Course in data analysis in track motorcycling
About our
The Track Motorcycling Data Analysis Course immerses participants in the world of telemetry and performance analysis. It explores the use of data collected by sensors on the motorcycle, including speed, acceleration, lean angles, and brake pressure, to optimize rider performance and motorcycle setup. It focuses on applying data analysis software tools, such as Race Studio and Motec, to identify areas for improvement in riding, suspension setup, and race strategy. The course prepares participants for roles such as data engineers, performance analysts, and telemetry technicians in racing teams.
The course provides hands-on experience in data interpretation, including the analysis of lap time graphs, trajectories, and acceleration graphs, as well as the relationship of this data to track conditions and rider skills. The importance of communication and collaboration between the data engineer, rider, and team is emphasized to achieve significant performance improvements. Advanced concepts such as traction control, ABS, and engine management are covered for more comprehensive data analysis.
Course in data analysis in track motorcycling
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 3
499 $
Competencies and results
What you will learn
1. Data Analysis for Performance Optimization in Track Motorcycling
Here is the requested content:
- Comprehensive evaluation of telemetry and sensor data to identify areas for performance improvement.
- Study of the key variables that influence motorcycle dynamics on the track: speed, acceleration, lean angles, and G-forces.
- Identification of patterns and correlations in the data to optimize motorcycle setup and race strategy.
- Application of advanced statistical analysis and data visualization techniques for a deep understanding of performance.
- Optimization of tire management: analysis of wear, pressure, and temperature to maximize grip and durability.
- Analysis of suspension behavior: understanding compression, extension, and damping to improve stability and control.
- Study of the rider’s impact on performance: analysis of posture, movements, and riding techniques to identify areas for improvement.
- Use of specialized software for data analysis Motorcycling and race scenario simulation.
Development of data-driven optimization strategies to improve lap times and performance consistency.
2. Data Analysis Strategies to Improve Performance in Motorcycle Racing
2. Data Analysis Strategies to Improve Performance in Motorcycle Racing
- Identify and evaluate critical motorcycle components: engine, chassis, suspension, and tires, using sensor and telemetry data.
- Apply data analysis techniques to optimize engine performance, including fuel management, ignition mapping, and injection calibration.
- Analyze chassis and suspension dynamics, using acceleration, displacement, and force data, to improve stability, grip, and handling.
- Use mathematical models and simulations to predict motorcycle behavior under different track conditions and optimize setup.
- Implement data analysis strategies for tire management, including monitoring pressure, temperature, and wear, to maximize performance and durability.
- Develop effective data dashboards and visualizations to facilitate real-time interpretation and decision-making. during competitions.
Apply machine learning techniques to identify patterns and trends in data, predict performance, and optimize race strategies.
Analyze the influence of environmental conditions (temperature, humidity, wind) on motorcycle performance and adjust the setup accordingly.
Evaluate and optimize pit stop strategies, including tire change planning and time management.
Establish an efficient and collaborative data analysis workflow, involving riders, engineers, and technicians, to improve overall team performance.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. Data Interpretation for Improving Motorcycle Track Times
4. Data Interpretation for Improving Motorcycle Track Times
- Identification and analysis of key factors: You will learn to identify and analyze data from telemetry, timing, and motorcycle sensors to understand track performance.
- Optimization of trajectory and braking points: You will study how data reveals opportunities to improve cornering lines, optimize braking points, and acceleration to gain time.
- Analysis of motorcycle dynamics: You will understand the influence of suspension setup, tires, and rider position on lap time, based on objective data.
- Evaluation of rider performance: You will learn to use data to evaluate rider consistency, identify errors, and areas for improvement in their riding style.
- Use of data analysis tools: You will use specialized software and platforms for analyzing racing data, including interpreting graphs and generating reports. Reports.
Race Strategies and Tire Management: Data will be analyzed to optimize race strategies, tire management, and real-time decision-making during competition.
Implementing Data-Driven Improvements: You will learn how to translate data analysis findings into concrete changes to motorcycle setup, riding style, and race strategy.
Data Comparison and Benchmarking: You will explore how to compare data from different riders, motorcycles, and tracks to identify trends, set targets, and measure progress.
5. Optimization of Track Motorcycling Through Data Analysis
5. **Optimizing Track Motorcycling Through Data Analysis: What You Will Learn**
- Apply methodologies for the analysis and interpretation of data generated during motorcycle races.
- Identify and evaluate the key parameters that influence a motorcycle’s performance on the track.
- Use specialized software for telemetry analysis and simulation of different race scenarios.
- Analyze the influence of rider position, suspension settings, engine configuration, and tire selection on lap times.
- Develop strategies for optimizing motorcycle and rider performance based on data analysis.
- Understand the principles of aerodynamics applied to motorcycling, including the importance of downforce and drag.
- Evaluate the impact of track conditions (temperature, humidity, etc.) in motorcycle performance.
Learn to interpret and use acceleration, braking, and lean angle data to improve riding technique.
Apply statistical analysis techniques to identify trends and patterns in race data.
Create reports and presentations to effectively communicate data analysis results.
6. Advanced Data Analysis and Competitive Strategies in Track Motorcycling
6. Advanced Data Analysis and Racing Strategies in Track Motorcycling
Delve deeper into the study of motorcycle dynamics: understand and analyze telemetry data to optimize track performance.
Master race data analysis: identify key trends, correlations, and patterns for strategic decision-making.
Develop skills in interpreting sensor data: accelerometers, gyroscopes, suspension and tire sensors, among others.
Study data-driven racing strategies: setup optimization, tire selection, race management, and overtaking.
Learn how to use specialized software: data analysis and simulation tools for track motorcycling.
Explore the relationship between rider, motorcycle, and data: how rider decisions influence the data and vice versa.
Analyze performance of different motorcycles: compare and contrast data from different models and brands.
Create effective data reports and presentations: communicate findings clearly and concisely to the team.
Develop leadership and teamwork skills: collaborate effectively with engineers, mechanics, and riders.
Implement continuous improvement strategies: use data to identify areas of opportunity and optimize performance over time.
Who this program is for:
Course in data analysis in track motorcycling
- Motorcycle racing enthusiasts with prior knowledge.
- Engineers interested in data analysis applied to the sport.
- Engineering students (mechanical, automotive, etc.) seeking specialization.
- Data analysts who wish to apply their skills to the world of motorcycling.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
1.1 Introduction to Data Analysis in Track Motorcycling
1.2 Importance of Data Analysis for Rider Performance
1.3 Data Collection: Telemetry and Sensors on Motorcycles
1.4 Data Analysis Tools and Software in Motorcycling
1.5 Key Performance Parameters: Lap Times, Sector Times, Speed
1.6 Identifying Areas for Improvement Through Data Analysis
1.7 Data Visualization: Graphs and Visual Representations
1.8 Track Analysis: Racing Lines and Braking Points
1.9 The Role of Data Analysis in Race Strategy
1.10 Case Study: Practical Examples of Data Analysis in Motorcycling
2.2 Data Sources in Motorcycling: Sensors, Telemetry, and Logs
2.2 Designing the Data Structure for Effective Analysis
2.3 Cleaning and Preprocessing Raw Data
2.4 Tools and Software for Data Preparation
2.5 Normalizing and Standardizing Performance Data
2.6 Data Management: Storage and Organization
2.7 Data Visualization for Trend Identification
2.8 Filtering Techniques and Selecting Relevant Data
2.9 Integrating Data from Multiple Sources
2.20 Validating and Verifying the Quality of Collected Data
3.3 Introduction to Data Analysis in Motorcycling: Fundamentals and Benefits
3.2 Data Collection and Management: Sensors and Acquisition Systems
3.3 Data Analysis Tools: Software and Specialized Platforms
3.4 Telemetry Analysis: Speed, Acceleration, Lean Angle
3.5 Data Interpretation: Identifying Weak and Strong Points
3.6 Trajectory Optimization: Ideal Lines and Braking Points
3.7 Race Strategies: Tire, Fuel, and Pace Management
3.8 Competitor Data Analysis: Studying Times and Riding Styles
3.9 Implementing Improvements: Motorcycle Adjustments and Rider Technique
3.30 Performance Evaluation: Key Metrics and Post-Race Analysis
4.4 Data Collection and Structuring in Track Motorcycling
4.2 Fundamentals of Data Interpretation: Key Variables
4.3 Telemetry Analysis: Essential Sensors and Measurements
4.4 Identifying Areas for Improvement: Lap and Sector Analysis
4.5 Rider Influence: Riding Style and Performance
4.6 Motorcycle Impact: Setup and Adjustments
4.7 Analysis of Weather and Environmental Data
4.8 Optimization Strategies: Setup and Lap Times
4.9 Validation and Refinement: Simulation and Track Testing
4.40 Case Studies: Race Analysis and Success Strategies
5.5 Importance of Data Analysis in Track Motorcycling
5.5 Data Sources: Telemetry, GPS, and Sensors
5.3 Introduction to Key Metrics: Lap Time, Sectors, Speeds, and Lean Angles
5.4 Software Tools for Data Analysis
5.5 Setting Goals and KPIs for Performance Improvement
5.6 Introduction to Data Collection and Organization
5.7 Fundamentals of Data Visualization for Understanding
5.8 Identifying Key Variables Influencing Performance
5.9 Initial Analysis of Rider and Motorcycle Data
5.50 Case Study: First Steps in Analyzing a Training Session
6.6 Data Collection and Preparation in Track Motorcycling
6.2 Analysis of Key Variables: Speed, Acceleration, Lean Angle
6.3 Identification of Areas for Improvement: Racing Lines, Braking, Acceleration
6.4 Data-Based Racing Strategies: Overtaking, Pace
6.5 Competition Analysis: Study of Rival Riders’ Times and Styles
6.6 Optimization of Motorcycle Setup: Suspension, Tires
6.7 Race Strategy Design: Tire Management, Pit Stops
6.8 Race Simulation: Prediction of Results and Strategies
6.9 Risk Assessment and Decision Making: Weather Conditions
6.60 Post-Race Analysis: Feedback and Future Planning
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
Do you have any questions?
Our team is ready to help you. Contact us, and we will respond as soon as possible.
F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).