AI's Transformative Impact on Football

OK, let's unpack this. We are diving deep into, well, a

really fascinating space right now where the world of sports,

particularly football, is just being rapidly reshaped by

technology. Yeah, it's moving fast.

We've been looking at some incredible material really that

kind of lifts the lid on exactly how artificial intelligence is

moving, you know, from theory into the stadiums, onto the

training grounds, into the fan experience, as we all know.

So our mission here in this deep dive for you listening is to

well, cut through some of the hype, find the really important

insights, look for those aha moments and really understand

what this all means for the game, for the athletes, you

know, pushing their limits, for the fans and of course for the

huge business side of sport. And it's a landscape that's just

evolving at, well, breakneck speed.

And the insights we're synthesizing today, they come

directly from people right in the thick of it.

We're pulling together perspectives from experts like

Jean Bade Salia, who brings that strategic consulting view.

Then to Rick Agda, he's got insights from like the cutting

edge AI tech startups and sports and add an Anakib, providing

that, you know, vital perspective from inside a

National Football federation actually dealing with this

innovation day-to-day. So combining these angles, it

gives us a pretty comprehensive picture I think.

Yeah, definitely. And sort of framing all of this,

there's this central, maybe even provocative question that came

up. Is it possible that by say,

20-30 teams, leagues, federations and football who

haven't fully committed to AI strategies, you know, things

like data-driven recruitment, better fan engagement,

performance analysis, detection, will they just find themselves

surpassed? Left behind.

Yeah, fundamentally left behind of the world is just moving so

fast. That question feels like a

really potent filter for everything we're about to

explore. It absolutely does because the

evidence, it really suggests the pace isn't slowing down, it's

speeding up and the consequences of just standing still, they

could be pretty severe. So let's dive right in.

Then to kick things off, what immediately jumped out at you

from this material about AI's current impact?

What are we actually seeing on the ground now in football?

Well, one of the the most significant shifts and Jean

Baptiste Alia really highlighted this, is the fundamental change

in how organizations can even process data.

Historically football, like, you know, lots of industries

generated massive amounts of unstructured data.

Think match videos, written scout reports full of subjective

stuff. Right, just notes and

observations. Social media chat about teams,

players Getting real, actionable insights from that kind of messy

data was incredibly hard, took ages, and often relied on

someone manually revealing it. Right Subjective calls.

So the raw material was there, but it was just hard to work

with. Precisely.

Now AI is completely changing that equation.

It's making it possible to access, process, analyze huge

volumes of these diverse data sets.

Stuff from on the pitch, off the pitch, player Wellness, admin

info, fan interactions, you name it.

This ability to quickly, efficiently digest complex,

messy info on a massive scale. It's truly revolutionary.

It unlocks insights that were basically hidden before or just

too expensive to find. It's like suddenly having a key

to rooms full of information you knew existed but couldn't get

into. That makes a lot of sense.

Better processing, more insights?

What else is happening right now that feels like a big step

forward? Joan Baptiste Alia also pointed

to big advancements in tracking data, specifically 3D data or

skeleton tracking. Skeleton tracking.

Yeah, this is tech that can get incredibly granular tracking

info like detailed body positioning, limb movements, all

from just a single camera feed. Wait hold on, body position from

1 camera? How does that even work?

It's a advanced computer vision algorithms.

Instead of needing loads of cameras and complex setups to

sort of try regulate positions, AI analyzes the single video

stream and estimates the pose, the movements of the players

skeletons. Now OK, the immediate real time

high performance use cases like analyzing player mechanics

during a match, that's still mostly limited to the absolute

top tier teams. We could.

Processing that instantly is tough.

Exactly, need serious infrastructure people but the

potential for off pitch applications or analyzing after

the game, that's massive and becoming much more common.

Like for broadcasting. Yeah, think about revolutionary

stuff for broadcasting. Imagine overlays showing a

player's fatigue via heat maps on their body, or automatically

generating highlights based on specific skillful movements,

like a perfect tackle or a certain type of shot.

It opens up possibilities for, you know, really immersive fan

experiences, using this rich data to tell new stories.

And as this tech gets cheaper, right?

As Jean Baptiste Elliot pointed out, as the cost and complexity

drop, this kind of deep granular data becomes available way down

the pyramid. Non premium competitions,

smaller clubs, academies even. It changes the depth of data

available everywhere, not just at the very top.

That's huge democratizing access to these advanced insights,

right? What about making data easier

for fans to understand and engage with?

Terbrick AGDA gave some great examples here.

He mentioned the augmented reality experiences fans could

get on their phones at the Qatar World Cup.

Oh yeah, I remember reading about that.

You hold up your phone towards the pitch and the AI overlays

real time stats, player info, tactical graphics onto the live

view. That's a perfect example of

taking complex dynamic data like player speed, distance covered

and making it instantly understandable consumable for

the average fan. Enhances the experience whether

you're there or at home. Yeah, that really brings the

data to life, doesn't it? Makes it easy to grasp.

Absolutely. And he also shared a really

interesting logistical use case from his experience in Canada

using automated cameras with AI. AI for scouting across huge

distances, multiple time zones. Instead of needing human scouts

to travel everywhere to watch games in like remote places, AI

can automatically follow the action, detect specific player

actions, record tailored footage.

Scouts can then review talent remotely, efficiently, covering

way more ground than traditional methods allow.

It's AI tackling those basic logistical headaches and finding

talent. That's scouting application

really highlights solving practical big scale problems,

bottlenecks really. It does, and Turek Agda also

noted how AI is pushing the boundaries of event detection

beyond just the basic stuff. Like goals in corners.

Yeah, we're moving past just identifying goals, corners,

fouls. AI can now be trained to

recognize much more specific, nuanced actions on the field,

maybe identifying a certain type of press trigger, or a defensive

block pattern, or successful transitions.

And crucially, it can accurately spot which players were involved

and even determine if the action was successful based on what

happened next. So not just what happened, but

how, who did it and did it work. Exactly that level of detail

goes way beyond traditional manually tagged data.

It gives you a far richer data set for tactics, player

evaluation, even coaching. Wow.

OK, so you combine the ability to process messy data with super

granular tracking including body movements, making real time data

engaging for fans, using AI for logistics like scouting, and

recognizing incredibly specific game events.

That's a pretty significant set of capabilities already here.

Really is. Does this initial overview

already strongly suggest that the answer to our opening

question about being left behind by 20-30 if you don't engage is

becoming? Well, pretty clear, it

absolutely does. The experts were quite emphatic.

As Jean Baptiste Elliot put it, if organizations in football

aren't making the necessary investments in their basic data

capabilities, how they collect managed structure info, and then

the AI layer on top, they're falling behind.

And the critical point is the gap isn't static.

It's widening, accelerating. Trying to operate today without

leveraging data and AI insights, it's increasingly like trying to

compete blindfolded. AI isn't just an edge anymore,

it's fast becoming fundamental to just staying in the game.

OK, That paints a very clear picture of the current landscape

and the stakes. Let's narrow our focus now to a

core area where AI is having a really profound immediate impact

player performance and health. Adnan Anaki from the Rock

Confederation specifically highlighted this as seeing big

advancements just in the last couple of years.

Yes, he pointed to several critical applications for

athlete welfare and optimization.

First, athlete Health Protection.

Now it's still developing, but AI models are increasingly used

to help understand individual players physical limits, their

stress points. They're starting to show

potential and predicting some non contact injuries.

Not perfectly obviously, but giving probabilities,

identifying players at higher risk based on workload recovery,

bio mechanics. It's about proactive risk

management. So moving from reacting to

injuries to maybe preventing some.

Exactly giving insights to medical and performance staff to

inform load management, recovery strategies.

But Aden and Anna Keefe emphasized a crucial area where

AI is making a very tangible difference right now, where the

data is robust, the applications clearer is in the return to play

process re athletization after an injury.

Yes, that could be so tricky, can't it?

Judging when a player is truly ready to go again.

It is because historically, getting a player back relied

heavily on, you know, subjective assessments, medical staff,

physios, even the players saying how they feel.

And while their clinical expertise is vital,

irreplaceable data provides objective, quantifiable

insights. Is the athlete's body

functionally ready to handle the physical demands of competitive

football again? And I mentioned for clubs,

getting that right is incredibly important financially too.

Hugely important. An injured player is a massive

cost on the payroll, not contributing.

Plus rush a player back too soon, big risk of reinjury.

That's even more disruptive, more costly, maybe longer

absences, chronic issues. So data-driven return to play

protocols informed by AI, analyzing various data streams.

That's a significant innovation. It gives a more objective basis

for assessing readiness, helps clubs make better informed

decisions, mitigates those financial and performance risks,

ensures players return not just fast, but safely and

effectively. That makes perfect sense now to

actually do this level of detailed analysis for

individuals. What kind of data are

federations and clubs collecting?

Where's it all coming from? Adnan Anna Keefe outlined it as

a multi faceted data acquisition process.

You've got the objective on pitch data right from computer

vision, intelligent cameras capturing match stats, training

metrics, sprints, changes of direction, duels 1 passing

accuracy under pressure, things like that.

And the training data too. Yes, the objective Off pitch

data from physical prep sessions, metrics from

wearables, GPS devices, tracking distance, speed, acceleration,

deceleration, physical load data from strength and conditioning

gear, results from medical and physical tests off the field.

OK, objective stuff from games and training.

But you mentioned subjective data earlier.

That seems key too. Yes, and he really stressed the

absolute importance of incorporating subjective data

from the athlete, understanding how the player feels by their

recovery, sleep quality, energy levels, muscle soreness, their

overall state, mental too often collected through daily

questionnaires or quick chats. Interesting.

So it's combining what the numbers say and how the person

actually feels. Exactly because a player's

subjective state can massively impact their risk profile, their

readiness, even if the objective numbers look fine on paper,

right. All these different types,

objective on pitch, objective off pitch, subjective Wellness,

they have to be fed into what Adnan and I keep called the

grinder. The grinder.

Yeah, basically the integrated system or platform that can take

in process, synthesize all this diverse info.

And he emphasized this isn't about collecting data points in

isolation. It has to be acquired

consistently over time for each individual player.

Building a history. Exactly Build that longitudinal,

personalized profile for every athlete.

Understand their unique responses to load, their injury

patterns, their recovery needs, performance fluctuations.

This comprehensive historical profile is essential for

optimizing that specific player's performance, managing

their health long term, making truly data informed, tailored

decisions. So, building a personalized data

portrait over time within a structure like a National

Federation, where does all this data go?

Who uses it? Adnan Anakif explained the flow

within the Moroccan federation. First, it directly helps the

national team. Preparing players for camps

matches, managing fitness during intense periods and forming

selections makes sense. Second, it's vital for talent

development in their academies, tracking young players progress,

identifying specific areas for improvement backed by objective

metrics. And crucial for scouting, which

he mentioned earlier is a key use.

Case absolutely objective data supports the human scouts

identifying potential future national team players,

especially from outside the main domestic leagues and given that

most talent is in the club's pro amateur women's teams, the

federation is actively working to empower these clubs,

providing tools, support, helping clubs collect this rich

training and performance data on their own players consistently.

This helps the club manage their squad, but it also flows up to

the Federation, creating this crucial historical database for

players who might get called up later.

It's symbiotic data collection at club level benefits them

locally, and the national program builds A deeper

knowledge pool. Aroach supporting data

collection down at the club level to benefit the national

set seems very strategic. Let's broaden out again from the

individual to AI's impact on analyzing the collective team

sorts tactics. Tariq Agda with his startup

background, offered some perspective here.

Yeah, he really hammered home how dramatically the ability to

analyze team performance has changed.

He contrasted it with his playing day's analysis meant

watching video, maybe some basic GPS if you were lucky.

Right now, teams get rapid access to match and training

video, plus vastly more interesting, detailed data from

tracking and event analysis. And this isn't just for the

Super rich elite clubs anymore. That's a key point he made.

Again, thanks to AI and automated cameras, even BASIC

data collection and analysis are becoming accessible.

Lower down amateur teams, colleges, lower leagues.

This wider access is raising overall data literacy,

understanding how data can help performance across the whole

sport. But with more data access, he

also raised an important challenge, especially with

younger players. Yes, the education challenge,

particularly with the social media generation of players that

they're used to seeing curated content, often just their own

highlights, which can lead to maybe an inflated sense of their

overall performance. Yeah.

You only see the good bits. Exactly.

AI provides objective data, metrics on defense, off ball

work work rate, decision making under pressure, stuff way beyond

highlight reels. But just dumping raw data isn't

enough. You need to educate coaches,

analysts and the players on how to interpret and use this data

correctly, understand its strengths, limits, integrate it

with feel and context. He felt someone like Adnan

Anakif focusing on digital transformation and education and

federation is perfectly placed for this kind of data literacy

push that leads. Us nicely into the age-old

debate that came up. Can AI ever really replace the

coach's human eye, their intuition built over years?

And the consensus from the experts was a very clear no, AI

won't replace the human coach. The coach is still the decision

maker, selects the team, defines tactics, motivates players,

makes those crucial end game calls.

But AI is emerging as an incredibly powerful cool to

support and enhance those human decisions.

So augmenting capability, not substituting it.

Precisely. Jean Baptiste Ally used a great

analogy. AI itself won't replace you, but

a person using AI likely will replace someone who doesn't.

Interesting. That applies directly to

coaching. Even the top coaches with

amazing flair rely on data analysts behind the scenes.

They get data-driven insights to prep for opponents, monitor

fitness, review games. Top level coaching is already

data assisted coaching fundamentally.

And there's a practical reason for that too, right?

The sheer amount of information in a game is just overwhelming

for one person. Tarek Agda gave some compelling

numbers on this. In a typical pro match there are

maybe 2000 to 3000 distinct events, passes, tackles,

movements, adjustments. Wow.

Our brains get millions of bits of info per second via senses,

but studies suggest we only consciously process about 5 bits

per second. Incredibly limited.

AI bridges that huge gap. It can ingest, analyze, cross

reference the sheer volume and detail that a human coach, no

matter how experienced, simply cannot fully capture, recall, or

analyze effectively, especially in real time or even after.

And he gave that really powerful, almost painful example

to highlight what can be missed. Yeah, that vivid example from

the 2006 World Cup quarterfinal, Brazil versus France.

Moments before Henry scored the winner from a free kick, the

camera showed Roberto Carlos visibly exhausted, hands on

knees, maybe not fully focused near the wall.

I remember that moment. The question Turek Agda posed

was how could that level of fatigue in such a crucial

moment, World Cup knockout game, top team like Brazil not be

spotted and maybe addressed. A sub, a tactical tweak.

Now the coach still makes the sub decision based on lots of

factors. But having objective data on

fatigue, workload, physical state, right then it could

provide critical undeniable support to inform that decision,

potentially avoid a costly oversight.

At the absolute highest level, AI provides the info to flag

those missed cues. That really underscores how AI

isn't just optimal marginal gains, it can provide critical

info to avoid fundamental errors that could change outcomes.

Let's shift gears now to something John Baptiste tallied

really hammered home the critical prerequisite for using

AI effectively. It's not just about the fancy

tools, it's the foundation underneath.

He was crystal clear on this. Before any organization in

football club or federation seriously thinks about advanced

AI, they must have a robust, well defined data strategy

first. Meaning, understand what data

you're collecting, where it's coming from, and most

importantly, how it's being structured and managed.

And the reality is, many are still falling short there.

That's the big gap, he highlighted.

Many clubs, even some federations, still operate with

data in silos. Disconnected systems, often just

scattered excel files. Oh dear.

Data from different places. Player Wellness reports showing

poor sleep. GPS data showing high training

load. Matched data showing a drop in

performance. Physio notes might exist

separately. They don't connect, don't talk

to each other. You have the pieces, but without

integration you can't see the full picture.

Understand the combined risks. So connecting it is key and who

owns it? Exactly, data integration, but

also understanding data ownership, who controls it,

where it lives and having processes.

As for who processes it for insights, true deep

understanding only comes from merging these diverse data

types. Event data, what happened?

Tracking data where, how players moved, biometric data, body

responses, even biomechanical data movement patterns.

He gave the example of academies collecting tracking data on

young players U nineteens. Why?

Because event data alone isn't enough.

Tracking captures crucial off ball stuff, positioning,

pressing, recovery runs, runs into space, fundamental tactical

contributions often missed in basic stats.

Merged data gives a much richer picture of development.

So it sounds like data volume isn't the main problem today,

it's the lack of structure, integration, and strategy.

What does AI actually do once an organization does have that

solid foundation? OK, once the data is properly

structured, integrated, accessible AI becomes incredibly

powerful. Jean Baptiste Elliot described

how it ingests these vast complex data sets, adds context

by cross referencing, provides granularity impossible for

humans. The key benefit?

AI moves beyond raw data feeds or static dashboards, which he

rightly said are useless to a busy coach.

They don't have time for. That instead AI flags the most

interesting info, identifies subtle patterns humans might

miss, provides recommendations, highlights areas needing human

attention. How does it connect those

different data points you mentioned?

He used a great example. AI flags a player with high

training load, plus reports poor sleep, eating for days, plus

shows specific on pitch red flags.

Maybe fewer sprints, more errors.

AI I connects these disparate pieces from different sources

that an analyst might see in isolation and raises an alert.

Potential risk of injury, burnout, underperformance.

It supports decision making with better, faster detection.

Improves planning for training load and recovery.

Enhances match prep by highlighting key insights.

That integrated insight is clearly where the real value

lies. Let's pivot now to how AI impact

another massive area fan engagement and creating new

business models. Adnan Anakif gave insights here

from the Federation perspective, a key concept the move towards a

unique fan ID for each supporter seen at recent World Cups.

The idea is treat each fan individually, give them a

personalized experience. He gave examples from Qatar

where fan ID linked to Visas digital wallets for services,

making the fan journey more seamless.

So. Moving beyond just anonymous

crowds to recognizing individuals.

Exactly. Building a relationship based on

recognition across different touch points.

Beyond basic services, it enables more immersive

experiences. He mentioned examples from

American football like AR filters letting fans take photos

seemingly next to players. While real time AR overlays onto

the pitch are still less common in football, the potential's

there to enhance the live experience.

The fan experience isn't just the 90 minutes, is it?

Not at all. It's the whole journey before,

during, after admin. And a Keef gave a relatable

example using tech and AI to optimize the stadium experience,

like mobile ordering for food and drink to cut queues so you

don't miss the action. Digitalization streamlines these

operational bits. So how does this focus on

understanding and engaging fans translate into actual revenue?

Jean Baptiste Daliate had some key insights on the money side.

He stressed a fundamental point. Data is now a crucial currency

in the sports economy. Not just on pitch data, but

critically off pitch fan data. Info in CRM systems, those

databases about fan interactions and CDPS, custom data platforms

which integrate fan info from different sources into one

profile. OK, without this comprehensive

fan data, a club or leagues value proposition to sponsors,

investors, it drops significantly.

He used a specific high profile example didn't he?

The Barcelona Spotify one. Yes, exactly.

Barcelona has a massive global following, like 600 million

social media followers. Huge number.

But when Spotify did their homework, they look for the

tangible, addressable audience. The ones they could actually

con. Right, the qualified fans in

their databases, fans they could profile and engage directly.

That number way smaller, around 3,000,000.

Investors like Spotify aren't just wowed by big passive social

media numbers. They assess the tangible value

of the audience they can reach and market to effectively an

audience with a calculable lifetime value.

Owning, structuring, capitalizing on this first party

fan data is critical for demonstrating and growing

commercial value. That makes total sense.

Investors want quantified ROI requires understanding the

actual customer base. Where does the revenue

specifically come from using this data?

Multiple big channels First, the on pitch performance data

itself. Leagues sell this detailed data

to companies like Sportradar Genius Sports.

They use it to power betting fantasy sports enhance

broadcasts with real time stats. Direct revenue for rights

holders. Second, media rights, often the

single biggest revenue source in today's fragmented media world,

keeping fans engaged. Subscribing requires highly

personalized immersive content that relies heavily on merging

the rich on pitch data with enriched fan data.

The zero and first party data collected directly from fans via

loyalty programs, apps, interactions.

So connecting what happens on the field to the fans specific

interests off it. Exactly.

Data let's organizations activate fans effectively

before, during, after the match. Jean Baptiste Elliott gave the

example. A fan known to love a specific

player gets personalized notifications, highlights, maybe

alerts about merchandise drops for that player.

This targeted engagement is only possible if you know who the fan

is and what they care about. Requires effective data

collection, segmentation analysis.

And Trig Agnes saw this whole fan engagement and monetization

area as opening up big new market opportunities for

startups. Definitely, he noted.

AI is creating distinct new B to C&B to B markets and sport fan

engagement tech is a major growing segment separate from

performance deck. He circled back to that Canadian

D2 League example. Right with the influencers.

Using influencers to share AI generated player data and

highlights online. A deliberate strategy to engage

fans not at matches. Help them know players follow

the league. Fostering engagement through

accessible data. The real time stats in La Liga

broadcasts, Another example designed to fuel fan discussion,

enhance viewing, create more value around the broadcast.

And he specifically mentioned a growing recruitment market in

North America using AI. Yes, a significant market,

especially in North America, using AI for recruitment between

players and colleges or academies.

Players compile AI, analyzed data packages, highlight reels

using automated tools. They share this objective info

remotely with recruiters, crucial for getting scouted,

maybe getting scholarships or contracts without needing in

person. Evils for everyone.

Jean Baptiste Alliant also added that crucial point about AI tech

becoming more accessible globally.

How does that impact these markets?

That's foundational for a lot of this growth, he pointed out.

AI tech is becoming more of a commodity.

Things needing massive investment 5-10 years ago are

now much cheaper or more robust thanks to AI.

Automated broadcasting is a prime example.

What needed multiple camera OPS, Complex production now done with

sophisticated automated AI cameras that follow the action

even cut views intelligently. He mentioned the Australian

federation automatically broadcasting something like 3500

matches a year across various levels using this.

Wow, that must unlock totally new revenue streams for those

leagues. Absolutely.

Makes broadcast viable where traditional methods were too

pricey. Enables new sponsorship models

tied to AI detected metrics or events.

Fuels OTT streaming platforms. They can affordably broadcast

huge amounts of content, then serve dynamic targeted ads based

on viewer data in game events. Plus, technologies like live AI

translation open up content to new languages, dialects

dramatically increases reach monetization potential in new

territories. The increasing accessibility

means advanced capabilities, broadcasting, scouting, analysis

aren't just for the richest anymore.

They're attainable much wider, including affordable global

scouting by buying data sets and using AI to analyze them.

It really sounds like tech is becoming a democratizing force

in some ways, lowering the barrier to entry.

Let's turn now to the investment side.

How are public bodies and private investors looking at AI

in sports? How's the ecosystem developing?

Adnan Anikiev gave us the Federation perspective on

investment criteria. Yeah, he emphasized.

For a public body like a federation, investment decisions

in tech like AI are very pragmatic.

There must be a clear, specific need, a cadisage, A defined

problem the tech solves effectively.

Innovation for innovation's sake isn't the goal.

It has to address a real challenge or opportunity.

What was his main example for the Moroccan federation's AI

investment? Their core use case is scouting

the diaspora Moroccan players scattered across Europe, North

America, elsewhere, relying only on human scouts, traveling

everywhere. Logistically, financially

impossible. AI helps filter that vast pool,

especially from leagues, is not typically filmed or tracked, he

noted. Getting data from these unfilmed

sources needs some cleverness. Maybe automated cameras,

crowdsourcing, finding innovative ways to capture info.

And once they spot potential players, how does AI help track

them? AI analyzes unstructured data

from traditional scout reports, usually written text, subjective

notes, helps extract useful structured info for a database,

aids decisions on who needs more attention, and crucially, once a

player's on a watch list, AI tools help track their progress

over time, integrating any available data feeds from their

club or league flagging new info.

This long term data-driven follow up is essential as these

players aren't always under direct federation control.

Keeping an updated profile is tough without tech help.

What other factors influence their decisions on adopting AI?

He mentioned following international standards like

FIFA Tech certifications, benchmarking against other

leading federations and clubs. But critically, any potential AI

app must be thoroughly tested against the federation specific

requirements and football philosophy.

Customization is key. Exactly.

An off the shelf AI might analyze data or define stats in

a way that doesn't fit their coaching principles.

He's stressed needing to work closely with tech providers to

ensure the AIS lexicon, its analytical framework, aligns

with their unique needs. Their footballing identity

validation against their specific context is vital.

That makes perfect sense. The Tech needs to speak their

football language. How does the startup ecosystem

like Tarik AGDA's company find funding in the Sports AI space?

Tarik AGDA gave insights from Canada, noted governments

globally see AI and tech as future economic drivers,

investing heavily. While he mentioned a potentially

huge figure for AUS Gov tech investment generally the core

idea is public funding plays a role for private investment.

He contrasted Canada and the US. The US has a much bigger private

tech investment scene, prominent incubators like Y Combinator

providing early funding, mentorship connections for

equity, accelerating growth. And he saw potential in Africa,

too. Yes, specifically highlighted

Africa, particularly Morocco. Large, young population, highly

competent people, very smart, he said.

Especially Moroccans and deep passion for sport football.

That mix is why his company chose Casablanca sees it as a

strategic hub with strong growth prospects for sports AI.

But finding the right investment partners for a niche like sports

AI seems like a specific challenge itself.

It is, he stressed, a key challenge is finding investors

who get the sports industry. It's dynamics, unique market

characteristics. Sport isn't traditional B2B

sauce, vital to find partners who see the tech potential and

understand the operational realities.

Market nuances ensure investment aligns with the company's vision

and the sports market specific needs requires a more tailored

funding search. What about broader market

signals for sports investment overall?

John Baptiste Elliot in a generally positive view, but

with caveats. His analysis sport is

increasingly seen by sophisticated investors as an

underperforming asset class, basically an industry yet to

fully optimize its commercial potential.

They see significant growth room drawn by prospects of

substantial returns potential multiples of by 4, by 5, even by

10. And they believe that potential

comes from professionalization, data-driven approaches.

Exactly the core thesis Sport often run like associations, has

untapped margins, efficiency gains unlockable through more

professional data-driven management leveraging tech and

AI. However, for startups seeking

significant VC funding, Jean Baptiste Elliot offered critical

advice. While sport is compelling a

great proving ground, large VCs typically need to see a clear

path for applying the startups Cortec to other larger sectors

too. So use sport as the start but

show expansion potential. Precisely sport is a powerful

start demonstrates real world value, but often to niche alone

for the scale of investment needing massive market potential

to mitigate risk. Investors want to see how the AI

or data platform for sport can also apply to related sectors

broader entertainment, retail, fan merchandise, experiences,

health, Wellness, media, tech. That's a really practical

insight for sport tech entrepreneurs.

It is. He also highlighted

consolidation. Larger sport tech companies are

buying smaller specialized firms to quickly build capabilities,

offer more comprehensive services across the value chain.

Tracking event data, fan engagement, broadcast solutions

shows a maturing dynamic ecosystem and finally a

significant shift rights holders themselves.

Big leagues, rich clubs like NBAMLS, City Football Group, PSG

are increasingly investing directly in sport tech

companies. Interesting.

They recognize the value being created, want early access to

tech, and potentially a share in the financial upside previously

going mainly to external VCs. That certainly signals

confidence when the biggest players put their own money into

the tech shaping their future. Now as promising as this all

sounds, the material also highlighted significant

challenges. One critical issue from the Q&A

raised by an audience member and acknowledged by the panel was

the ongoing problem of data standardization.

You mentioned that FIFA provider test issue.

Yes, this is a fundamental hurdle.

Undermines potential benefits The core problem?

Different third party data providers.

The company's capturing performance data often use

different definitions for even basic events.

Like what? What's a long pass definition?

Can vary tackle shot on target, how pitch zones are defined,

Even how metrics like pressing intensity are calculated can

differ significantly. The audience member referenced a

FIFA test where multiple providers analyze the same match

video. The only stat they universally

agreed on was the number of goals scored.

That's honestly shocking. Goals are the most basic thing.

It shows how wide the discrepancies can be beyond the

most undeniable events. Moving to nuanced actions

performance metrics, FIFA's Football language initiative

tries to create common terms, standardized definitions.

But panelists noted for sophisticated high level

analysis by top clubs or federations with specific

philosophies. This standard language might not

be granular enough or precisely aligned with their internal

coaching terms and analytical needs.

So if the raw data coming in is inconsistent, what does that

mean for organizations trying to build a robust data strategy and

use AI? And who deals with this?

This inconsistency creates huge challenges for integration

analysis. Hard to compare data across

competitions, time periods, players of definitions differ.

This points to a critical need raised by the audience member

and strongly affirmed by the panel.

Clubs and federations need specialized data expertise.

Data scientists, data engineers who can work with raw data,

understand the nuances, inconsistencies between

providers, work with providers to align definitions maybe, and

crucially translate the technical AI output into

actionable insights and language coaches and staff can actually

understand and use. A coach can't realistically work

with complex raw data or technical reports.

They need distilled, relevant intelligence.

So these data experts are the vital bridge between the tech

and the humans making decisions. Exactly, and Adnan Anakif

particularly stressed customization and validation

here. Organizations most rigorously

test any AI application or data service, ensure it analyzes data

according to their specific policy philosophy.

The precise needs of the coaching staff can't just buy an

off the shelf tool with fixed definitions.

Need adaptable applications aligned with your internal

framework? What coaches actually need.

Jean Baptiste Dallied offered a historical take on why this

standardization mess exists. He explained that for years,

rights holders, leagues, federation's clubs basically let

the third party data providers define football's data language

providers built proprietary systems, definitions based on

their tech, what they captured. Now getting everyone to agree on

and switch to a common standard retroactively incredibly hard.

He feels it's time for rights holders to reclaim control, step

back into the driving seat, define and impose their own

nomenclature, standards based on their business needs, strategy,

football identity, rather than operating on definitions

dictated by external tech providers.

That framing makes a lot of sense.

A power dynamic around data, value and control.

Well, we've covered an immense amount of ground here, exploring

how AI is truly reshaping football from so many angles.

It's clear there's incredible potential.

Enhancing performance, Safeguarding health.

Creating richer fan experiences. Unlocking new business models.

And as we've discussed, unlocking that potential isn't

just about buying the latest AI. It fundamentally requires

strategic focus on data itself, collecting and effectively

structuring, integrating it, properly owning it.

AI is a powerful engine, but the human element, coaches,

analysts, decision makers remains absolutely critical.

The ones who will thrive are those who can best leverage

these tools to enhance their human expertise.

Circling back to that initial provocative question by 20-30,

will those in football who haven't fully embraced AI be

left behind? Based on everything we've heard

from John Babcy's Dalia Torik, Agda Aden, Anna Keefe, the

answer seems well, unequivocally yes.

The trends, the capabilities, the acceleration rate suggest

ignoring these shifts means a significant, maybe

insurmountable, competitive disadvantage.

And perhaps a final thought to leave you with building on the

perspective shared. While technology is advancing at

a staggering pace, poised to transform so much the core

spirit of sport that raw confrontation of skill, tactics,

human resilience, competition should ideally stay central.

The ultimate goal should be for AI and data to serve that spirit

to help and enhance the human elements, performance, strategy,

passion, not over shadow or reduce the fundamental

unpredictable nature of the game we love.

Striking that balance feels like the key challenge ahead.

That's a really profound point to consider.

Thank you for joining us on this deep dive, and we really

encourage you to keep thinking about how technology will

continue to shape the sports you care about.

AI's Transformative Impact on Football

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