# How AI Is Discovering Athletes That Human Scouts Miss | Richard Felton-Thomas | TED

Source: https://www.youtube.com/watch?v=OKu0yybmtkc
Recap page: https://rapidrecap.app/video/OKu0yybmtkc
Generated: 2025-11-10T20:37:21.726+00:00

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## Quick Overview

Richard Felton-Thomas showcases how his company uses AI and mobile technology to democratize elite athlete scouting and development by providing objective, measurable data to young players globally, effectively leveling the playing field beyond traditional geographic and socioeconomic barriers.

**Key Points:**
- The global talent pool for sports like soccer is estimated at over 1,000,000+ players, but only about 2,000 are visible to scouts annually due to geographic and access barriers.
- Felton-Thomas's company developed an AI-based solution that uses smartphone computer vision and deep learning to analyze 22 key body segments during drills, converting 2D video into 3D biomechanical data.
- The platform allows young athletes to perform standardized drills (like 10m sprints, cone dribbling) and receive objective, comparable, and reliable data scores, eliminating human intuition bias.
- The company partnered with Premier League clubs like Burnley FC and Chelsea FC, and also works with organizations like the Laureus Foundation in India, demonstrating broad applicability.
- The technology provides actionable feedback, such as inviting players who score well to a commercial virtual academy, which led to one player who was never scouted conventionally receiving a five-year scholarship.
- The ultimate goal is to make talent assessment universal, enabling players anywhere with a smartphone to be objectively evaluated against global standards.
- The company is expanding its AI capabilities to be multi-cloud and country-specific, supporting various sports like baseball, basketball, and athletics.

![Screenshot at 01:17: The talent funnel graphic clearly illustrates that out of a potential talent pool of 1,000,000+ players, only 2,000 are visible to scouts, highlighting the problem the speaker's technology aims to solve.](https://ss.rapidrecap.app/screens/OKu0yybmtkc/00-01-17.png)

**Context:** Richard Felton-Thomas, speaking at TED Sports Indianapolis, addresses the inherent limitations and biases in traditional sports scouting, where only a tiny fraction of global talent is ever seen by professional clubs. He introduces his company's AI-driven technology, developed from a background in biomechanics, as a solution to objectively measure and compare athletic potential regardless of a player's location or background.

## Detailed Analysis

Richard Felton-Thomas argues that traditional sports scouting overlooks the vast majority of global talent due to geographic and access constraints, noting that out of 1,000,000+ potential athletes, only about 2,000 are seen by scouts yearly. To combat this, his company built an AI-powered solution that analyzes 22 key body segments from standard 2D smartphone video recordings of athletes performing drills like sprints, dribbling, and jumping. This technology converts the footage into objective 3D biomechanical data, allowing for standardized scoring across factors like acceleration, power, and coordination, applicable across different sports (soccer, baseball, basketball). He cites successful collaborations with Premier League clubs like Chelsea FC and Burnley FC, and an initiative with the Laureus Foundation in India, where the system identified talent missed by conventional scouting methods, leading to scholarships. The platform provides immediate, objective feedback, such as an invitation to a virtual academy, ensuring that talent is evaluated fairly based on measurable performance rather than subjective human intuition, thus leveling the playing field globally.

### The Scouting Problem

- Talent pool is 1,000,000+ players, but only 2,000 are visible to scouts annually
- Geographic and cost factors limit access
- Traditional scouting relies too much on intuition.

### The AI Solution

- Using computer vision and deep learning to analyze 22 key body segments from smartphone video
- Converts 2D video into objective 3D biomechanical data
- Standardized drills (sprints, dribbling, jumping) provide comparable metrics.

### Real-World Application & Success

- Partnered with Chelsea FC and Burnley FC; used in India with Laureus Foundation
- One 17-year-old who was never scouted received a 5-year scholarship
- System is being deployed multi-cloud and country-specific.

### Data Interpretation & Feedback

- Data is presented via dashboards showing specific metrics and an overall NRS score
- Provides actionable feedback, like invitations to virtual academies, allowing players to be scored objectively.

### Future Vision

- Expanding the technology to be used for at-home healthcare, medical applications, and across various sports beyond soccer, including baseball and basketball.

![Screenshot at 00:05: Richard Felton-Thomas begins his presentation, setting up the concept of visualizing sporting greatness.](https://ss.rapidrecap.app/screens/OKu0yybmtkc/00-00-05.png)
![Screenshot at 00:13: A graphic overlay confirms the event location as 'RECORDED AT TEDSports Indianapolis'.](https://ss.rapidrecap.app/screens/OKu0yybmtkc/00-00-13.png)
![Screenshot at 01:17: A slide displays the talent funnel, showing the massive disparity between the 1,000,000+ potential players and the 2,000 visible to scouts.](https://ss.rapidrecap.app/screens/OKu0yybmtkc/00-01-17.png)
![Screenshot at 03:15: The 3DAT software interface shows a blue 3D model of an athlete performing a movement, overlaid with real-time biomechanical metrics like Pelvic Linear Velocity/Magnitude.](https://ss.rapidrecap.app/screens/OKu0yybmtkc/00-03-15.png)
![Screenshot at 03:36: A visual demonstration showing two laptop screens displaying the AI's analysis: one showing a full body metric breakdown and the other a circular performance wheel.](https://ss.rapidrecap.app/screens/OKu0yybmtkc/00-03-36.png)
![Screenshot at 04:51: A grid view of multiple video feeds demonstrating the diverse range of sports and drills the AI system can analyze.](https://ss.rapidrecap.app/screens/OKu0yybmtkc/00-04-51.png)
![Screenshot at 05:09: A screen split showing 'Player A' performing drills in a dry, outdoor environment versus 'Player B' in a snowy, structured environment, illustrating objective comparison across conditions.](https://ss.rapidrecap.app/screens/OKu0yybmtkc/00-05-09.png)
![Screenshot at 07:05: A slide shows a group of young athletes in India holding their national flag, representing the global reach of the talent identification initiative.](https://ss.rapidrecap.app/screens/OKu0yybmtkc/00-07-05.png)
![Screenshot at 09:35: The AI monitor interface displays a score of 1.49 \(NRS\) and feedback: 'INVITED TO ELITE VIRTUAL ACADEMY' for the female soccer player being analyzed.](https://ss.rapidrecap.app/screens/OKu0yybmtkc/00-09-35.png)
![Screenshot at 10:11: The presentation shifts to a laptop screen displaying a detailed dashboard with metrics surrounding a holographic human figure, representing the depth of analysis available.](https://ss.rapidrecap.app/screens/OKu0yybmtkc/00-10-11.png)