AI's Transformative Impact on Football

0:00 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.

0:45 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.

1:13 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.

1:45 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.

2:19 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.

2:52 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.

3:23 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.

4:03 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.

4:40 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?

5:12 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.

5:42 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.

6:15 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.

6:47 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.

7:24 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.

7:50 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.

8:27 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.

8:54 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.

9:32 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.

10:01 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?

10:30 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.

11:01 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.

11:39 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.

12:07 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.

12:38 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.

13:23 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.

13:50 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.

14:17 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.

14:51 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.

15:23 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.

15:50 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.

16:19 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.

16:52 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.

17:24 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.

17:51 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.

18:18 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.

18:47 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.

19:18 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.

19:50 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?

20:19 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.

20:46 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.

21:20 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?

21:47 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.

22:19 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.

22:48 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.

23:21 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.

23:52 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.

24:27 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.

24:53 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.

25:27 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.

25:59 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.

26:31 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.

27:01 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.

27:32 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.

28:01 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.

28:33 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.

29:00 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?

29:29 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.

29:57 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.

30:30 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.

31:03 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.

31:41 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.

32:11 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.

32:45 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.

33:15 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.

33:43 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?

34:18 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.

34:53 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.

35:24 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.

35:54 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.

36:21 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.

36:50 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.

37:28 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.