In summary: Analyse movement using biomechanical principles. Use technology to measure performance. Apply findings to improve technique Key exam tip: For your practical investigation, select a clearly defined phase of a skill (e.g. take-off in long jump) and systematically link each biomechanical principle to that phase.
Exam Tips for Scientific Principles of Sports Performance (Internal Assessment)
For your practical investigation, select a clearly defined phase of a skill (e.g. take-off in long jump) and systematically link each biomechanical principle to that phase.
When presenting data, compare your performer against a technical model or normative data; explicitly state the implications of any differences for efficiency, safety, and performance.
In your write-up, use precise terminology (e.g. 'angular velocity', 'moment of inertia') and show how you triangulated video with force data where possible.
Always use full biomechanical terminology (e.g., 'transverse plane' not 'horizontal plane') to demonstrate precision
Provide specific, named sporting examples for each law or principle — generic statements lose marks
Structure movement analysis answers clearly: identify joints, lever systems, planes, axes, and muscular involvement
When discussing Newton's laws, explicitly state the law and then show its application; do not just describe the motion
In internal assessments, include diagrams or video analysis with annotations to support biomechanical explanations
Common Mistakes
Confusing kinetic (forces) with kinematic (motion) variables, or applying principles incorrectly (e.g. mislabeling lever classes in a sporting action).
Insufficient or inconsistent data collection (e.g. only one trial, poor camera angle), leading to unreliable analysis.
Summarising results without interpreting what they mean for performance, or offering vague, unsupported coaching points like 'bend knees more' without biomechanical justification.
Neglecting to consider the interaction of multiple biomechanical factors (e.g. force, timing, coordination) when proposing improvements.
Confusing planes and axes (e.g., associating sagittal plane with frontal axis)
Incorrectly identifying the lever class in a sporting action, especially mixing up 1st and 3rd class levers
Marking Points
Award credit for accurate identification and application of relevant biomechanical principles (e.g. Newton's laws, levers, linear/angular motion) to the chosen movement.
Evidence of competent use of technology (e.g. video analysis, force platforms) with appropriate calibration, data collection protocols, and attention to ethical considerations.
Critical evaluation of collected data, linking quantitative findings directly to the performer's strengths and weaknesses in technique.
Clear, justified recommendations for technique modification that are firmly grounded in biomechanical reasoning and directly address the performance issues identified.
Award credit for accurately identifying the plane and axis for a given movement (e.g., sagittal plane, transverse axis in a bicep curl)
Expect clear explanation of the lever system (1st, 2nd, 3rd class) with correct placement of fulcrum, effort, and load
Look for application of Newton's laws with precise sporting examples (e.g., law of inertia in a sprint start, action-reaction in swimming)
Credit identification of impulse and its effect on momentum, with use of force-time graphs if appropriate
Overview of Scientific Principles of Sports Performance (Internal Assessment)
The Internal Assessment (IA) for the CCEA A-Level Physical Education course on 'Scientific Principles of Sports Performance' is a practical investigation where you apply scientific concepts to analyse and improve an aspect of your own or another's sports performance. This component is worth 20% of your final A-Level grade, making it a significant opportunity to demonstrate your understanding of physiology, biomechanics, and psychology in a real-world context. You will design, conduct, and write up a scientific experiment, typically involving data collection on variables like heart rate, force production, or reaction time, and then evaluate the results in relation to theoretical principles.
This topic matters because it bridges the gap between theoretical knowledge and practical application, a key skill for careers in sports science, coaching, or physiotherapy. By completing the IA, you learn how to formulate hypotheses, control variables, and critically analyse data—skills that are transferable to university study and professional practice. The IA also allows you to explore a specific area of interest, such as the effect of warm-up protocols on sprint performance or the relationship between flexibility and injury risk, giving you ownership over your learning.
Within the wider A-Level course, the IA draws on content from all three scientific principles: physiological factors (e.g., energy systems, cardiovascular responses), biomechanical factors (e.g., levers, projectile motion), and psychological factors (e.g., arousal, motivation). It is typically undertaken after you have covered the core theory, as you need a solid foundation to design a valid investigation. The IA is submitted as a written report, usually around 2000-3000 words, and is marked internally before moderation by CCEA.
Frequently Asked Questions
How do I choose a good research question for my IA?
Start by identifying a practical problem or observation from your own sport, such as 'Does the type of warm-up affect my sprint start?' Then, narrow it down to a specific, measurable variable. Ensure you have access to equipment (e.g., timing gates, heart rate monitors) and participants. Check that your question links to at least one scientific principle from the course. For example, 'The effect of different warm-up intensities on heart rate recovery after a 400m run' links to physiology.
What statistical test should I use for my IA?
The most common test is the independent t-test if you are comparing two different groups (e.g., static vs. dynamic warm-up), or a paired t-test if you are comparing the same group before and after an intervention (e.g., pre-test and post-test). If you have more than two conditions, use a one-way ANOVA. Always check assumptions (e.g., normal distribution) and report the p-value. If you're unsure, consult your teacher or use online resources like Laerd Statistics.
How many participants do I need for my IA?
Aim for at least 10-15 participants per group for a between-subjects design, or 10-15 participants total for a within-subjects design (repeated measures). This gives enough data for meaningful statistical analysis while being manageable. Ensure participants are similar in age, fitness level, and experience to control for confounding variables. If you cannot get enough participants, consider a case study approach with detailed qualitative data.
Can I use myself as a participant in the IA?
Yes, you can use yourself as a single participant in a case study design. This is common if you are investigating a personal training programme or a specific technique. However, be aware that results cannot be generalised to others. You must still follow ethical guidelines (e.g., not causing harm) and ensure your method is rigorous. For example, you could measure your own heart rate during different interval training sessions over several weeks.
How do I write the evaluation section of my IA report?
In the evaluation, critically assess the strengths and limitations of your study. Discuss validity (e.g., did you measure what you intended?), reliability (e.g., were results consistent?), and sources of error (e.g., timing inaccuracies). Suggest improvements, such as using more precise equipment or a larger sample. Then, reflect on the implications of your findings for sports performance and how they link to theory. For example, 'The results support the theory that dynamic warm-ups enhance muscle temperature and nerve conduction velocity, leading to improved sprint performance.'
What are common mistakes in the IA that lose marks?
Common mistakes include: not stating a clear hypothesis, failing to control variables (e.g., different times of day for testing), using inappropriate statistical tests, presenting raw data without analysis, and not linking conclusions to scientific principles. Also, avoid plagiarism—always reference sources using a consistent style (e.g., Harvard). Finally, ensure your report is well-structured with sections like Introduction, Method, Results, Discussion, and Conclusion.