Welcome back, Coach.
Everything's unlocked — full study guide, 503 practice questions, timed mock exams, flashcards, case studies and your planner. Pick up where you left off.
Exam blueprint
Research Process is the heaviest crosscutting lens (35–41%) — weight your study accordingly.
Diagnostic placement test
40 questions sampled across all six areas in proportion to the exam. It takes about 30 minutes and produces a profile of your strengths and gaps, plus a study plan weighted to what you actually need.
The complete study guide
Two ways in: exam-domain notes with high-yield flags, or the full chapter-by-chapter breakdown of all 31 chapters of NSCA's Essentials of Sport Science.
Training Theory & Process
23–29%▸Why sport needs scientists
Sport adopted data later than other industries because of historical limits in acquiring and integrating new information — not a lack of value. The sport scientist exists to convert data into objective information that informs decisions, sitting inside the High Performance Unit (HPU) and interdisciplinary team (IDT).
A successful IDT runs on competent people, shared values and a collaborative physical environment. A shared mental model aligns behaviour; the sport scientist adds objectivity and scientific insight to decisions others make from experience.
The training load model
External load = the work performed (distance, speed, reps, GPS/force metrics). Internal load = the athlete's physiological and perceptual response to that work (HR, blood lactate, RPE). The internal response to a given external load is what actually drives adaptation — so the two must be interpreted together.
The dose–response is mediated by the athlete's training status; the same session produces different responses in different athletes. Load is quantified to track progression or regression, not to diagnose genetics or diet.
Both very high and very low loads associate with injury. The acute:chronic workload ratio has poor injury-prediction ability and mathematical-coupling problems — plan and measure progressions carefully, but don't use ACWR as a predictive gate.
Periodization — individual sports
Periodization is the logical, phased organisation of training toward a competition peak. Plans nest: multiyear → annual (macrocycle) → mesocycle → microcycle. The sport scientist supports the plan with objective data and adapts it to context rather than imposing a rigid template.
Tapering reduces load before competition to shed fatigue while retaining fitness, so the athlete arrives fresh. Volume is cut more than intensity.
Team sports & HIIT
Team preparation leans on high-intensity interval training (HIIT). Buchheit & Laursen frame HIIT around targeted physiological responses and a set of programming "weapons" (work interval intensity/duration, relief, series, between-series recovery, work modality) manipulated to bias aerobic, anaerobic or neuromuscular/musculoskeletal load. HIIT is then integrated into the weekly microcycle around matches.
Needs Analysis
24–30%▸Key performance indicators (KPIs)
A KPI is a quantifiable measure used to judge success against a performance objective — the concept is borrowed from business. Analysis works top-down: understand the demands of the sport (rules, equipment, schedule, physical/technical/tactical requirements), then assess each athlete against them to build individualised plans and situate them among peers.
KPIs come in types — technical, tactical, physical/physiological, psychological. Historical results databases and international federation records let you track progress, especially in CGS (centimetres–grams–seconds) sports where outcomes are directly measured.
Determinants of performance & the holistic model
A holistic needs analysis maps the factors that influence the competitive outcome and how movement outcomes trace back to biomechanical, physiological and psychological determinants. Rigid, one-size approaches have been criticised; evidence-gathering is what shifts fixed mindsets and established procedures.
Be cautious generalising research from lower-level athletes to elites — elite athletes have unique physiological phenotypes and exceptional skill, so findings may not transfer.
Profiling & benchmarking
Profiling characterises an athlete on relevant qualities; benchmarking compares that profile to a reference (norms, positional standards, the athlete's own history). Interpretation must account for maturation and long-term athletic development — biological age can diverge from chronological age, so norms are applied with care in youth.
Turn data into standardised scores (z-scores, T-scores, STEN) to compare across tests, and always attach uncertainty (confidence intervals) to a benchmark before acting on it.
Acute & Chronic Monitoring
25–30%▸Measuring external load
Camera systems and microtechnology (GPS/LPS, accelerometers, power meters) quantify external load. Higher sample-rate GPS (10 Hz > 5 Hz) is more valid and reliable for high-intensity efforts and accelerations. Deciding on absolute vs relative speed/acceleration thresholds depends on whether you compare between or within athletes.
Measuring internal load
Heart rate is the workhorse: above ~100–110 bpm it's sympathetically driven and tracks oxygen delivery, so it proxies internal load and feeds models like TRIMP and HR-zone systems. HRV is useful at rest but hard to capture cleanly during exercise. Biomarkers address three interrelated areas — fitness adaptation, fatigue/recovery, and health — with responses classed as acute, delayed (~24 h–days), or chronic.
Subjective monitoring: session-RPE training load = RPE × session minutes (sRPE-TL). It's cheap, valid and reliable, but psychologically influenced; combine with objective measures and watch monotony and strain.
Making change meaningful
Every measure carries error. Validity is judged against a criterion (criterion validity) and by real-world impact (ecological validity); reliability is captured by the standard error of measurement and CV%. A change is only real when the signal exceeds the noise. The smallest worthwhile change (SWC) — often a fraction of the between-athlete SD — is the smallest change that actually matters.
ACWR = recent (acute) load ÷ longer-term (chronic) load — an index of how prepared an athlete is for recent work. Know its flaws: mathematical coupling and weak prediction. Contrast coupled vs uncoupled and rolling-average vs EWMA.
Communication & Education
19–24%▸The sport scientist as a bridge
The scientist's core job in the IDT is to build an evidence base that helps coaches decide about preparation and competition. That means working horizontally across departments, not in a silo, and managing the flow of information to forecast an athlete's readiness. Interdisciplinary support is a mindset, not an org chart.
Data delivery & visualization
Know your audience and report "simple but powerful": a few decision-relevant points, noise removed (drop excess decimals), meaningful changes highlighted at a glance. Traffic-light systems make monitoring tables instantly readable. Show the data spread (dot/box/violin plots), not just means; avoid bar graphs for continuous data with small samples; include error bars to communicate uncertainty.
Storytelling with data is powerful but risky — the failure mode is cherry-picking facts that fit a narrative while burying the ones that don't. Present uncertainty honestly.
Dissemination & evidence-based practice
Match the channel to the goal: blogs/vlogs (~600 words) strip the methods and lit-review to surface the finding; infographics complement — never replace — the source paper, which should always be linked. Guard data hygiene (minimal error in collection and storage) so decisions rest on high-quality data, and practise evidence-based practice — integrate research, context and expertise.
Research process (35–41%)
The single heaviest lens on the exam — these statistics and inference ideas show up inside every domain's cases.
Validity & reliability
Validity: does the test measure what it claims (vs a criterion)? Reliability: does it repeat? Quantified with correlation, typical error / SEM, and coefficient of variation (CV%). Reliability sets the floor for detecting real change.
Inference: two roads
Statistical inference is either hypothesis testing or estimation. NHST tests a sample against a null (usually "no effect") and returns a p-value — the probability of the observed result or larger if the null were true.
Type I / Type II & power
Type I (α) = rejecting a true null (false positive). Type II (β) = missing a true effect. Power = 1 − β rises with effect size and sample size; used to plan sample sizes.
Effect magnitude, not just p
Pair significance with effect size and a SESOI (smallest effect size of interest). Minimum effect tests and magnitude-based approaches ask whether an effect is meaningfully large, not merely non-zero.
Confidence intervals
A 95% CI captures the true value 95% of the time across infinite resampling — a visual read on uncertainty. If a 95% CI crosses the null, NHST returns p > 0.05.
Modeling tasks
Statistical modeling serves three jobs: description, prediction, and causal inference. Watch for confounders (omitted third variables) before claiming a cause. Data mining and ML add supervised/unsupervised tools.
Research Process primer
The heaviest lens on the CPSS, taught in plain English for practitioners. Twelve short lessons, each with a worked sporting example and check questions. Start anywhere, but lessons 3, 4 and 12 carry the most exam weight.
Practice all 503 questions
503 questions across six areas. Use Applied & scenario mode for exam-style judgment items, or Rapid recall to drill terminology. Accuracy by domain builds as you go.
Practice bank
Fix what you got wrong
Every question you miss lands here and keeps coming back until you answer it correctly twice in a row. This is where study time pays the highest return.
Missed questions
Nothing yet
Timed mock exam
Rehearse exam-day pacing. Questions are drawn at random from all 503 with a clock set to the real exam's ~86 seconds per item. Scored against the 70% pass line with a full breakdown.
Key-term flashcards
146 cards across 13 topics. Flip a card, rate how well you knew it, and the deck reorders to resurface shaky terms more often. Filter by topic below; tap a card to flip.
Cheat sheets
Seven condensed one-pagers built for printing. Pick a sheet, hit print, and take it to the gym, the commute, or the exam-eve review. Everything except the sheet itself is stripped from the printout.
Scenario simulator
Half the real exam is scenario-based (6 athlete cases, 3 research reviews here — matching the real exam). These 14 cases with 80 linked questions rehearse that format. Five are built on real open-access papers — open them alongside and practise finding the answer, with 'where to look' and Ctrl+F prompts after every question.
Plan & countdown
Set your exam date and get a week-by-week focus schedule weighted to the blueprint. Your countdown shows on every screen.