The role of technology in physical activity and sport — AQA A-Level Physical Education

This topic examines how technology influences physical activity and sport, from elite performance to mass participation. You will study devices such as GPS vests, video analysis software, wearable monitors, and decision-review systems like VAR and goal-line technology, as well as the role of social media and apps in promoting exercise. The focus is on evaluating both the benefits and the drawbacks of these technologies for performers, coaches, officials and spectators.

Understanding this topic is essential because technology is increasingly embedded in all levels of sport, and exam questions often ask you to analyse its impact on fairness, cost, injury prevention, and participation. It links closely to biomechanics, sports psychology, and socio-cultural issues such as commercialisation and inequality. Being able to discuss technology critically will help you access the highest marks in evaluation questions.

• →Performance analysis technology: GPS, video analysis, and motion tracking provide objective data on movement, technique and tactics, enabling targeted feedback and improved performance.
• →Wearable technology: devices such as heart rate monitors and accelerometers track physiological data, helping athletes train at correct intensities and monitor recovery, but accuracy and cost can be issues.
• →Decision-review technology: systems like Hawk-Eye, goal-line technology and VAR aim to improve officiating accuracy and fair play, but can slow the game and reduce the authority of officials.
• →Participation and health technology: apps, social media and fitness trackers can increase motivation and access to exercise, but may also create unrealistic body image pressures and exclude those without digital access.
• →Ethical and socio-economic considerations: technology can widen the gap between rich and poor athletes or clubs, raise privacy concerns over data, and challenge the spirit of sport if used to gain unfair advantage.

• Identifies types of data analysis, such as quantitative performance statistics and qualitative observational or video analysis.
• Describes collection methods, including notational analysis, video analysis, GPS or heart-rate monitoring and simple recording sheets.
• Explains how data is processed and interpreted to identify strengths, weaknesses and trends in performance.
• Explains how findings are used to set targets, adjust training, refine tactics or monitor recovery, thereby optimising performance.
• Recognises the role of re-testing or comparison over time to judge whether interventions have worked.
• Acknowledges limitations, such as inaccurate recording, data overload or statistics that omit tactical context.
• Selects a named technology appropriate to a stated physical activity or sport, for example video motion analysis, GPS tracking, force plates, heart-rate monitoring or performance-analysis software.
• Justifies the selection by linking the technology to the specific performance question, such as improving technique, monitoring workload or measuring physiological response.
• Explains the type of data the technology produces and how that data is interpreted to inform feedback or training decisions.
• Explains how the analysis and feedback could optimise performance, for example by correcting technique, adjusting training load or targeting fitness development.
• Acknowledges at least one limitation of the chosen technology, such as cost, access, reliability, or differences between laboratory and competitive settings, and explains how this affects its usefulness.
• Uses accurate subject terminology and applies the justification to a concrete sporting context rather than describing the technology in the abstract.
• Identifies a named technology used in sports analytics, such as video motion analysis, GPS tracking, heart-rate monitoring, force plates or performance-analysis software.
• States accurately what that technology measures or records, for example distance and speed, heart rate, ground reaction force, or movement patterns.
• Explains how the data produced is used to inform feedback, training or tactical decisions.
• Distinguishes between technologies by purpose, for example technique analysis compared with physiological monitoring or match statistics.
• Recognises a limitation of sports analytics technology, such as cost, access, reliability or the difference between laboratory and competitive conditions.
• Quantitative data is numerical and measurable, for example distance in metres or heart rate in beats per minute.
• Qualitative data is descriptive and judgement-based, for example a coach's observation of technique or body position.
• Objective data is collected with minimal human interpretation, typically by a device such as timing gates or a GPS unit.
• Subjective data relies on human interpretation, such as a coach's rating of a performance.
• Validity concerns whether data measures what it claims to measure; reliability concerns whether repeated measurements give consistent results.
• A measurement can be reliable yet invalid, so consistency alone does not guarantee that the data is meaningful.
• Video records performance so it can be replayed, paused and reviewed after the event.
• Analysis programmes allow frame-by-frame viewing, drawing of angle lines and comparison with a model performance.
• Tagging and coding functions let a coach mark key events and generate counts or clips of them.
• Video analysis supports qualitative technique judgement and can also produce quantitative data such as successful first-serve counts.
• Benefits include objective evidence, a permanent record and feedback delivered away from competition pressure.
• Limitations include cost, time to film and code, camera angle restricting the view, and possible over-dependence on the technology.
• Identifies a metabolic cart as equipment for indirect calorimetry that measures oxygen uptake and carbon dioxide production.
• Explains that indirect calorimetry estimates energy expenditure from respiratory gas exchange rather than measuring heat directly.
• States that the cart records ventilation and gas concentrations and uses software to calculate oxygen consumption and respiratory exchange ratio.
• Applies the equipment to sport or exercise contexts such as aerobic capacity testing, training-intensity prescription or monitoring recovery.
• Recognises limitations: need for calibration, steady-state exercise, a good mask seal and controlled environmental conditions.
• Identifies GPS and motion-tracking hardware such as satellite receivers, accelerometers and gyroscopes worn by performers.
• Explains that software records and analyses movement variables including distance, speed, acceleration, changes of direction and player load.
• Applies the data to coaching decisions such as training load management, tactical analysis, fatigue monitoring and injury-risk reduction.
• Recognises limitations: signal interference, sampling rate, device placement and estimation of some movements.
• Discusses ethical considerations including consent, data ownership and surveillance of athletes.
• Defines data integrity as the accuracy, completeness and consistency of performance data over its entire lifecycle.
• Identifies collection-stage threats to integrity, such as faulty sensors, calibration errors or manual entry mistakes.
• Explains storage and access controls, including secure servers and restricted permissions, to prevent unauthorised alteration of data.
• Describes verification methods such as cross-checking devices, reviewing logs and cleaning data before conducting performance analysis.
• Links integrity failures to negative consequences for coaching decisions, such as over-training an athlete due to incomplete load data.
• Describes the collection and organisation of performance data from matches, training or wearable devices.
• Explains pattern recognition, such as identifying strengths, weaknesses or recurring tactical events.
• Outlines comparison of athletes or teams using objective indicators like distance covered or pass completion.
• Describes monitoring of workload, recovery and injury risk over time.
• Explains how analytics informs coaching decisions, talent identification or evaluation of interventions.
• Technology such as heart-rate monitors, GPS vests and wearable trackers collects objective fitness data during training or competition.
• Data is compared with the performer's baseline or the demands of the sport to judge current fitness.
• Interpreted data informs decisions about training intensity, volume, recovery or readiness.
• Monitoring supports individualised training and can track progress over time.
• Feedback from monitoring is only valuable when it is analysed and acted upon, not simply recorded.
• Video and motion-analysis technology records technique so it can be reviewed in detail, including slow motion.
• Performers compare their movement with a technical model or previous performance to identify faults.
• Feedback from analysis is used to set specific corrections and shape subsequent practice.
• Repeated practice of the corrected movement develops consistency of skill and technique.
• Technology supports skill development but the performer and coach must interpret the feedback and act on it.
• Identifies a named technology used in injury prevention, such as GPS tracking, video analysis, motion capture or force plates.
• Explains the data the technology provides, for example distance covered, sprint count, joint angle or ground reaction force in newtons (N).
• Links the data to a specific injury risk, such as overuse from excessive training load or joint stress from poor landing mechanics.
• Describes the preventive action taken, such as reducing training volume, altering technique or prescribing rest, and explains how this lowers injury risk.
• Distinguishes prevention from rehabilitation, recognising that prevention occurs before injury while rehabilitation follows it.
• Identifies a named game-analysis technology, such as video analysis, notational analysis, performance-analysis software or GPS.
• States the type of data the system records, for example pass completion, tackle count, distance covered or top speed in metres per second (m s⁻¹).
• Explains how the data is used to evaluate individual or team performance, such as identifying a player's strengths and weaknesses.
• Explains how the data informs a tactical or coaching decision, such as altering formation, marking or substitution strategy.
• Recognises that game analysis supports both live in-game decisions and post-match review.
• Talent identification is the process of recognising performers with the potential to excel in a sport.
• Scouting is the systematic search for talented performers, typically carried out by clubs, academies or governing bodies.
• Technology such as timing gates, GPS tracking and video analysis provides objective performance data that can support talent identification.
• Databases and performance-tracking software allow long-term monitoring of a performer's development.
• Technology can widen the talent search beyond local leagues but may miss performers if data are incomplete or biased.
• Effective practice combines technological data with expert observation and judgement.
• Developments in equipment include new materials such as carbon fibre, lightweight alloys and synthetic surfaces.
• Facility developments include all-weather pitches, floodlighting, indoor centres and accessible design features.
• Improved equipment can raise performance through greater speed, power, accuracy or consistency, for example carbon-fibre rackets or synthetic tracks.
• Better facilities can increase participation by making activity more available, comfortable and safe, for example floodlit courts or inclusive gyms.
• Cost, maintenance and unequal access can restrict participation despite technological advances.
• Some developments raise fairness or safety concerns, such as differing access to advanced equipment between competitors.
• The impact of a development depends on the sport, the level of participation and the resources available.
• Material technology uses engineered materials such as carbon fibre, titanium, composites and polymer foams to change equipment mass, stiffness, strength or shock absorption.
• Adaptation for disability includes prosthetics such as carbon-fibre running blades, seated throwing frames, sports wheelchairs and handcycles.
• Adaptation for age includes lighter rackets, softer or lower-compression balls, smaller bats and reduced-size equipment for children, and lighter, lower-impact equipment for older participants.
• The purpose of adaptation is to match the physical demand of the activity to the performer, which widens access and can raise performance.
• A correct answer links a named material property to a specific adaptation and its effect on the participant.
• Olympic legacy is the lasting effect of hosting a Games, including venues, participation, transport, elite training and economic or social outcomes.
• Legacy can be positive, such as continued community and elite use of a venue, or negative, such as high maintenance costs or underused facilities.
• Surfaces are engineered for a sport's demands: synthetic turf with infill, mondo athletics track, sprung wooden floor for gymnastics and sprung courts for basketball.
• Multi-use facilities share space between activities using movable seating, retractable dividers or interchangeable surfaces, increasing usage and spreading cost.
• A correct answer links a facility feature to its effect on participation, performance or cost.
• Identifies distinct roles of technology, such as performance analysis, officiating, coaching, rehabilitation, equipment design and spectator engagement.
• Explains a positive impact with a specific sporting example, for example improved decision accuracy through goal-line or video review systems.
• Explains a negative impact with a specific sporting example, for example disrupted match flow, cost barriers or unequal access at grassroots level.
• Analyses the effect on athletes, coaches, officials or spectators, showing how the technology changes behaviour or outcomes.
• Weighs positive against negative impacts and reaches a supported overall judgement rather than listing points.
• Uses accurate subject terminology such as performance analysis, data, classification, integrity and accessibility.
• Explains what technology is and identifies the performer as the participant who trains and competes.
• Gives at least one specific sporting example of technology used by a performer, such as GPS tracking in football or motion capture in tennis.
• Explains the mechanism: how the technology produces data or feedback that changes training or performance.
• Analyses benefits for the performer, such as objective feedback, injury prevention, motivation or refined technique.
• Analyses limitations for the performer, such as cost, unequal access, data privacy, over-reliance or reduced spontaneity.
• Reaches a supported judgement about the overall effect of technology on the performer, using the examples given.
• Explains what technology is and identifies the coach as the person who plans, delivers and reviews training and competition.
• Gives at least one specific sporting example of technology used by a coach, such as video analysis software in rugby or a training-load app in athletics.
• Explains the mechanism: how the tool produces information that changes a coaching decision or session.
• Analyses benefits for the coach, such as objective evidence, time saved, individualised feedback or better long-term planning.
• Analyses limitations for the coach, such as cost, learning time, data overload or loss of contextual judgement.
• Reaches a supported judgement about the overall effect of technology on the coach, using the examples given.
• Explains how broadcast, streaming and mobile technology expand the audience for sport beyond those attending in person, giving a global reach.
• Explains how social media and digital clips increase engagement, allowing fans to follow athletes, share opinions and interact with sport between events.
• Explains how a larger audience generates commercial income through subscriptions, advertising, sponsorship and merchandise, which can be reinvested in the sport.
• Explains drawbacks such as paywalls and subscription costs excluding some fans, illegal streaming reducing revenue, and online abuse affecting performers' wellbeing.
• Uses a specific sporting example to show how technology has changed what the audience can see or do, such as enhanced replays, multiple camera angles or live statistics.
• Reaches a supported judgement about whether the changes to the audience are, on balance, positive or negative for the sport and its followers.

• 💡Read the question stem carefully to check whether it asks for types, uses or both.
• 💡Eliminate options that describe technology generally rather than data analysis specifically.
• 💡Where an option links data to a concrete action such as adjusting training, it is more likely to describe optimisation.
• 💡Check that any option claiming data guarantees success is treated with caution, since interpretation is required.
• 💡Anchor your answer in one named sport and performer so every justification is concrete and checkable.
• 💡Structure the response as selection, data produced, interpretation, performance impact and limitation.
• 💡Use correct terminology such as validity, reliability, feedback and workload, and apply each term to your example.
• 💡Reserve time to state a limitation and explain how it constrains the value of the analysis.
• 💡Read each option and check it against the specific data output of the technology named in the question.
• 💡Eliminate options that describe a different technology's function before choosing.
• 💡Where two options seem similar, choose the one that correctly links the technology to its use in improving performance.
• 💡Revise technologies in pairs of what it measures and how the data is used.
• 💡Define each term precisely before applying it to a sporting example, so quantitative, qualitative, objective and subjective are clearly distinguished.
• 💡Use a paired example such as timing gates (objective, quantitative) against a coach's technique rating (subjective, qualitative) to show the contrast.
• 💡When asked about validity or reliability, state what the data claims to measure and whether repeated trials would give the same result.
• 💡Name a specific function of the programme, such as frame-by-frame playback, angle drawing or event tagging, rather than saying only that it records video.
• 💡Balance benefits with limitations such as cost, time and camera angle to show evaluation rather than description.
• 💡Link the technology to a clear purpose, for example improving a tennis serve or analysing tactical movement in a team game.
• 💡Link the equipment to a named use, such as determining maximal oxygen uptake or prescribing aerobic training zones, rather than describing it in isolation.
• 💡Use correct units and notation when quoting values, for example dm³·min⁻¹ or cm³·kg⁻¹·min⁻¹, and keep the superscript in cm³.
• 💡When evaluating, give one strength and one limitation with a consequence, such as accurate energy-expenditure data but limited ecological validity in team sports.
• 💡Name the specific variable being measured, such as distance covered or number of accelerations, rather than saying 'tracking movement' generally.
• 💡Link each piece of data to a coaching or performance decision, for example using high-speed running distance to adjust recovery.
• 💡When evaluating, state a limitation and its consequence, such as signal loss in indoor arenas reducing the reliability of distance data.
• 💡In multiple-choice questions, eliminate options that confuse integrity with confidentiality, consent or data quantity.
• 💡Look for the option that names a specific control for accuracy, such as calibration, secure storage or verification, rather than a vague benefit.
• 💡Check whether the scenario describes a collection, storage or analysis problem, because each points to a different measure for maintaining data integrity.
• 💡In multiple-choice questions, choose the option that names a specific analytical function, such as pattern recognition or workload monitoring, rather than a general benefit.
• 💡Reject options that describe technology hardware without saying what the data are used for.
• 💡When a scenario is given, match the function to the decision the coach needs to make.
• 💡Link each piece of technology to the specific fitness component it measures, such as heart rate for aerobic capacity.
• 💡Use a named sporting example to show how data changes a training decision.
• 💡Explain the interpretation step, because marks reward understanding of how data informs performance, not just the device name.
• 💡Name a specific technology, such as video analysis or motion tracking, and the technique it develops.
• 💡Explain the sequence: record, compare with the model, identify the fault, correct and repeat.
• 💡Use a sporting example to show how feedback changes what the performer practises.
• 💡Read the question stem carefully to check whether it asks for a technology, a risk factor or a preventive action, and answer the part actually asked.
• 💡When evaluating options, reject any that name a technology with no plausible mechanism for reducing injury risk.
• 💡Use precise terminology such as training load, ground reaction force and technique correction rather than vague words like 'helps' or 'good'.
• 💡Check whether the question asks about the technology, the data or the decision, and select the option that matches that focus.
• 💡Reject options that describe a technology with no clear link to analysing game performance.
• 💡Use accurate terms such as notational analysis, performance profile and tactical decision rather than general words like 'watching' or 'looking'.
• 💡Read the question carefully to check whether it asks about talent identification, scouting or both.
• 💡Link each technological example to a specific sporting context, such as sprint gates in athletics or GPS vests in football.
• 💡When evaluating, give both a benefit and a limitation of using technology in talent identification.
• 💡Use precise terminology such as objective data, databases and performance tracking rather than vague phrases like 'computers help'.
• 💡Structure each paragraph around one development, then explain its participation or performance impact with a concrete example.
• 💡Use comparative language such as 'this increases' or 'this limits' to make the impact explicit.
• 💡Include both benefits and limitations to reach the higher bands of evaluation.
• 💡Refer to specific sports and equipment, such as carbon-fibre bikes in cycling or 3G pitches in football, rather than generic statements.
• 💡Name the material and the property, then state the adaptation and its effect on the performer in one chain.
• 💡Use a concrete example such as a carbon-fibre running blade or a lighter junior racket rather than a vague reference to better equipment.
• 💡Check whether the question asks about disability, age or both, and make sure your chosen option covers the clause named.
• 💡Link each facility feature to a clear effect on participation, performance or cost rather than describing it alone.
• 💡Use a named surface and state the property it provides, such as grip, shock absorption or consistent bounce.
• 💡When judging legacy, give both a positive and a possible negative outcome to show balanced understanding.
• 💡Plan two or three roles of technology and ensure each has both a positive and a negative impact before writing.
• 💡Use specific sporting examples to support each impact, then link back to the question wording.
• 💡Reserve time for a short conclusion that weighs the evidence and states a justified overall view.
• 💡Before reading, set three headings: role of technology, positive impact, negative impact, and sort notes under them.
• 💡Highlight any sporting example and label which role it illustrates so it can be reused in an extended response.
• 💡After reading, write one sentence that weighs a positive against a negative impact to practise evaluative thinking.
• 💡Anchor every point to a named sport and a named piece of technology so the answer stays concrete.
• 💡Use connectives such as because and therefore to show how data leads to a change in performance.
• 💡Reserve time to write a short conclusion that directly answers the question about the performer.
• 💡Name the coaching task first, then the technology, then the effect on the session or plan.
• 💡Use a range of examples across different sports to show breadth of understanding.
• 💡Finish with a clear judgement that answers the question about the coach rather than drifting into general comments about sport.
• 💡Plan two or three clear points before writing, each linking a named technology to a specific effect on the audience.
• 💡Support every point with a concrete sporting example, such as a global streaming service showing a major football league or an athlete building a following through social media.
• 💡Finish with a short judgement that weighs the benefits against the drawbacks, rather than stopping after a list of points.
• 💡Always name the specific technology and the sport or activity it is used in. Generic answers about 'technology' without context rarely gain high marks.
• 💡For evaluation questions, use a balanced approach: give at least one advantage and one disadvantage, then make a clear judgement. This shows the examiner you can weigh up evidence.
• 💡Use correct terminology such as 'performance analysis', 'real-time data', 'work-rate', 'injury prevention', 'fair play', and 'digital divide'. This demonstrates precise subject knowledge.

• Confusing data analysis with merely watching a recording; the correction is to state that the footage or figures must be recorded, interpreted and acted upon.
• Assuming all data is numerical; the correction is to recognise qualitative observation as a valid type alongside quantitative statistics.
• Believing data alone improves performance; the correction is to explain that a coach or performer must translate findings into changed training or tactics.
• Ignoring reliability of collection; the correction is to note that consistent definitions and accurate recording are needed for valid comparison.
• Describing how a technology works without justifying why it was selected for that performer and activity; the correction is to state the performance problem first, then explain why this tool answers it.
• Treating any technology as universally best; the correction is to match the tool to the context, since a laboratory tool may be impractical in competition.
• Ignoring limitations and presenting data as automatically accurate; the correction is to discuss reliability, cost, access and the difference between testing and match conditions.
• Listing several technologies without developing any one justification; the correction is to select one or two and justify them in depth.
• Confusing what different technologies measure, for example assuming GPS measures heart rate; the correction is to link each tool to its specific data output.
• Believing analytics data is automatically accurate and objective; the correction is to recognise issues of reliability, validity and context.
• Describing a technology without stating how the data improves performance; the correction is to connect data to feedback, training or tactics.
• Assuming all analytics technology is equally accessible; the correction is to consider cost and level of competition.
• Treating quantitative and objective as identical: quantitative data is numerical, while objective data is collected with minimal human judgement; a coach could count tackles numerically but still judge subjectively what counts as a tackle.
• Assuming reliable data is automatically valid: a faulty speed gun may give consistent readings that do not reflect true speed, so reliability must be checked separately from validity.
• Confusing qualitative with unreliable: descriptive observations can be systematic and useful, but they depend on the observer's interpretation and should be corroborated.
• Believing technology removes all subjectivity: someone still selects the device, the sampling rate and the variables recorded, so human decisions remain.
• Thinking video analysis only produces qualitative data: tagging and counting events also generates quantitative data such as the number of successful passes.
• Ignoring camera angle as a limitation: a single view can hide or distort a movement, so footage from more than one angle may be needed.
• Assuming the software makes the judgement: the programme supplies the images and data, but the coach or performer still interprets what they mean.
• Overlooking the time cost: filming, tagging and reviewing footage takes considerable time, which can limit how often it is used.
• Confusing indirect calorimetry with direct calorimetry: a metabolic cart estimates energy expenditure from respiratory gas exchange, whereas direct calorimetry measures heat loss in a chamber.
• Assuming the cart measures only oxygen: it also measures carbon dioxide production and ventilation, which are needed to calculate respiratory exchange ratio.
• Treating the cart as a field-based tool for match play: it is primarily laboratory-based because of the mask, tubing and calibration requirements.
• Ignoring calibration and steady-state conditions: without these, the recorded values are unreliable and cannot be compared across tests.
• Treating GPS as accurate for all movements: indoor or obstructed environments weaken satellite signals, and accelerometers estimate some actions rather than measuring them directly.
• Confusing GPS with motion tracking: GPS uses satellite positioning, while motion tracking uses inertial sensors such as accelerometers and gyroscopes, often combined in one unit.
• Assuming more data automatically improves performance: data must be interpreted alongside context, and poor interpretation can lead to inappropriate training loads.
• Overlooking ethical issues: collecting athlete movement data raises questions of consent, ownership and surveillance that must be addressed.
• Conflating data integrity with data privacy. Correction: integrity is about accuracy and completeness, whereas privacy and confidentiality are about who is allowed to see the data.
• Assuming all recorded data are automatically correct. Correction: sensors can fail or lose signal, so regular calibration and data cleaning are essential to maintain integrity.
• Ignoring the effect of missing data on analysis. Correction: incomplete data can distort load monitoring and selection decisions, leading to poor coaching interventions.
• Confusing analytics with simple video recording; correction: analytics involves systematic data collection and interpretation, not just filming.
• Believing analytics replaces the coach; correction: it provides evidence that the coach interprets alongside contextual knowledge.
• Thinking analytics only applies to elite professional sport; correction: it can be used at any level where data are recorded and reviewed.
• Assuming more data always improves decisions; correction: irrelevant or poor-quality data can mislead, so selection and integrity matter.
• Thinking that simply wearing a fitness tracker improves performance: the correction is that the data must be interpreted and used to change training.
• Assuming one reading gives a reliable picture of fitness: the correction is that trends across sessions compared with a baseline are more meaningful.
• Believing monitoring replaces the coach's judgement: the correction is that technology provides data the coach interprets alongside observation and context.
• Believing that recording technique automatically improves it: the correction is that the performer must analyse the footage, identify a fault and practise the correction.
• Confusing skill with technique: the correction is that technique is the mechanical execution of a movement, while skill is the learned ability to perform it consistently and effectively.
• Thinking technology replaces practice: the correction is that analysis informs practice, and improvement comes from repeated, refined rehearsal.
• Misunderstanding: any technology used in sport counts as injury prevention. Correction: the technology must be linked to reducing a specific injury risk, so a scoreboard or a performance-analysis app with no injury link is not an example of injury prevention.
• Misunderstanding: injury prevention technology only means protective clothing. Correction: monitoring and analysis technologies such as GPS, video and force plates are also central because they change training or technique before injury occurs.
• Misunderstanding: collecting data alone prevents injury. Correction: the data must be interpreted and acted upon, for example a coach reducing weekly distance after GPS shows an excessive acute load.
• Misunderstanding: prevention and rehabilitation are the same. Correction: prevention reduces the chance of an injury occurring, whereas rehabilitation supports recovery after an injury has happened.
• Misunderstanding: game analysis is the same as injury prevention. Correction: game analysis evaluates performance and tactics, while injury prevention focuses on reducing injury risk, even though both may use similar technologies.
• Misunderstanding: game analysis only means watching a replay. Correction: it involves systematic recording and interpretation of data, such as counts, percentages or distances, to support decisions.
• Misunderstanding: the technology makes the decision. Correction: the coach or analyst interprets the data and decides what action to take.
• Misunderstanding: game analysis applies only to elite professional sport. Correction: it can be used at any level where recording and reviewing performance is practical and useful.
• Confusing talent identification with talent development: identification is about finding potential, whereas development is about nurturing it once identified.
• Assuming technology replaces scouts: technology supplies data, but human judgement is still needed to interpret it and assess intangible qualities.
• Believing GPS and video data are always accurate: measurement error, poor calibration and limited match contexts can distort the picture.
• Thinking scouting only happens at elite level: grassroots and school-level scouting also use technology-supported methods.
• Describing a piece of equipment without linking it to participation or performance: always state the effect and give a sporting example.
• Assuming all technological developments increase participation: cost, location and maintenance can reduce access for some groups.
• Treating performance and participation as the same thing: performance concerns results and standards, while participation concerns who takes part and how often.
• Ignoring fairness and safety issues: unequal access to advanced equipment can create an uneven playing field.
• Thinking adaptation only helps disabled performers: correction – age-related adaptation, such as lighter junior rackets and softer balls, is equally part of material technology.
• Assuming lighter equipment is always better: correction – equipment must also be strong, stable and suited to the performer, so mass is balanced against control and safety.
• Confusing material technology with general training or coaching methods: correction – material technology concerns the design and manufacture of the equipment itself.
• Believing adapted equipment removes all disadvantage: correction – it reduces a specific barrier such as mass or instability but does not equalise every factor.
• Treating Olympic legacy as automatically positive: correction – legacy can be negative if venues are costly to maintain or fall into disuse.
• Confusing a surface with a venue: correction – the surface is the playing layer, such as synthetic turf or mondo track, while the venue is the whole facility.
• Assuming multi-use means the same surface suits every sport: correction – multi-use often requires interchangeable or protective surfaces and movable equipment.
• Ignoring cost and maintenance when judging legacy: correction – long-term funding and upkeep are central to whether a legacy succeeds.
• Listing technologies without explaining their impact; correction: for each technology, state the role it performs and then give one positive and one negative consequence.
• Assuming technology is always beneficial; correction: balance every advantage with a limitation such as cost, reliability, fairness or reduced spontaneity.
• Ignoring the effect on different groups; correction: consider athletes, officials, coaches, spectators and grassroots participants separately.
• Describing a device in vague terms without a sporting context; correction: anchor each point to a named sport or competition situation.
• Treating 'Sport' as a topic to define rather than a context for selecting material; correction: use it to filter reading towards sporting examples of technology.
• Copying long passages without identifying the role or impact; correction: summarise each point as role, example and positive or negative impact.
• Accepting unsupported claims about fairness or accuracy; correction: check whether the source gives evidence or reasoning before noting the point.
• Writing generally about technology in sport without linking it to the performer: correct by naming the participant and explaining how the device changes their training or competition.
• Listing devices without explaining how each one works: correct by describing the mechanism, for example how a GPS vest records distance and heart rate to inform training load.
• Treating technology as wholly positive or wholly negative: correct by weighing benefits against limitations and reaching a supported judgement.
• Confusing the coach's use of technology with the performer's use: correct by focusing on planning, delivering and reviewing sessions rather than the athlete's own experience.
• Describing a device without explaining the coaching decision it informs: correct by linking the data to a specific choice, such as changing a training drill or selection.
• Assuming technology always improves coaching: correct by weighing benefits against limitations such as cost, time and data overload.
• Writing only that technology makes sport 'more popular' without explaining the mechanism; the correction is to name the technology, such as live streaming or social media, and state exactly how it changes who can watch and how they engage.
• Treating the audience as only those inside the stadium; the correction is to recognise that the audience now includes remote and online followers, which is the main focus of this topic.
• Listing benefits only and ignoring drawbacks; the correction is to balance the answer with issues such as subscription costs, illegal streaming or online abuse, then judge the overall effect.
• Confusing the audience with the performer or official; the correction is to keep the focus on spectators and followers, using performers and officials only where technology affects how the audience experiences them.
• Students often think technology always improves fairness. In reality, decision-review systems can still involve human error, slow down play, and may not be available at all levels, creating inequality.
• Many assume wearable technology is always accurate. In fact, wrist-worn heart rate monitors can be less precise during high-intensity exercise, and calorie estimates are often rough.
• Some believe technology only benefits elite sport. However, apps and online workouts can significantly increase participation among the general public, though cost and digital literacy can limit access.

1. 1Step 1: Create a table of at least six technologies (e.g. GPS, video analysis, Hawkeye, Fitbit, VAR, social media apps) with columns for 'how it works', 'benefits', 'drawbacks' and 'sport example'.
2. 2Step 2: For each technology, write a short paragraph explaining its impact on either performance or participation, using subject-specific terminology.
3. 3Step 3: Practise 4-mark 'explain' and 6-mark 'evaluate' questions, using the balanced structure: point, evidence, explanation, judgement.
4. 4Step 4: Review mark schemes and examiner reports to identify common pitfalls, then rewrite one of your answers to improve it.
5. 5Step 5: Test yourself with active recall questions and flashcards, focusing on definitions and specific examples, until you can recall them without notes.

• 📋Short-answer 'explain' questions (2-4 marks): e.g. 'Explain how one technology can improve performance in a named sport.' Advice: be specific, name the technology and sport, and link to a clear benefit.
• 📋Evaluation questions (6-9 marks): e.g. 'Evaluate the impact of technology on fair play in sport.' Advice: present both sides, use examples, and reach a justified conclusion.
• 📋Data analysis questions: e.g. interpret a graph of GPS data to suggest training adaptations. Advice: read axes carefully, identify trends, and relate to physiological principles.
• 📋Extended essay questions (15 marks): e.g. 'Discuss the role of technology in increasing participation and performance.' Advice: plan your answer, cover multiple technologies, and integrate socio-cultural and physiological factors.