| (Abidin,
2021) |
Selection
Prediction & Team Formation |
Player position
classification (Defender, Midfielder, Forward) and lineup formation for
U13 Altınordu Football Academy players. Compared ML lineups with
coach’s ideal lineup and 20 match lineups. |
RF best at
93.9% accuracy, κ=0.91; MLP 92.6%, LMT 90.5%. Adding Hit/it training
data improved accuracy across all algorithms vs. baseline (e.g., RF 81.8%
→ 93.9%). For team formation, lineups of SMO & SimpleCART
closest to coach (Pearson r≈0.975). Lineup similarity with match
lineups averaged 89.36%. |
Demonstrated
importance of combining coach evaluation + training device (Hit/it)
data. Synthetic data generation addressed small sample. Lineup similarity
analysis showed ML can approximate coach/team decisions without using match
data. |
Authors conclude
ML models (esp. RF, MLP, LMT) can reliably support player selection and
lineup formation, potentially integrated into weekly coaching tools. Hit/it
data deemed essential to boost predictive accuracy. External generalizability
remains untested beyond single academy. |
| (Razali et
al., 2017) |
Selection
Support & Team Formation |
Prediction
of most suitable playing position (10 outfield roles: sweeper, backs, midfielders,
wingers, forwards; GK excluded) based on physical, mental, and technical
ratings. |
Bayesian
Networks: 99% accuracy; Decision Tree: 98%; KNN: 97%. |
Framework
combined coach-rated attributes (1–10 scale across physical, mental,
technical skills) with ML classifiers. Developed a Football Talent Identification
Site for practical deployment. Expert evaluation (20 coaches/managers) confirmed
ease of use and relevance. |
Authors conclude
ML classifiers can assign players to their optimal positions with very high
accuracy, reducing subjective bias in coach decisions. Prototype system
was well-received (75–80% strongly agreed on usability, suitability).
Limitations: small single-school dataset, manual skill ratings subjective,
no external validation. |
| (Woods et
al., 2018b) |
Team Formation
& Position Classification |
Classification
of elite junior Australian football players (U18) into 4 playing positions
(defender, forward, midfield, ruck) based on 12 technical skill indicators
from national championships. |
LDA: 56.8%
accuracy (errors: midfielders 19.6% → rucks 75%). Random Forest:
51.6% accuracy (errors: midfielders 27.8% → rucks 100%). PART decision
list: 70.1% accuracy (errors: midfielders 14.4% → rucks 100%). |
Rule induction
(PART) generated 6 classification rules, mainly leveraging disposals, contested/
uncontested possessions, kicks, and inside 50s. Showed defenders and forwards
overlapped heavily; midfielders most distinct; rucks poorly classified due
to small sample. |
Authors conclude
that existing commercial technical indicators provide limited discriminatory
power for position classification, with high homogeneity across roles. PART
offered relatively better accuracy but overfitting risk noted. Practical
implication: recruiters should use more position-specific technical indicators
and design competitions/training environments that allow players to demonstrate
role-specific attributes. Reliance solely on standard technical stats may
obscure positional differences and complicate objective recruitment. |