlete in a singlesession, the training load, can be measured in a number of ways. We use the TRIMPmeasure. This measure in its simplest form is essentially the total number of heart beats ina training session. Then the training loads of successive sessions are accumulated into asingle measure of training up to time t. This we term the accumulated training effect (attime t). Performance during a session at time t is defined as a function of the power outputobserved during the session. We consider various performance measures and describethese in detail in the thesis. Then in the second step, we relate the performance at time t tothe training load up to time t using a regression model, estimating the parameters of theperformance training relationship. The final step is the training optimisation step, wherebythe known training-performance model parameters can be used to specify training loads upto time T that will maximise (in expectation) the performance at time T. We demonstrate the methodology using the training data histories of ten competitivemale cyclists. As each athlete has his own specific characteristics, we should focus onoptimising training and performance individually. We compare and contrast the differentperformance measures that we propose. Our principal findings are that: Banister model parameters can be estimated; that thedifferent performance measures yield different Banister model parameter estimates andtherefore that the performance measure specification is a matter for athlete/coach choice;and that finally the Banister model has a serious shortcoming for the optimisation oftraining. The articulation of this shortcoming is an important contribution of this thesis

Level dc:type.qualificationlevel

Doctoral (Level 8)

Year dc:date.issued

2026

Author dc:creator

• Al-Otaibi, NM

Language dc:language

en

Identifier

oai:salford-repository.worktribe.com:1384859

OAI identifier oai:identifier

oai:salford-repository.worktribe.com:1384859