| (de Almeida-Neto
et al., 2023) |
Orientation
& Selection Support |
Predicted
similarity between morphological + neuromuscular profiles of youth
in Sport Initiation (SI) vs. young athletes in six sports (soccer, swimming,
tennis, volleyball, rowing, BJJ). |
Reliability
of MLP models reported at 87%. Similarity scores: SI → Soccer 88%,
Swimming 79%, BJJ 77%, Tennis 70% (combined analysis). No significant similarity
for Rowing. |
Demonstrated
how MLPs can integrate morphological + neuromuscular + biological
maturation factors. Highlighted BM as a major confounder influencing neuromuscular
strength and morphology. Suggested that MLPs can reduce selection errors
by combining multiple domains. |
Authors conclude
MLPs are effective tools to guide orientation of SI youth into sports matching
their physical/neuromuscular profiles, reducing misallocation risk. Stress
need to consider biological maturation in TID. Limitations: cross-sectional,
small sample (N=75), no longitudinal follow-up, non-elite athletes. |
| (Contreras-García
et al., 2024) |
Development
/ Specialization Analysis |
Classification
of shooting zones and detection of outlier patterns to identify early specialization
vs. versatility in U14 basketball players compared with professional players. |
KNN
model classification of shots reached 99.6% accuracy (professionals as reference).
Outlier analysis: 97.7% of U14 players vs. 64.7% of professionals showed
extreme FGA% patterns. Versatility: U14 2.3% vs. Professionals 35.4%. |
Machine
learning cluster analysis identified 8 shooting zones; combined with outlier
detection, yielded 7 role categories. Revealed U14 lacked versatility and
3-point shooting ability, often over- specializing in 2–4 midrange
zones. Professionals characterized by either versatile players or one-zone
specialists. |
Authors
conclude U14 basketball players show premature specialization patterns not
aligned with professional demands. Recommend formative training to enhance
shooting versatility or to cultivate one-zone specialist roles deliberately.
Findings raise concerns that current youth competitions may prioritize short-term
success over long-term player development. |
| (Ge, 2024) |
Performance
Assessment & Training Support |
Quantitative
classification of youth basketball players’ physical fitness (excellent,
good, pass, fail) using CNN-AE-MG model. |
CNN-AE-MG
achieved mAP = 89.12%, assessment accuracy = 97.5%. Male subgroup prediction
100% accurate (20/20 correct), female subgroup 95% (19/20 correct). |
Combination
of CNN + Autoencoder enabled unsupervised feature learning, reducing
feature loss. Gaussian Mixture with EM algorithm improved classification
reliability. Identified endurance (1000m/800m), lung capacity, grip strength
as weak areas in youth players. |
Authors conclude
the CNN-AE-MG model provides accurate, dynamic assessment of youth basketball
players’ physical health, superior to baseline models. Proposed use
for exercise prescription personalization, training program adjustment,
and talent selection support. Limitations: single-country, limited external
validation, general fitness focus rather than sport-specific outcomes. |
| (Gogos et
al., 2020) |
Selection
Prediction & Career Outcome Forecasting |
Career outcomes
of AFL draftees (matches played, mean AFL Player Rating, mean AFL Player
Ranking). |
Draft combine
alone explained <3–4% of variance in career outcomes. Adding
draft order & playing position improved variance explained slightly
(up to 6%). Individual combine tests explained <2% variance. |
Boosted trees
showed player position (>35% relative importance) and draft order (>25%)
far outweighed combine results (<10%). Key forwards showed no clear
relation between draft position and in-game performance; midfielders/rucks
showed positive relation. Evidence of loss aversion bias: early draftees
played more games irrespective of performance. |
Authors conclude
AFL Draft Combine tests are poor predictors of long-term career outcomes.
Draft position and playing position provide small additional explanatory
power. Suggests physical test batteries are insufficient for TID and should
be complemented by in-game skill, decision-making, and contextual factors.
Highlights systemic biases (early draft order → more opportunities). |
| (Kelly et
al., 2022) |
Talent Development |
(a) Player
review ratings (U9–U16, n=98); (b) Selection to professional contract
(U18, n=18). Both based on ~53 variables across four domains (technical/tactical,
physical, psychological, social). |
Study 1:
15/53 features had non-zero coefficients; strongest = % predicted adult
height (0.196), lob pass (0.160), dribble completion (0.124), total match-play
hours (0.145), older relative age. Study 2: strongest predictors of professional
contract = PCDEQ Factor 3 (coping with pressures), PCDEQ Factor 4 (ability
to organise quality practice), plus progression ratings, slalom dribble,
lower home SES. |
Lasso regression
identified holistic, non-linear predictors across all FCM domains. Key insight:
psychological factors (esp. coping with pressure, organization) emerged
as strongest contributors to contract attainment, not just technical/physical.
Also highlights relative age bias and importance of match-play opportunities. |
Authors conclude
that youth development is multifactorial and dynamic. Success not solely
determined by technical/tactical ability; psychological resilience and self-organization
are critical. Early maturation, relative age, and cumulative match-play
also drive coaches’ evaluations. Findings support bio-banding and
greater investment in psychological development within academies. Limitations:
small samples (esp. Study 2), retrospective data, exploratory nature of
ML. |
| (Kilian et
al., 2023) |
Profiling
/ Latent Structure Analysis |
Identification
of latent factors underlying multidimensional assessments (technical, tactical,
physical, anthropometric, psychosocial). |
Not predictive
classification; evaluated model fit and factor interpretability. nI-WAVE
outperformed PCA with clearer separation, fewer cross-loadings. |
Four interpretable
latent factors: (1) Subjective coach evaluations, (2) Anthropometric/age-related
(incl. sprint), (3) Technical skills (dribbling, ball control, juggling),
(4) Speed/agility. nI-WAVE showed superior interpretability and factor structure
stability. |
Authors conclude
that deep learning factor models (nI-WAVE) provide better latent structure
recovery than PCA, improving interpretability of multidimensional TID data.
Highlight importance of large-scale datasets in advancing ML-based profiling.
Limitations: requires large data, anchors affect loadings, only U12 German
cohort examined. |
| (López-De-Armentia,
2024) |
Scouting
Support & Talent Detection |
Detection
of potential women’s football talents across ~30 leagues using
automated data collection (Soccerdonna) + alert system. |
No accuracy
metrics (non-ML predictive model). Evaluation: Usefulness 4–5/5;
Ease of use 4–5/5; all experts agreed alerts were effective and tool
improved efficiency. |
Tool integrates
basic player data (demographics, position, minutes, contract expiry, market
value, injuries) with automatic alert generation (e.g., U20 players with
1000 min, >5 goals, or consistent starts). Dashboards allow filtering/searching
~12,000 players. |
Authors conclude
WTDTool increases efficiency and coverage in scouting women’s football,
particularly for clubs with limited resources. Experts confirmed ease of
use and usefulness. Limitations: women’s data coverage incomplete
(contract and market data available for only ~25% of players); no
predictive analytics yet. Future: add anomaly detection and integrate multiple
data sources. |
| (Retzepis
et al., 2024) |
Maturation
Prediction |
Classification
of athletes with predicted PHV ≤ median vs. > median age, using anthropometric,
body composition, and strength measures. |
LR achieved
96.67% accuracy, 98% recall, 96.33% precision, 97.09% F1-score, ROC AUC
99%. RF and NN slightly lower (94–96%). |
SHAP (explainable
AI) revealed key predictors: sitting height, weight, height, body fat, left
& right handgrip strength, father’s height. Sitting height
and weight most influential (higher values → PHV > median). Body
fat higher predicted PHV ≤ median. |
Study concludes
explainable ML can accurately predict PHV timing in 11-year-old athletes.
Key growth and strength indicators (esp. sitting height, weight, grip strength)
discriminate maturity status. Findings help avoid misclassification of early
maturers as “talents” and support better talent ID, injury
prevention, and training load management. Recommends longitudinal validation
to confirm predictive power and extend to other sports and female athletes. |
| (Venkataraman
et al., 2024) |
Scouting
Support & Cognitive Profiling |
Player suitability
for selection and development, integrating psychometric (YODA) and coach-based
evaluations into a standardized scouting framework (YUVA-SQ). |
Not accuracy-based:
case demonstration. YODA generated trait/personality plots for individual
players, producing actionable insights for coaches. Validated by expert
use and player development outcomes. |
YODA psychometric
tool provided granular insights into players’ cognitive profile (e.g.,
coachability, team orientation, game knowledge, analytical style). Combined
with coach technical ratings and trial performance for continuous monitoring. |
Authors conclude
YUVA-SQ offers a holistic, standardized scouting framework blending cognitive/behavioral
assessment with technical/physical evaluation. Demonstrated utility in restructuring
a university football team. Proposed extension to grassroots talent scouting
in India, aligning with AIFF “Vision 2047.” Limitations: descriptive
case study only, no predictive performance metrics, no large-scale validation. |
| (Woods et
al., 2018a) |
Talent Development
& Competition Comparison |
Classification
of competition level (elite youth U20 vs. senior NRL) using 12 team performance
indicators (runs, tackles, missed tackles, kicks, etc.). |
CI classification
tree correctly classified 79% of U20 and 93% of NRL games. |
Key discriminators:
‘all runs’, ‘tackles’, ‘tackle breaks’,
‘missed tackles’, ‘kicks’. NRL games = more
runs and tackles, fewer missed tackles. U20 = higher tackle breaks, more
errors. |
Authors conclude
that NRL and U20 competitions show distinct gameplay profiles. U20 players
entering NRL may lack exposure to required tackling capacity and physicality.
Coaches should focus on tackling ability and physical development in U20s.
Suggests “bridging” via State League participation to aid
transition. Practical implication: training interventions should aim to
align youth gameplay with senior competition demands. |
| (Zhao et
al., 2019) |
Talent Identification
& Sport- Specific Profiling |
Classification
of U15–U16 male athletes (basketball, fencing, judo, swimming, table
tennis, volleyball) into their respective sport based on 25 tests (18 anthropometric,
5 physiological, 2 motor). |
DA: 71.3%
correct classification (original: 98.9%). Best: fencing 85%, volleyball
72.7%. Worst: basketball 57.1%. MLP: 71.0% correct classification (original:
99.3%). Best: volleyball 83.4%, table tennis 83.3%. Worst: basketball 20%. |
Key discriminators:
Anthropometry (height, shoulder width, crista width, Achilles tendon length),
Motor (back strength, reaction time), Physiological (vital capacity, hemoglobin
mass, resting HR). Volleyball = tall stature, strength, high lung capacity.
Judo = strength, chest girth, Hb mass. Swimming = lung capacity, tendon
length. Fencing = smaller chest/shoulder width. Table tennis = short lower
leg length + strong back. |
Authors conclude
that generic test batteries of anthropometric, physiological, and motor
measures can differentiate youth athletes by sport with ~70% accuracy,
comparable to European studies. Findings confirm discriminative value of
body size, strength, and aerobic capacity in talent ID. Basketball was hardest
to classify due to small sample size. Implication: test batteries are useful
for broad sport allocation, but need more sport-specific, larger-scale validation. |