LALIGA's Data Revolution: How Sportian Performance Uses AI and 3.5 Million Data Points to Win European Trophies

0:00 Welcome everyone to a really exciting deep dive. Today we're pulling back the curtain on something truly fascinating. Forget the itch side drama for justice a moment because honestly the biggest battles in football right now they might just be happening inside server rooms. That's so true. We're talking about this incredible fusion, this mix of high level science, massive amounts of data and you know, the beautiful, unpredictable chaos. It is football. Exactly. And we're zooming in on Spanish football, specifically a Liga, to figure out how they didn't just dabble in data, they built an entire system around it.

0:35 And the result? Well, let's just say it involves a lot of silverware. A lot of silverware is right. It's quite a story of and commitment that the platform they built has been a cornerstone for Spanish teams, leading to this frankly unprecedented era of success. We're talking 16 European titles since its implementation. 16 That's not just luck, is it that strategy, that's technology making a tangible difference on the European stage. Absolutely, and that success really points to the power of the engine behind it all.

1:04 For years, many of you will have known this system as media coach. It's been the standard since what, the 2010 eleven season, analyzing every single movement. Right Media coach has been the name synonymous with allergy data. But things are evolving, aren't they? There's a big change coming. There is. It's a significant transition starting July 15th, 2025. Media coach is becoming Sportion Performance. Now. This isn't just slapping a new label on it. It's much bigger than that. OK, tell us more. What's behind the name change?

1:33 It signifies a major strategic alliance. Allegia has partnered with Sportion, which is the dedicated sports technology division of the global giant Globin. So Sportion performance reflects this new, deeper integration and a commitment to, well, continuous innovation across the entire league. So, Sportion Globent, this sounds like a powerhouse partnership aimed at pushing the boundaries even further. Precisely. The goal isn't just analysis anymore. It's about driving this constant technological evolution league wide.

2:03 It's about institutionalizing that competitive edge through tech. And to really understand how they do that, you've got to look under the hood right at the technological foundation. Let's let's impact this portion performance ecosystem. Yeah. The ecosystem strength really comes from that three-way partnership. La Liga, Band, Sportion and crucially, Microsoft providing the cloud infrastructure and AI capabilities it's set up designed for constant growth. And who's steering the ship within La Liga?

2:32 That responsibility falls to La Liga's newly named Football Intelligence and Performance Unit. Think of them as the internal hub, the brain trust that overseas the whole strategy. They support all 42 professional clubs, Loviga, EA Sports and La Liga Hypermotion. 42 clubs getting this level of support and they're doing training workshops. Constantly. It's about ensuring every club, regardless of size, can leverage these tools effectively. They're integrating new features all the times, keeping everyone at the cutting edge.

3:01 OK, You mentioned continuous evolution, and for that you need fuel. Data, fuel. The sheer volume of data processed here must be astronomical. Oh, it is. The scale is almost hard to comprehend sometimes. Let's put some numbers on it because that really tells the story. Hit us with it. For every single match played in Elega, the platform captures over 3.5 million data points. Three and a half million per game. Law, game and that raw data is then processed, analyzed, crunched, eventually generating more than 6400 unique metrics that are made available to the clubs.

3:36 6400 different ways to look at a match. Exactly. And if you zoom out across an entire season, this system turns out over 112,000 analytical reports. It's just a fire hose of information. 112,000 reports. How on earth do they capture that initial 3.5 million data points with the speed and accuracy needed for it to be useful? That feels like the core challenge it. Is, and the answer lies in some pretty sophisticated tech installed in every stadium. It's primarily an optical tracking system that use a combination of cameras.

4:06 OK. What kind of cameras? So you've got three main tactical cameras sort of following the general play like you might see on a broadcast feed, but position higher, wider, but the real precision comes from up to 16 high resolution fixed perimeter cameras. 16 fixed cameras dotted around the stadium. That's right, these are the workhorses. They're constantly tracking the position of every player on the pitch, both teams, plus the referees and of course, the ball itself. And how frequently are they capturing this positional data?

4:36 This is key. 25 times per second. That's 25 Hertz. That frequency gives you incredibly granular data. You're not just seeing where a player was, you're seeing their exact trajectory, their micro movements, acceleration, deceleration. 25 times every second. 25 times a second. OK, now I understand how you get to 3.5 million data points. That level of detail lets you analyze things like body orientation, angles of runs, the exact distance between players, things you just can't see reliably with the naked eye.

5:06 Precisely, it turns simple positioning into a rich data set for advanced analysis. Now historically, all this incredibly rich raw data, it was mostly kept within the Leaguers own analysis teams, right? The club's got the finished reports the output, but not necessarily the underlying raw numbers. Exactly. But that's undergone a massive shift recently with something called the Media Coach Sandbox. The sandbox. OK, what's revolutionary about that? Ricardo Resta, the director of Media Coach, calls it a game changer.

5:36 He does, and for good reason, think, feel like this. For years clubs were getting, you know, the beautifully Bay take the final analysis report. But increasingly they were saying, hey, we appreciate the cake, but can we get the flour, the eggs, the sugar? We want the raw ingredients. I like that analogy. They wanted to do their own baking, test their own recipes based on their specific coaching philosophy. You've got it. The sandbox provides exactly that. It's an advanced analytics environment running in a secure data bricks workspace where clubs get direct access to the raw data.

6:07 And raw data means. We're talking the fundamental stuff. Individual player coordinates, XY 25 times a second. Collective positioning data, specific angles of movement, the team shape metrics like the convex hull. That's the area the team occupies, Distances covered at different speeds, all the granular detail. OK. So they have the raw ingredients. Why is that such a big deal beyond just having more numbers? Because it unlocks true customization and automation, often using AI. Imagine a coach is obsessed with a very specific type of defensive press.

6:43 Maybe they don't just care about total pressures. Right, they want something more specific to their system. Exactly. Maybe they want to measure, say, how quickly do my 2 central midfielders close the space between them after we lose the ball in the opponent's half. That's a bespoke metric. Something you wouldn't find in a standard report. Probably not, but now in the sandbox the club's own analyst can use the raw positioning data, maybe apply some AI, and build an automated process to track exactly that metric tailored 100% to their coaches unique tactical ideas.

7:12 It bridges the gap between generic data and specific coaching needs. That makes perfect sense. It turns the platform from a provider of information into a toolkit for generating unique insights. And that leads us perfectly into the next phase. How do these tools actually land with the coaches? Let's move into Section 2 products and, crucially, the language of the coach. Yeah, this is so important because you can have the most powerful analytics engine in the world, but if the coach doesn't trust it or doesn't understand it or doesn't use it, it's basically worthless.

7:47 It stays in the analyst's office. Exactly. And La Liga seems very aware of this. The philosophy, particularly championed by Roberto Lopez, who coordinates a Ligas sports research department, is crystal clear. He stresses that the technology is a supplement, never a replacement for the coach. So it's about decision support, not decision making. Precisely. Lopez's phrase is that the goal is to help coach make fewer mistakes, to provide objective information, to challenge assumptions or confirm observations.

8:14 But ultimately, the final word always belongs to the coach. Data informs intuition, it doesn't override it. That cultural sensitivity feels key to why it's been adopted so widely. OK, so let's breakdown the actual suit of products and services they offer. How does it work in practice? Maybe starting with the heat of the moment during the match itself. Right. So during the game the primary tools are the live NRT near real time tools. These are typically delivered pitch side, often on a tablet or maybe a digital whiteboard in the dugout area.

8:46 Near real time, so not quite instantaneous. There's a slight delay, yeah, and you have to accept that the data quality and quantity in NRT might be a little bit lower than what you get post match. Processing millions of data points instantly and validating them is incredibly complex. OK, so if the NRT data isn't perfect, what's its main job during a game? It's about providing immediate critical alerts for decision support. The source has got a great example, high injury risk alerts. How does that work?

9:14 The system is constantly tracking physical metrics like high intensity meters run Sprint distance, number of accelerations for every player. Let's see if a star player absolutely crucial. It's the 70th minute an alert might pop up on the analyst tablet. Really to the bench warning. Player 10, has exceeded 90% of his typical high intensity distance for this stage of the game based on his seasonal load. Yeah. So it's not saying he's injured, it's saying objectively he's entering a known risk zone for potential muscle fatigue or injury.

9:44 Exactly that. It doesn't tell the coach sub him off. It presents objective data. Here's a calculated risk factor. Now the coach has to make a judgement call, right? Is the game situation critical enough to push him, or is it safer to protect him for the next match? That's the dilemma. The NRT tool provides the objective data point. The coach provides the experience, the context, the human judgement. It's a perfect blend. That's a really smart application of NRT. OK, so the final whistle blows, the immediate pressure is off.

10:15 What happens next? Well, almost immediately after the match, often literally on the team bus or maybe on the way at a press conference, the coaching staff might use the mobile application. The mobile app what for quick look UPS. Yeah, quick reviews. It serves a couple of purposes. 1 is just immediate internal debriefing, but another, perhaps more tactical use is preparing for the media scrutiny. Like a defensive tool against criticism. You could definitely see it that way. Imagine the team just lost 10 despite playing OK.

10:44 The narrative might be they look tired or they just didn't show up in the second-half. Right, the easy narratives. So the coach or an assistant can quickly pull up the mobile app and find objective data. Actually, look, our total distance covered was above average. Our number of penalty box entries was our third highest this season. It allows them to immediately counter subjective criticism with objective facts. Provides a data shield. I like that. OK so NRT for alerts, mobile for quick review and press conference prep.

11:13 But for the really deep post match dissection, where does the series analysis happen? For that, Roberto Lopez points to one tool as his personal favorite, and it seems to be the core of the analytical workflow, the desktop visualizer, which is called vision. Vision Why is this one the gold standard for the analysts? Because Vision is where the complex data finally starts speaking the coach's native language. Yeah. And what is that language? Video. Video. Absolutely. Coaches understand the game through watching it.

11:42 Vision's power is that it embeds all that granular tracking data, the 3.5 million points per game, directly onto the corresponding video footage. So you're not looking at spreadsheets or abstract charts, you're watching the game, but with layers of data visualized on top. Exactly. You might see player movement trails, distances between players displayed in real time, shaded zones indicating controlled space, maybe even probability metrics popping up during key moments, all synchronized perfectly with the video.

12:09 I can see why that's powerful. It bridges the abstract data world with the concrete visual world of football. It's critical you can show a coach the most elegant Power BI dashboard full of complex metrics, and honestly, their eyes might just glaze over. It's not their language. Show them three curated video clips using vision, highlighting a specific tactical pattern with the data overlaid. Instant understanding, instant engagement. Video is the universal translator, and Vision apparently takes this further with deep customization.

12:40 They talk about achieving the quadrature of the circle. Yeah, that's the Alleghay analyst term for it. It basically means hitting the bullseye, finding the perfect solution. Vision achieves this by connecting three things, the raw data, the analytical visualizers or widgets, and the video itself. And the crucial part is that it's highly customizable. How custom are we talking? Extremely Lugay doesn't just hand over the software. Their analysts provide what they call 360° Consulting. They actually go to the club, sit down with the coaching staff and the club's own analysts.

13:14 Right partnership, not just vendor relationship. Exactly. And together they design and build custom vision dashboards tailored specifically to that club's football philosophy and the coach's specific needs and points of focus. So a team focused on counter attacks would have a completely different vision setup than a team focused on high possession. Completely different. The counter attacking teams dashboard might automatically flag moments of rapid transition, measure the speed of the forward runs, show spaces exploited behind the opposition defense.

13:44 The possession team's dashboard might focus on passing networks, player spacing in the build up phase, metrics around breaking defensive lines through intricate passing. That level of personalization making the tool fit the coach's brain rather than forcing the coach to adapt to a generic tool that feels like a huge competitive advantage. Elygia certainly sees it that way. They view this deep customization and consultancy as a key competitive differentiator. It ensures the powerful data is actually used effectively because it speaks the specific dialect of each individual coach.

14:18 OK, this is fascinating. We've got the data engine, we've got the tools. We understand the importance of tailoring it to the coach. Now let's push into the really cutting edge stuff. Section 3 AI driven strategic application and predictive analytics. How is artificial intelligence changing the game here? AI is really the catalyst, taking this whole operation to the next level. It represents A fundamental paradigm shift, moving beyond just descriptive analytics telling you what happened towards genuine predictive and even prescriptive capabilities.

14:48 And the key advantage of AI is speed and scale. Absolutely. Think about those 3.5 million data points per game. No human team could possibly sift through all that, connect all the dots, identify all the subtle patterns in real time, or even post match efficiently. AI algorithms excel at processing these vast data sets incredibly quickly and accurately. So it's supercharges the whole decision making loop. Where are we seeing the biggest impact of AI and football strategy right now? Let's break down the core functions.

15:18 OK, first up, and arguably the most exciting, is predictive analytics. Now, this isn't about redicting the final score like 2-1, it's more granular. Redicting what then? Redicting opponent behaviors, tactical adjustments, even likely player actions based on context. For instance, AI can analyze historical data and real time patterns to predict with a pretty significant degree of accuracy when a specific opposition midfielder is likely to drop deeper to cover space based on the ball's position and his teammates movements.

15:48 OK. So if you can predict that micro adjustment. You can preempt it. A coach might get an alert or see a pattern suggesting this will happen and instruct their forward to make a run into that predicted space, maybe seconds before the opponent even moves. It's about anticipating and exploiting tactical shifts before they fully materialize. That's next level chess. What about focusing on your own players? That brings us to the second core function, player performance optimization. AI models are brilliant at analyzing individual player data over long periods.

16:20 Not just match data, but training load, biometric data where available, movement efficiency. And what does it do with that? Create personalized plans. Exactly. It can identify patterns indicating fatigue risk, suggest modifications to training intensity, or even pinpoint specific biomechanical inefficiencies that might lead to injury. If player A always shows a drop off in Sprint speed after playing 390 minute games in a row, the AI flags this and suggest a tailored recovery or training plan for the following week.

16:51 So it's about maximizing potential while minimizing risk player by player, yes. Which leads to better team performance overall, fewer non contact injuries, and actually higher player satisfaction because they feel the training is truly individualized. Makes sense, and obviously AI must be transforming how teams find these players in the first place. Absolutely. That's the third area scouting in a recruitment. Traditional scouting, you know, relied heavily on the eye test on subjective human judgement.

17:17 Watching hours and hours of video, going to games, all. Right now, AI platforms can analyze performance data from thousands of players across dozens of leagues using far more metrics than just goals and assists. They look at efficiency, decision making under pressure, spatial awareness metrics derived from tracking data, things that are hard to quantify visually. So we can find hidden gems players undervalued by traditional methods. Precisely. It can identify talent much more efficiently and objectively.

17:45 And perhaps even more powerfully, AI models can attempt to project long term player development paths. They might identify a 17 year old whose specific movement signature and decision making profile strongly correlate with success in the coaches desired tactical system five years down the line. Wow, turning recruitment into a much more data-driven predictive science. OK 4th area. How does AI impact the day-to-day grind? That would be optimizing training. AI can simulate various game scenarios based on the upcoming opponents likely tactics pulled from the predictive models we discussed.

18:19 Virtual practice. Sort of. It calculates the probabilities of different outcomes within those simulated scenarios. This allows coaches to design training sessions that aren't just generic drills, but highly specific scenario based exercises targeting the most likely situations and challenges they'll face in the next match, according to the data. So practice becomes much more focused and efficient. It sounds like AI is becoming this indispensable analytical copilot. That's a great way to put it, especially with real time decision support.

18:49 We talked about the NRT alerts, but AI enhances this significantly. During the game, AI systems are constantly processing the live data stream, looking for emerging patterns or deviations. And offering suggestions. Yeah, offering tactical suggestions, not commands, but options. Maybe suggesting a specific substitution based on fatigue data and the tactical situation, or highlighting a sudden weakness in the opponent's formation that's just appeared and allows teams to react more quickly and effectively to the dynamic, unfolding chaos of a match.

19:21 That analytical power is immense, but as we hinted earlier, it's not just about the machine. Let's transition to Section 4, which is crucial advanced use cases applying expert criteria. This is where human intelligence directs the AI, right? Absolutely, the LL analysts are very clear on this. AI is a tool, but it's value comes from the quality of the questions you ask it. It needs expert human criteria to guide it towards meaningful insights, not just noise. So the human analyst defines what the AI should look for.

19:53 Let's look at case one optimizing space using the Boronoi spaces model. Can you briefly explain what that model does? Sure. In basic terms, a Boronoi diagram divides the pitch into zones of control. For any point on the field, the model tells you which player is closest to that point, so it maps out who owns which area of the pitch at any given instant. Sounds useful for understanding space, but what were the problems with the traditional versions of this model in football? The traditional Boronoi models were too simplistic for football's specific rules in physics.

20:21 They had two major flaws they completely ignored offside and they assumed all players moved at the same speed or had the same acceleration. Right, a space isn't useful if it's offside. Exactly. If the model highlights a huge open space, but receiving a pass there would trigger an offside flag, that space is tactically worthless. The advanced Allega model automatically annulls basically ignores any calculated space that is in an offside position relative to the ball and the defense. It makes the spatial map instantly more realistic.

20:52 OK, that fixes the offsite problem. How did they address the speed and acceleration issue? They moved away from generic assumptions. Instead, they assign unique maximum speed and crucially, maximum acceleration values to each individual player's ID based on their actual track performance data overtime. So it's like giving each player their own custom physics engine in the model, reflecting how they actually move, like in a FIFA game, but based on real data. That's a perfect analogy, and interestingly, the analyst found that for controlling these Bornois spaces, especially in tight areas, a player's acceleration, their ability to burst into a space quickly, is often more critical than their absolute top speed.

21:32 Football is often one in those first few yards. That's a really sharp insight derived from applying football knowledge to the model. Now. There was also fascinating advice for analysts building these models. Prioritize false positives over false negatives. Why is it sometimes better for the model to be slightly wrong but helpful? This comes down to human psychology and maintaining trust with the coaching staff. Imagine you're the analyst and you present the coach with an 8 clip video package showing defensive errors identified by your model.

22:01 OK, now if the coach vividly remembers one absolutely disastrous defensive breakdown from the game, A really obvious error, and your fancy model missed it, a false negative, what happens? The coach loses faith. Your system missed the most important mistake. Instantly, credibility shattered. So the pragmatic advice is it's actually better to show an 8 clip package where maybe two of the clips are borderline calls. Technically false positives. The model flagged something that wasn't a huge error but still relevant.

22:31 Rather than missing that one glaring mistake, the coach remembers. So over deliver slightly on potential issues rather than under deliver and miss something obvious. Precisely. You maintain the coach's trust that the system is seeing the game realistically, even if it's occasionally a bit oversensitive. It's about building that human computer relationship. Very insightful. Let's look at case 2, the effect Iman model, the magnet effect. What's that measuring? The Effect Iman model quantifies how strongly defending players are drawn towards the ball carrier.

23:02 Like a magnet, it measures defensive compactness or sometimes over compactness around the ball. Why is that useful? It's incredibly useful for scouting opponents. If your analysis shows that a rival teams midfield tends to have a high Iman score, meaning they get sucked towards the ball, you know where the space will likely open up. On the opposite side or behind them? Often, yes, on the weak side or in the gaps they vacate. So you can prepare specific tactics like quick diagonal passes or runs into those predicted spaces to exploit that known tendency of your opponent.

23:34 The model can also measure the opposite how well A-Team organizes around the ball when they win it back. Super specific tactical preparation based on quantifiable opponent tendencies. OK. And case 3 involved intersecting lines using convex hull analysis. What's the focus there? Right. The convex hull, as we mentioned, is basically the shape outlining your team's overall position on the field. This specific analysis, though, doesn't just look at the whole shape. It focuses on the crucial intersections, the zones where key lines of the team overlap.

24:06 Which lines are we talking about? Specifically, the intersection between the defensive line and the central midfield line, and also the intersection between the central midfield line and the attacking line, particularly as the team gets closer to the opponent's box. And why is that intersectional space so important? The analysis revealed that the most dangerous attacking plays, the ones most likely to resulting goals, often involve combinations and movements within these specific intersectional zones near the penalty area.

24:32 It's the area where lines are being broken, where defenders and midfielders are forced into difficult decisions. So it pinpoints the highest value real estate on the pitch for creating scoring chances, moving beyond just saying attack the final third. Exactly, it gives a much more precise geographical target for attacking strategies. These cases really highlight how human expertise guides the AI to find genuinely actionable football insights. They really do. But we need to be balanced. This whole system isn't perfect.

25:04 There are significant challenges. Let's move into Section 5, the obstacles and the philosophical debate of AI. What are the major hurdles? Well, the first and perhaps most fundamental is the issue of imperfections of data. The old saying applies Garbage in, garbage out. AI models are utterly dependent on the quality of the data they receive. And getting clean, reliable data from a chaotic football match isn't always easy. Not at all. Think about poor weather. Heavy rain, snow, even dense fog can significantly impact the accuracy of the optical tracking cameras, potentially corrupting the data feed.

25:38 And infrastructure differences. That too. While Allega aims for standardization, there might still be subtle differences or perhaps older camera systems in some stadiums compared to others. Ensuring consistent, high fidelity data capture across every single venue under all conditions is a massive, ongoing challenge. OK, so data quality is one thing. What about actually making sense of it? The interpretation challenge. This is huge. As Roberta Lopez pointed out, data needs context. Knowing a player sprinted 50 meters is meaningless unless you know why.

26:09 Was it a brilliant attacking run or was he caught out of position and desperately tracking back? AI struggles without why? It struggles immensely with nuance and context, especially those split second decisions made under pressure, influenced by factors like opponent positioning, game state, fatigue, maybe even just a gut feeling. AI can measure the what, but understanding the why behind complex human actions in a dynamic environment is still very difficult. Which leads directly into the cultural issues like the black box and trust issues.

26:38 Coaches aren't always comfortable with answers they don't understand. Exactly. Many advanced AI algorithms, especially deep learning models, can function like a black box. They produce an output, a suggestion, a prediction. But the exact step by step reasoning how they reach that conclusion can be opaque even to the experts who built them. And that's a problem for adoption. It's a major barrier. Football coaching relies heavily on experience, intuition, and clear cause and effect reasoning. If a coach gets a tactical suggestion from an AI they don't fully understand and it goes against their gut feeling developed over decades, well, they're unlikely to trust it, especially in high stakes moments.

27:18 It creates resistance and there's a financial barrier too, isn't there? This tech isn't cheap. Not at all. Developing and maintaining this level of AI capability, plus the required infrastructure, the high res cameras, powerful servers, sophisticated sensors, requires a substantial ongoing financial commitment. This can definitely create a gap between the wealthiest clubs and smaller clubs, even with La Liga's efforts to democratize access. Plus you need highly skilled staff to run it all, which adds to the cost.

27:48 And all this feeds into cultural resistance. Football is a traditional sport in many ways. Deeply traditional, it's history is built on the genius of coaches, the intuition of players, the feel of the game. Embracing data-driven AI assisted decision making requires a significant shift in mindset for everyone involved. Roberto Lopez joked that coaches like Ancelotti or Simeoni aren't likely to take a Power BI course. Exactly. The onus is on the analysts to bridge that gap. They need to learn to speak the coach's language.

28:15 The source called it learning German. If the analysts speak Chinese, they need to translate complex data insights into simple, visual, actionable formats, usually video clips that resonate with the coach's existing understanding of tactics and players. So analysts need tactical understanding as much as data science skills. OK, perhaps the biggest philosophical hurdle is the game itself. The chaos of the game, it's inherent unpredictability. This is fundamental. Football is fluid, dynamic, chaotic.

28:46 So many critical factors are incredibly hard, maybe impossible to quantify reliably. Things like player creativity, mental resilience, team morale, the emotional state of individuals on any given day. How do you measure confidence or the impact of a player having a bad night's sleep? You really can't, not systematically anyway. There's another layer. Players operate in a hyper competitive world. They often actively hide weaknesses. What do you mean? Well, if a player is struggling with a personal issue, stress at home may be grieving a loss.

29:15 They're often reluctant to share that with the club hierarchy for fear it might be seen as weakness or cost them their place in the team. So crucial data about their true mental and emotional state might simply be unavailable. Even if you had a way to measure it, this prevents AI from having a truly complete picture. All of this builds towards what you called the paradox of prediction. Can you explain that? Yeah, it's a fascinating thought experiment. The analysts involved acknowledge that actually prescribing exactly what to do to guarantee a win is basically science fiction.

29:46 Why? Because if you could. Because if a perfect predictive model existed 1 they could say with 100% certainty if you do XY and Z you will win 20 what would happen? The other team would get that information or figure it out and change their strategy to counter XY and Z. Precisely the moment a perfect prediction becomes known, it influences behavior in a way that invalidates the prediction itself. Knowledge of the future would change the future. So perfect prediction in a competitive environment is logically impossible.

30:17 It seems to be. And the deeper philosophical point is, if football did somehow become 100% predictable, if the outcome was known in advance, would anyone watch the chaos, the uncertainty, the possibility of upsets? That's the magic. Analytics aims to reduce uncertainty, but eliminating it entirely would kill the sport. A powerful reminder of the limits of data in a human game. OK, mindful of those limits, let's look ahead. Section 6. Ethical concerns and the future horizon. What are the key ethical considerations with this level of data collection?

30:50 This is becoming increasingly important as data collection gets more granular, touching on biometric and physiological data. Player privacy is paramount. Clubs need clear consent policies. Where do you draw the line? Is tracking sleep quality or stress levels fair game or is that confidential medical information? Clear boundaries are essential. That's a big one. What? Else algorithmic bias and fairness. If only wealthy clubs can afford the absolute cutting edge of AI analysis, does that create an unbridgeable gap, undermining competitive balance and fairness?

31:21 Well, your standardized platform helps mitigate this, but it's an ongoing concern across sports tech. And the risk of over reliance devaluing human expertise. Yes, the human judgement factor. There's a risk that over reliance on AI could sideline the invaluable experience and instincts of seasoned coaches and scouts. Find the right balance. The optimal fusion of talent and technology is the goal. It's not AI versus human, it's AI enhancing human capability. So the winning formula is talent plus technology.

31:51 Looking ahead, what emerging AI applications are they exploring, particularly with that Globeint and Microsoft partnership using Azure Open AI? They're actively piloting several exciting generative AI solutions using models like GPT 3.5 and GPT 4. One key area is enhancing accessibility and engagement. Like the NRT multi language subtitles. Exactly, using AI like Whisper for transcription and GPT models for translation, they're working on generating automatic subtitles in multiple languages for live match broadcasts almost instantly.

32:23 This is huge for global audiences and particularly for inclusivity like helping people with hearing impairments. Follow the commentary. That's a fantastic use case. What about other content? Automatic sports content translations. Think about the sheer volume of articles, social media posts, video descriptions Allajet produces. Using Gen. AI to translate this content quickly and accurately into many different languages massively increases their global reach and fan engagement worldwide. And making content more relevant to individual fans.

32:51 Yes, that's the third pilot area. Match briefings and personalized content instead of generic match reports. Imagine getting a summary automatically generated by AI that focuses specifically on your favorite team, maybe highlighting stats for players you follow. Compare them to rivals, all tailored to your known interests. This deep personalization aims to build stronger fan loyalty and reduce churn. Very cool. Beyond Gen. AI, what other technologies are on the horizon for a training and simulation?

33:19 The big ones mentioned are augmented reality AR and virtual reality VR. The potential here for training is immense. Imagine players using VR headsets to run through specific tactical scenarios, defending a particular set piece, practicing penalty shootouts under simulated crowd noise and pressure without the physical fatigue of doing it repeatedly on the training pitch. Arkansas could overlay tactical instructions or ghost players onto the real training ground. It promises much more immersive and efficient tactical learning.

33:50 Wow. From tracking dots 25 times a second to VR training simulations, it's an incredible journey. So wrapping this up, the move from media coach to sports and performance, this deep commitment from Eliga, it seems aimed at securing the entire league's future competitiveness. That's the core strategy. By providing this advanced, standardized platform and consultancy to all 42 clubs, Eliga acts as a crucial democratizing factor. It helps level the playing field somewhat, ensure that competitive advantages aren't soley based on which club has the biggest budget for Tech.

34:22 It lives the analytical capability of the entire league. Ricardo Resta really emphasized this. R&D, the university partnerships, it all flows back to making the teams better on the pitch. Exactly. It translates directly, they believe, into increased competitiveness, more sophisticated tactics league wide and ultimately improve team performance across the board. It's an investment in the quality and appeal of La Liga football itself. A profound transformation using data not to replace humans, but to empower them to refine decisions and to push the boundaries of the beautiful game.

34:55 Beautifully put. It's about making the game smarter without losing its soul. OK, so you've heard how data is dissecting tactics, optimizing players, even predicting microbehaviors. But if this powerful sportion performance platform becomes the standard, the baseline for everyone, it leaves us with a fascinating question for you to ponder. In this new era of data-driven strategy, what single, unquantifiable human element, something the algorithms can't truly capture, will ultimately become the most valuable competitive edge in the next decade of football.

35:26 Think about that.