SEIKO and Ωmega Timing provide precise measurements in both track and field elite events. In this context we focus on SEIKO, which delivered timekeeping and precise measurements during the 2024 European Championships in Rome. Given the nature of the project, the focus is on their Measurement Systems, in particular their Jump Management System (JMS), Jump Video Distance Measurement system (JUMP VDM) and Triple Jump Phase Measurement (TJP).

Jump Management System: JMS employs a 300 FPS camera capturing long and triple jump takeoff, thus ensuring a clear view is provided to the judges to determine valid or foul jumps. Note the toe-board distance is also measured.

Jump Video Distance Measurement System: JUMP VDM is a two camera system installed in the spectator area. The measurement is achieved via software by positioning the cursor onto the landing mark on the frame visible on the operator’s screen.

Triple Jump Phase Measurement: TJP employs 60 FPS high-resolution cameras capturing the full attempt from run-up to landing, thus measuring important metrics such as speed, flight time and phase distribution.

The following key constraints have been identified, requiring innovation at both elite and grassroots level.

• Gap in data access.
• Limited spectators engagement.
• Talent development barrier.

3.1 Elite Level Data Access Constraints

During the Triple Jump finals at 2024 European Athletics Championships, performance metrics were captured by the SEIKO measurement equipment. However only the official distance and wind were displayed on the event board once the athlete’s attempt was completed.

While SEIKO’s technology is extremely sophisticated and able to measure distance, height, and speed of each phase, as well as phase distribution, this data is inaccessible to coaches and athletes in real time or post competition.

Fig. 1: Jude Bright-Davies’ 5th Attempt Triple Jump Finals, Novuna UK Athletics Championships - 2025 Alexander Stadium, Birmingham, England, UK.

Post-attempt, there is no formal platform or formal feedback mechanism for coaches and their team to review details about the athlete’s attempt aside from relying on recorded video footage.

Fig. 2: Jazmin Sawyers’ Long Jump Finals, Novuna UK Athletics Championships - 2025 Alexander Stadium, Birmingham, England, UK.

Therefore, there exists a gap between data collection, data visualisation and accessibility.

3.2 Grassroots Level Data Access Constraints

Similarly to section 3.1, grassroots competitions also experience a lack of data accessibility as well as data collection.

While elite events have sophisticated hardware systems, grassroots level competitions often lack these kinds of resources.

There is no affordable system that can be installed during club-level competitions (e.g., National Athletics League - NAL or Southern Athletics League - SAL) to analyse athletes’ attempts and provide feedback post-competition, or to be integrated with online live boards such as OpenTrack. Having such a system would make a significant difference in performance improvement and understanding of highly technical events. Such a system would positively impact athletes’ progression and improve coaching quality, which in turn affects talent development.

3.3 Talent Development Barrier

The constraints identified in sections 3.1 and 3.2 contribute to talent development barriers. Technical improvements can be achieved by identifying what went wrong in competition settings that often cannot be replicated during training sessions. Moreover, in circumstances where the coach is not present at the athlete’s competition, they may want to review the performance at a later stage and provide effective guidance accordingly.

3.4 Limited Spectators Engagement

Finally, something that is sometimes overlooked is the broadcasting of field events. In particular, triple jump is often poorly understood or not at all by viewers who may not be familiar with the event. Providing access to dynamic graphs for spectators, and providing more in-depth data to commentators (such as phase distribution, flight times, and angle of takeoff) can improve technical analysis of the event as well as a more concrete comparison between athletes other than official distance comparison.

The proposal is twofold,

• A comprehensive platform with tailored spectators’ and coaches’ access.

• Affordable equipment for grassroots competitions to collect data and support coaching

Following is the proposed deisgn for both components.

4.1 Software

The software component relies on standardized athletes’ data collection to ensure consistent analysis and comparison across different competitions and equipment used.

const { data, error } = await supabase
  .from('JUMPLENS_ATHLETES')
  .select('athlete_id, name, surname, club, event, country, image')
  .eq('lead_coach', 'David C Johnson')
  .order('name', { ascending: true });

4.2 Hardware

The hardware for grassroots level competitions has two independent components, a competition-grade equipment that closely follows the design of [1], and a standalone smart belt system for training purposes. The smart belt specifications are not included in this submission as they are part of ongoing work which I hope to commercialise in the future.

Whilst the previous camera system from Panoutsakopoulos, V., & Kollias, I.A. utilises two cameras, JumpLens equipment incorporates an additional camera to cover the run-up.

JumpLens not only provides a new method to visualise data and provide visual cues, it also introduces a platform for decentralised data to be displayed for coaches to support their training and performance analysis.

It introduces a way to access metrics that can aid storytelling, allowing spectators to better understand highly technical events, such as triple jump.

This approach can be expanded to other field events and support coaches and athletes regardless of where they train in the world, especially during the athletes’ younger years of development where strong foundations must be established.

To conclude, it will not only benefit individuals that practice or teach athletics, it will inherently change the way field events are experienced.

This project is dependent on the availability of accessing decentralised data from SEIKO and OMEGA, including historical data. There is a wealth of data available, and the current state-of-the-art equipment is extremely sophisticated, which means building JumpLens for elite-level competitions is feasible in the near future.

Incorporating the platform at the grassroots level presents greater challenges due to its dependence on the development of the hardware described in section 4.2. Nevertheless, given the wealth of research conducted since the 1990s, including previous hardware systems built for this purpose, it is feasible to achieve such a system.

Key challenges include designing the system to be independent of venue-specific installation requirements, whilst ensuring robust cloud infrastructure to facilitate efficient real-time data flow. The feasibility of this project ultimately depends on establishing partnerships with equipment manufacturers and developing scalable hardware solutions for grassroots implementation.

DATABASE SCHEMA

Athlete Data

• JUMPLENS_ATHLETES: Basic athlete information (name, country, PB, etc.)

Performance Data

• JUMPLENS_ATHLETES_PERFORMANCE: Athlete performances.

• JUMPLENS_PHASE_DISTANCES: official and effective phase distances.

• JUMPLENS_TAKEOFF_VELOCITIES: take-off velocities and corresponding angle of projection.

• JUMPLENS_COM_VELOCITY_CHANGES: Center of mass velocity changes during triple jump phases.

• JUMPLENS_LANDING_DISTANCE_VELOCITIES: Landing distance and ankle velocity at contact.

• JUMPLENS_STRIDES_LENGTH: Stride length data and C.O.M. horizontal and vertical velocities.

• JUMPLENS_AVG_CONTACT_AND_FLIGHT_LENGTH: Contact flight times.

Competition Data

• JUMPLENS_COMPETITIONS_DETAILS: Competition information (venue, date, etc.)

Identity Data

• JUMPLENS_USERS: Coach and user profiles.

• JUMPLENS_USER_SESSIONS: Login sessions