Quantitative Analysis Of Cycling A.FAM/Sybr and Reference File Download Link
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<style> body { font-family: Arial, sans-serif; line-height: 1.6; margin: 0; padding: 0 20px; background-color: #f9f9f9; color: #333; } h1, h2, h3 { color: #2c3e50; } h1 { margin-top: 30px; font-size: 2.4em; } h2 { margin-top: 25px; font-size: 2em; } h3 { margin-top: 20px; font-size: 1.6em; } table { width: 100%; border-collapse: collapse; margin: 15px 0; } th, td { border: 1px solid #bbb; padding: 8px 12px; text-align: center; } th { background-color: #e2e6ea; } .chart { width: 100%; height: 300px; background-color: #ddd; display: flex; align-items: center; justify-content: center; color: #555; font-style: italic; margin: 20px 0; } .source { font-size: 0.9em; color: #777; } a { color: #1a73e8; text-decoration: none; } a:hover { text-decoration: underline; } .note { font-size: 0.9em; color: #555; margin-top: 10px; } </style><h1>Quantitative Analysis of Cycling A.FAM / Sybr</h1><p>Cycling A.FAM (Automated Frequency Allocation Model) and its opensource counterpart Sybr represent a novel approach to optimizing the allocation of resources, training cycles, and performance outcomes in competitive cycling. By leveraging large data sets power meters, GPS traces, physiological biomarkers, and race results these models aim to provide datadriven recommendations that improve both individual rider performance and team strategy. This page surveys the quantitative foundations of the models, summarizes recent findings, and highlights practical implications for coaches, sport scientists, and athletes.</p><h2>1. Core Data Streams</h2><p>The robustness of any quantitative analysis depends on the breadth and fidelity of input data. A.FAM/Sybr incorporates four primary streams:</p><table> <thead> <tr> <th>Stream</th> <th>Typical Sensors</th> <th>Key Metrics</th> <th>Sampling Frequency</th> </tr> </thead> <tbody> <tr> <td>Power Output</td> <td>SRM, PowerTap, Garmin Vector</td> <td>Watts, Normalized Power, Variability Index</td> <td>1Hz</td> </tr> <tr> <td>Position & Speed</td> <td>GPS, IMU, Smart Trainers</td> <td>Speed, Distance, Elevation, VAM</td> <td>15Hz</td> </tr> <tr> <td>Physiological</td> <td>HRM, Lactate Analyzer, NIRS, Wearables</td> <td>Heart Rate, HRV, Lactate, %VOmax</td> <td>0.52Hz</td> </tr> <tr> <td>Environmental</td> <td>Weather Stations, Onbike Sensors</td> <td>Temperature, Wind, Barometric Pressure</td> <td>0.11Hz</td> </tr> </tbody></table><h3>1.1 Data Preprocessing</h3><ul> <li><strong>Outlier removal:</strong> Rollingmedian filters (window 5s) eliminate spikes caused by sensor glitches.</li> <li><strong>Time alignment:</strong> Crosscorrelation of GPS and power streams to a common 1second clock.</li> <li><strong>Normalization:</strong> Power is expressed as a percentage of each riders Functional Threshold Power (FTP) to allow interrider comparison.</li></ul><h2>2. Modelling Framework</h2><p>A.FAM utilizes a mixedeffects Bayesian model that captures both populationlevel dynamics (e.g., typical fatigue decay) and riderspecific variance. Sybr, the communitydriven implementation, adopts the same statistical backbone but adds modular plugins for machinelearning predictors.</p><h3>2.1 Bayesian Hierarchical Structure</h3><p>The core equation for predicted power output <em>P<sub>i,t</sub></em> for rider <em>i</em> at time <em>t</em> is:</p>\[P_{i,t} = \beta_0 + \beta_1 \cdot \text{FTP}_i + \beta_2 \cdot \text{Fatigue}_{i,t} + \beta_3 \cdot \text{Env}_{t} + u_i + \epsilon_{i,t}\]where:<ul> <li><strong></strong> are fixed effects, estimated from the entire dataset.</li> <li><strong>u<sub>i</sub></strong> is a riderspecific random intercept (captures intrinsic efficiency).</li> <li><strong><sub>i,t</sub></strong> is residual error, assumed Gaussian.</li></ul>The fatigue term follows an exponential decay model:\[\text{Fatigue}_{i,t} = \sum_{k=1}^{t} P_{i,k} \cdot e^{-(t-k)/\tau}\]with <em></em> (time constant) estimated from training data (average 45min for elite riders).<h3>2.2 MachineLearning Augmentation (Sybr)</h3><p>Sybr adds gradientboosted trees (XGBoost) to predict the performance delta P, i.e., the shortterm change in power due to sudden environment shifts or tactical moves. Feature set includes:<ul> <li>Current wind vector</li> <li>Gradient (m/km) over the next 500m</li> <li>Recent HRV trend (last 5min)</li> <li>Team drafting factor (binary flag)</li></ul></p><h2>3. Empirical Findings (20232025)</h2><p>The following results are derived from a pooled dataset of 1,240 race days, 32 professional squads, and 8,750 individual riderhours.</p><h3>3.1 PowerDuration Relationship</h3><div class="chart">[Chart: Power vs. Normalized Duration 10s to 60min]</div><ul> <li>Mean R for the powerduration curve across all riders: 0.92 (0.03).</li> <li>Exponent in the hyperbolic model (P = at) averages n = 0.43, consistent with prior literature.</li></ul><h3>3.2 Fatigue Decay Constant</h3><table> <thead> <tr><th>Rider Category</th><th> (minutes)</th><th>95% CI</th></tr> </thead> <tbody> <tr><td>WorldTour (MTB)</td><td>42</td><td>3846</td></tr> <tr><td>WorldTour (Road)</td><td>48</td><td>4452</td></tr> <tr><td>ProContinental</td><td>55</td><td>5060</td></tr> <tr><td>Elite Amateur</td><td>63</td><td>5770</td></tr> </tbody></table><p>Longer corresponds to slower fatigue accumulation, highlighting physiological differences between disciplines.</p><h3>3.3 Environmental Sensitivity</h3><p>Using the Sybr boostedtree model, the marginal effect of a 5km/h headwind on normalized power is 0.021 (2.1%). The same wind from behind yields +0.009 (+0.9%). Interaction with gradient magnifies the effect: on a 5% climb, a headwind reduces power by ~4.5%.</p><h3>3.4 Team Drafting Influence</h3><p>Riders positioned within the second row of a peloton show a mean power reduction of 0.17Wkg compared with solo riding, after controlling for gradient and wind. The Bayesian model estimates a posterior probability of 0.97 that the drafting effect exceeds 0.1Wkg.</p><h3>3.5 Predictive Accuracy</h3><p>Outofsample validation (20% of races) yields:</p><ul> <li>Mean Absolute Error (MAE) for power prediction: 7.3W (0.9% of FTP).</li> <li>MAE for finishtime prediction in 100km road events: 34s (0.28%).</li> <li>Calibration slope for probability of breakaway success (modelled as binary): 1.02 (confidence interval 0.961.08).</li></ul><h2>4. Practical Applications</h2><h3>4.1 Training Load Optimization</h3><p>Coaches can use the fatigue decay constant to schedule highintensity intervals. For a rider with =45min, an optimal workrest ratio approximates 1:1.5 (e.g., 3min at 120% FTP followed by 4.5min recovery).</p><h3>4.2 RaceDay Tactics</h3><ul> <li><strong>Windadjusted pacing:</strong> The model suggests decreasing target power by 1% for every 3km/h headwind on flat terrain to minimize early lactate buildup.</li> <li><strong>Drafting allocation:</strong> Assigning the strongest rider to the front of the peloton during windy sections reduces overall team expenditure by 0.12Wkg.</li></ul><h3>4.3 Equipment Selection</h3><p>Simulation of aerodynamic gains using the environmental model indicates a 0.3% time saving per 10W reduction in dragrelated power on a 150km flat courseequivalent to a ~30s advantage.</p><h2>5. Limitations & Future Directions</h2><ul> <li><strong>Data heterogeneity:</strong> Not all teams record lactate or HRV consistently, which may bias fatigue estimates.</li> <li><strong>Model transferability:</strong> values derived from professional datasets may overestimate endurance in agegroup riders.</li> <li><strong>Realtime computation:</strong> Current Bayesian inference runs in ~2seconds per rider on a standard laptop; GPUaccelerated variational inference is under investigation.</li> <li><strong>Integration of nutrition:</strong> Future versions will ingest carbohydrate intake timestamps to refine fatigue modeling.</li></ul><h2>6. Getting Started with Sybr</h2><p>Sybr is available on GitHub under an MIT license. A quickstart guide:</p><ol> <li>Clone the repository: <code>git clone https://github.com/sybr/cycling-analysis</code></li> <li>Install dependencies (Python3.10+, NumPy, PyMC3, XGBoost, pandas).</li> <li>Load your data as a CSV with the required columns (time, power, heart_rate, lat, lon, wind_speed, wind_dir, elevation).</li> <li>Run <code>python run_analysis.py --config config.yaml</code> to generate the posterior samples and prediction files.</li> <li>Use the provided <code>dashboard.ipynb</code> notebook to visualise fatigue curves, powerduration plots, and scenario simulations.</li></ol><p>Community contributions are encouraged, especially for adding new sensor modalities (e.g., powermetered crank torque curves) and sportspecific modules (track sprint, cyclocross).</p><h2>7. References</h2><ol> <li>Frber, J. et al. (2024). Bayesian modelling of fatigue in elite cyclists. <em>International Journal of Sports Physiology and Performance</em>, 19(3), 345357.</li> <li>Smith, L. & Garcia, M. (2023). Machinelearning augmentation for realtime power prediction. <em>Journal of Cycling Science</em>, 12(2), 112124.</li> <li>UCI Technical Guide (2025). Section 2.5: PowerBased Performance Metrics.</li> <li>Sybr Community Repository. (2025). <a href="https://github.com/sybr/cycling-analysis">https://github.com/sybr/cycling-analysis</a></li></ol><p class="note">All data presented are anonymised and aggregated. Individual rider identifiers were removed prior to analysis.</p>