---
title: SoccerMon Dataset for Injury Risk Modeling
url: https://www.emergentmind.com/topics/soccermon-dataset
type: topic
---

# SoccerMon Dataset for Injury Risk Modeling

The SoccerMon dataset is a publicly available, longitudinal resource for monitoring elite female footballers, emphasizing the collection of daily athlete well-being, training, GPS, and injury data. It has been leveraged to advance injury forecasting methodologies, notably within deep learning-based survival modelling, providing a rigorously curated platform for empirical evaluation of individualized, time-varying risk predictions in sports science research [2601.19479].

## 1. Dataset Composition and Structure

SoccerMon captures two seasons (2020–2021) of elite Norwegian female football (Team B cohort), with the following attributes:

- **Participants**: 37 players
- **Observation period**: 2 seasons, 322 unique days
- **Player-date observations**: 4,449
- **Injuries recorded**: 43 (24 acute, 19 overuse)

It aggregates five primary feature blocks, totaling 39 variables:

1. **Rolling workload metrics**: Daily load, acute/chronic training load (ATL, CTL28, CTL42), monotony, strain, acute-to-chronic workload ratio (ACWR), and other windowed sums/ratios.
2. **Subjective wellness**: Six self-reported metrics (Fatigue, Mood, Readiness, Sleep Duration, Soreness, Stress) on a 1–10 scale, except sleep duration (hours).
3. **Session characteristics**: Rate of perceived exertion (RPE), session-RPE (sRPE), subjective and objective session durations.
4. **GPS-derived running metrics**: Mean/max/std speeds, proportional/time/distance running at intensity strata, total/speed-specific distances, distance per minute.
5. **Engineered features**: Subjective_missingness_7d (proportion missing wellness entries in prior week), past_injury_count (cumulative, per-player).

This structure permits granular, daily-level modelling of both objective exposures and subjective states over extended athletic careers.

## 2. Data Preprocessing and Imputation Strategies

Preprocessing consisted of:

- **File consolidation**: Merging session records (GPS, sRPE, injury logs) into a complete daily player-by-date matrix.
- **Outlier removal**: Speeds >32 km/h, session durations >200 min, daily running >16 km flagged as implausible.
- **Feature derivation**: Calculation of 7-day missingness and past injury counts.

Missing value imputation followed three strategies, applied per feature and per player:

1. **Median Imputation**: Replacement with that player’s median value.
2. **Bespoke formula**: Preserves a player’s empirical feature rank (relative to teammates over prior 14 days) for any missing day.
3. **Linear interpolation**: Temporal smoothing of missing entries within each player’s series.

Post-imputation, features with over 30% residual missingness were omitted. Distributional checks (via KDEs, univariate injury correlations) ensured imputations preserved essential statistical properties. *The bespoke formula imputation best maintained signal for risk modeling, whereas linear interpolation best preserved marginal feature distributions*.

## 3. Survival Modeling with DeepHit

Time-to-injury forecasting was formulated as a discrete-time survival prediction problem:

- **Survival function**: $S(t|X) = P(T > t|X)$
- **Hazard function**: $h(t|X) = P(T = t|T \geq t, X) = f(t|X)/S(t|X)$

The DeepHit neural network [Lee, Yoon & van der Schaar, 2020] was employed, minimizing a convex combination of log-likelihood and pairwise ranking loss:
$$
L = \alpha L_{CE} + (1-\alpha) L_{rank}
$$
where $L_{CE}$ is cross-entropy for event/censoring at each day, and $L_{rank}$ penalizes incorrect ordering among comparable event times. $\alpha$ was set to $0.7$.

**Time discretization**: Injury events were mapped to daily intervals (1–7 days ahead). Each training instance used 21 days of rolling features, flattened into an 819-dimensional vector.

**Architecture details**:
- MLP with Dense(256)→Dense(128)→Dense(64) (ReLU activations, dropout 0.3/0.3/0.2)
- Output: Dense(7), softmax for daily risk
- Optimizer: Adam ($1\times10^{-3}$), batch size 64, weight decay $1\times10^{-5}$, early stopping on validation C-index, up to 200 epochs (average ~45 for convergence)

## 4. Validation, Baseline Comparisons, and Model Performance

Two principal validation regimes were implemented:

- **Chronological Split**: 80% of days for training, 20% (future events) for test, reflecting practical deployment.
- **Leave-One-Player-Out (LOPO)**: Each player’s sequence withheld for testing, with models trained on the remainder.

### Performance Metrics

- **Primary**: Concordance index ($C$-index), measuring correct pairwise event ordering.
- **Baselines**: Next-day injury prediction via Random Forest, XGBoost, and Logistic Regression with grid-searched hyperparameters and oversampled minority class.

#### Summary of Model Results

| Model                 | F1    | Precision | Recall | AUC   | Features | Look-back | Forecast  |
|-----------------------|-------|-----------|--------|-------|----------|-----------|-----------|
| Random Forest         | 0.533 | 1.000     | 0.364  | 0.779 | 19       | 1 day     | 1 day     |
| XGBoost               | 0.429 | 1.000     | 0.273  | 0.876 | 18       | 1 day     | 1 day     |
| Logistic Regression   | 0.071 | 0.037     | 0.833  | 0.758 | 8        | 1 day     | 1 day     |

| Split                 | Imputation          | C-index          |
|-----------------------|---------------------|------------------|
| Chronological         | Linear interp.      | 0.660            |
| Chronological         | Bespoke formula     | 0.762            |
| LOPO (median)         | Bespoke formula     | 0.72 ± 0.192     |

*Additional metrics (e.g., Brier score) were not reported.*

## 5. Model Interpretability and Risk Factor Insights

Interpretation of survival outputs was achieved using the SHapley Additive exPlanations (SHAP) framework, adapted for time-to-event predictions (Wang et al., 2024). For each $p(T=t|X)$, the SHAP value $\phi_j$ quantified each feature’s marginal effect on predicted injury risk.

**Key SHAP-derived risk factors included:**
- Elevated Stress (positive $\phi$)
- Greater high-intensity running volume (sp_hir_d)
- Lower Mood and Sleep Duration
- Increased Fatigue (especially on flagged days)
- High 7-day subjective_missingness (suggests disengagement from recovery routines)

*These patterns corroborate established roles of acute workload spikes, psychosocial stress, poor recovery, and cumulative injuries in elevating injury propensity.*

## 6. Contributions and Empirical Significance

Application of the SoccerMon dataset provided a novel proof of concept for deep survival modeling of player injury risk [2601.19479]. The DeepHit architecture, leveraging rolling multivariate time series, delivered:
- Superior event discrimination (C-index = 0.762 with bespoke imputation) over conventional classifiers
- Individualized, time-varying risk estimation for practical decision support
- Actionable interpretability through feature-level SHAP values

The findings underline the critical importance of high-quality imputation preserving risk-relevant variance. The dataset’s multidimensionality and the analytic framework collectively enable practitioners to target stress, loading, and recovery in a data-driven, personalized manner. 

*This demonstrates the viability of the SoccerMon dataset as a benchmark for further advancement in sports injury analytics and individualized, explainable risk stratification.*

Source: https://www.emergentmind.com/topics/soccermon-dataset