The Joy of Padel podcast with Vianney Dubois

Vianney Dubois’ Background and Passion

– Vianney introduces himself as a data enthusiast with a background in finance.

– Shares his journey from finance to sports data, focusing on padel as his most comprehensive project.

Personal Connection to Padel

– Vianney discusses his experience as an amateur padel player.

– Expresses frustration with the lack of detailed feedback beyond match scores.

Development of Padel Intelligence

– Vianney’s desire for better performance analysis led to the creation of a prototype.

– The project gained traction when a club director and TV commentator showed interest.

Business Model and Objectives

– Padel Intelligence aims to help players improve and engage padel fans.

– The goal is to convert match viewers into platform users for enhanced match insights.

Growth and Industry Challenges

– Vianney highlights the unique challenge of padel having more players than viewers.

– Discusses the need to convert padel players into fans to grow the sport.

Professional and Amateur Focus

– Padel Intelligence targets professional players and aims to expand to amateur levels.

– Vianney discusses the potential for AI and technology to enhance amateur player statistics.

Data and AI Integration

– Vianney explains the current manual data collection process and future AI integration.

– Emphasises the importance of combining manual tagging with AI for comprehensive analysis.

Key Performance Indicators

– Discusses benchmarks for left and right side players, including assists and error rates.

– Highlights the importance of understanding player contributions and performance dynamics.

Future Developments

– Vianney announces the upcoming launch of a public website for Padel Intelligence.

– The new platform will offer detailed player statistics and head-to-head comparisons.

Engagement and Contact Information

– Encourages listeners to follow Padel Intelligence on Instagram for updates.

– Minter Dial expresses enthusiasm for the future of padel and Padel Intelligence’s role in it.

To find out more about Vianney Dubois and Padel Intelligence:

• Find or follow PadelIntelligence on Instagram: @PadelIntelligence
• Check out their live tracking site, PadelIntelligence.Live (for the last rounds of the pro tournaments)

To listen to the show:

To listen to The Joy of Padel podcast, you can use the embedded player above, or go find it on Spotify, Apple Podcasts or any number of other podcasting services listed here.

Send in your questions or reactions:

Please send me your questions — as an audio file if you’d like — to nminterdial@gmail.com. Otherwise, below, you’ll find the show notes and, of course, you are invited to comment. If you liked the podcast, please take a moment to review and/or rate it! ¡VAMOS!

Further resources for The Joy of Padel:

Agustin Tapia | AI in padel | Alejandro Galan | amateur padel statistics | Arturo Coello | betting in padel | data analysis in padel | forced errors in padel | juan lebron | left side padel players | padel commentary enhancement | padel Fan Engagement | Padel Intelligence | padel match analysis | padel performance metrics | padel player assists | padel player benchmarks | padel player improvement | padel player positioning | padel statistics | padel tactics analysis | professional padel players | right side padel players | smash efficiency in padel | unforced errors in padel | Vianney Dubois | wearable technology in padel

This transcription comes courtesy of Flowsend.ai, an AI service for podcasters.

Minter Dial: Vianney, I am thrilled to have you on. I mean, really, tech and padel, it feels like it’s kind of a match made in heaven, to the extent that I’ve always said that while padel was born in 1969, it really came of age in the digital age. Let’s start with you, Vianney. In your own words, who are you?

Vianney Dubois: Well, thank you for inviting me first. I’m a data nerd. I’m a little bit nerdy. I don’t mind presenting me that way. I come originally from finance, and I guess I’ve worked the majority of my life with numbers often related to stock performance or stock market, and analysing performance of asset managers. But the brain is the same. But my passion really comes when it comes with sports data. I’ve done a number of projects related to sport and statistics in golf, in football, and then now in padel, which is probably the most articulate and the most in depth project that I’ve done so far.

Minter Dial: That’s beautiful. We will get into that. Vianney, let’s start talking about your relationship with padel. How did you get into padel? Why padel? Do you play? Do you enjoy the.

Vianney Dubois: Yeah, I’m a basic amateur player, and I was growing frustrated on a personal level to not have any feedback other than the score of the match, which I find sometimes can be quite misleading, and also the feedback from the. After the. With the bay, after the match of, you know, the impressions of the other players. And I.

Minter Dial: Well, hopefully with the bear.

Vianney Dubois: Right, of course, with the bear. And then I was. Because I’m mostly on the right side player, I find the right side player usually tend to not have been the shadow of the left side player, because the left side tends to finish the points with the winner, even though sometimes those points are being prepared by his partner. So I had sometimes the impression that we would win the game, but really, my contribution was not very important, and sometimes vice versa, that we might have lost the game. And I thought I played quite well, and most of the negative points were done by my partner. So I just wanted to also improve, and I wanted to have a tool that would allow me to pinpoint the areas in the match where I was losing points and also to be able to somehow some form of measurement of what was my real contribution as a player to the result of the match. And so I started. Bye. Doing a little prototype for myself. And then, yeah, it sort of became a serious project almost immediately, because as I was taking the stats of the first match ever, the director of the club, who was also a commentator for the Dutch tv in Padel, a good friend of mine called Marcel Bogart, said, what are you doing? So I explained, wow, this is fantastic. I’m commentating for the tv. We don’t have any stats. It would be amazing if you could somehow do the same for a professional match and send me the stats in real time, or more or less. So now it became the next objective, which we achieved within a month or so. And then I was in contact with a few professional players, because it turned out that I didn’t have any stats about my performance. Neither did the players. No one had statistics at all two years ago. And so suddenly this project, which was really just a local, little ambitious project, became something a little bit more entrepreneurial, if you want.

Minter Dial: Well, I’ve certainly enjoyed digging in a little bit on your statistics. So for those who are listening, tell us about the beginning of Padel Intelligence and what’s the business model you’re trying to achieve with it?

Vianney Dubois: Okay, so it’s a really good question. We’ve always had two very clear ambitions related to two types of targets, audiences. The first one, of course, is the player. And deep down in our DNA, we created pattern intelligence to help the players improve. We will continue improve our product to the point that our goal is that players who work with us end up winning more matches than players who don’t work with us. That’s the goal. Are we there? I don’t know. I think the players who work with us, we help them to some extent, but we’re not really there yet. I mean, I would lie if I said that the players that we work with have won because of us. I think we helped them to some extent, but clearly we want to improve. And that’s the goal of. The other goal is related to the padel fans, you probably know, because you deep into the padel industry, padel is a strange sport if we compare it to much more mature sports like football. In football, you have billions of people watching football, of which a very small proportion actually play in padel. It’s grown tremendously in the past few years, to the point that you have tens of millions of people playing padel, of which a tiny proportion actually watch professional padel. So it’s kind of completely reversed. So the industry somehow has to do something to bring these padel players into becoming padel fans. So that’s also our second objective, is to participate in that movement. And from a purely business point of view, what I want to do is to convert viewers of padel. So people watching a padel match into our users as well. So they watch a match and they come to our platform to actually follow the stats and to get some insights about what’s happening and some insight about the player and like, they’re getting way more information from us to make actually the whole watching the match more exciting than it is by just watching it. So that’s. These are really two clear objectives to help players to the point that when they work with us, they end up winning more. And for the fans, to the point that they want to come to our platform as they watch a match to get insights and engagement and excitement, these are two clear objectives. And in terms of business, clearly, number one, our big ambition would be to be the official supplier of statistics to promet padel, of course. And then immediately after this, we would love to work with the FIP and to come down to the next level down. I mean, so far we’ve concentrated on professional players, the top level, because it’s obvious from a business perspective, but the reality, if you call Tapia, you have a coach who’s watching you 100% of your time, of when you play. So if anyone doesn’t necessarily need help, it’s Tapia. If you like a 16 year old talent and you’re trying to make it to the big time, then you maybe have a coach helping you sometimes, but majority of the time you’re on your own. So if anything, these guys need our help even more than the top level. And I think there’s real excitement, potential if you bring the sort of analysis that we do of the top players to the next player, the next level down. We’ve done it to some extent here and there. I’ve worked a little bit with the dutch federation. I’ve also. We’re working with padel TV, which is a specialised tv in Scandinavia. So we followed some of the danish and swedish tournaments and sometimes we were able to take enough matches in a tournament to start to do rankings at the end of the tournament, who was the best player, who was the best smasher, and these guys love it and I love doing it, to be honest. I think it’s really exciting and our goal would be to have to cover the top 30 in every of the big padel countries, the national tools as well. So that would be the next level of our ambition, if you want.

Minter Dial: And I suppose within that, the business model is proving that it’s worth the value to pay for the analysis.

Vianney Dubois: Yeah, I think this will be the case when Padel is mature enough so that the players have enough money and realise that it’s helped them to actually pay sufficient money. That we can live from it. I think the first phase will be we’ll probably get our money through sponsors, through advertising, mostly sponsors. I think there’s a potential for brands who want to be associated with these talented emerging players and actually pay for them to have statistics. And as the sport grows and visibility grows of those players, et cetera, then that part of the revenue for us will grow as well. But ultimately, for sure, in a few years time, I want to bring sufficient value to the players that they recognise is something that they need to pay for. There’s no.

Minter Dial: It reminds me of Babalat who with their tennis rackets, they had these connected tennis rackets that were able to tell you the speed, the spin, the number of backhand forehands, overheads and such with power slice and stuff. And the question of course, ultimately was, well, what do you do with that data as a player? So for you, one of the gambles, I suppose, is figuring out how to convert data into valuable lessons. Because it’s like my benchmark may be 88% of first services of the pros go in okay, well, I noticed you hit 83%. Okay, well what does that say? Well, it says that you’re hitting less than the pros, but if I to get to 88, I may need to do slower serve, slower serves or less interesting serves, in which case I might get crushed because the returns are better. So to manage to convert the data into gold is where it’s at. I mean, where do you feel you’re in that process?

Vianney Dubois: That’s the best question one could ask. That’s something we ask ourselves constantly. So we are somewhere like inside that journey, but clearly still at the beginning. So for now, I can tell you the way we work with the players that we work with. So immediately after a match, but usually after a tournament, I think the reality of one match is difficult to analyse, at least to draw conclusions from after a tournament. It’s a little bit different after three or four matches to show the player how he played in all the different aspects of this game game compared to how he played before. Because beyond what I can tell them, they know what they’re working on. So we’re working with one player at this point. I don’t want to say who, but anyway, it’s a very top couple in the women world. So they are working with specific tactics with her partner together, like a specific sequence of players that should end up into a winner or to forcing an error from the opponent. So we’re able to actually see within the match how many times did that sequence actually happen? And we can do this by every match so we can go back in time saying, is this something? Are you winning new points more like this now than you were before? In this case, the work that you’ve been doing is actually, it seems to be bearing fruits or not. If not, why? So we are starting to have this kind of conversation. But for sure, for now, I guess the biggest value that we bring is to be able to analyse just the performance of one player in quite a few aspects of this match, compared to the benchmark, and analyse it dynamically across all the matches that displayed this season or now compared to different periods. And then we can drill into quite some detail. We can analyse how a player is playing, when he’s playing against a particular player, or we can analyse how a player is playing against, when he’s playing against lower level pairs compared to when he’s playing against top pairs. So they can actually see what difference where do their game sort of changes, for instance. And. But, yeah, so the journey is locked. We can see the. You can hardly see the end of the path to BNS. We started on that journey, but we’re hoping we can improve a lot.

Minter Dial: Well, it’s certainly an impressive series of data points that you’ve already accumulated from the number of. Of games points, men and women. And the benchmarks seem to very, very solid. I assume that those benchmarks I didn’t do in the good detail, but the benchmarks you have are female and male difference, because obviously very different types of games and the fact that you are giving all this data. I want to get into the methods in a moment, but I have to imagine that this is going to be gold dust for the betting companies.

Vianney Dubois: Yeah, I would imagine as well. And we are starting to discuss with some of the main players of the betting industry. Again, what I mentioned before, the fact that there’s still a very small, quite a small proportion of battle players who actually watch battle. So the whole industry needs to grow. And that, of course, includes betting. And there’s an element of chicken and egg there. But, yeah, there are a few betting players who start to seem to have started to put some form of interest into padel, at least to look into it. And we are talking with some of them to say, how can we help? There’s an element of having data so that they can calculate odds, which is the non sexy part. But if you want to make it more to bring people into betting and into watching padel, you also need to make something a bit more fun. More engaging. And this is where probably the depth of data that we have can actually help sort of creating some excitement before a match and sort of.

Minter Dial: Yeah, I mean. I mean, first of all, I wanted to say, via Nia, how I love your insight about the idea that there are so many players and not many watchers, as opposed to football and other sports. That’s most interesting. And when you look at the way it’s covered, I mean, the sport is entertaining by itself, but by golly gosh, if you don’t speak Spanish, I don’t know how it is in Dutch or I haven’t looked in French, but good lord, could we get some proper commentary? The level of analysis at the end of each match is four starers and winners. Yeah, unforced. Sorry, unforced areas and winners. And that’s all. They don’t do forced errors, which is something you do, and it’s not nuanced. I mean, it’s sort of so basic. And the type of commentaries that are made on it are like, oh, well, I guess they won nothing more than that. So I’m very much hoping that your data, your type of data anyway, will help foster some better commentary. We need to get some better people who actually know what they’re talking about when we do it. The fact that you’re a data geek, you’re nerd, as you say, and we talk so much about data, it leads me to need to understand how you’ve not converted it into AI. When you describe it to me, you have your ten. I can’t remember the name of them, but they have a name of these analysts who are certified, who really understand the difference in a forced, unforced error and such. But data and AI, I would say, go together. Where are you in the ideas of instituting some sort of AI in all of this?

Vianney Dubois: This is, again, a really good question. The answer is, I’ll answer in two parts, and I split the professional level with the amateur level at the professional level. And in fact, if you look into most professional, many professional sports, a lot of the statistics that we see in football, in basketball, a lot of it is taken manually because the level of accuracy is still higher when it’s a person doing it than it’s when it’s an AI. The AI will come and complement some of it. In some of the more advanced sports, of course, like in football, the runs, the placement element is not something that a person can do. You can’t measure the speed of Mbappe running just by looking at it. So in the most mature sports, then AI is coupled with manual tagging or manual intervention to bring the breadth of statistics that you see again in some of those bigger sports. And I think that will be probably the way we want to go, as in not replace what we do, at least not for the time being, but complement it with additional data that we currently don’t have using AI, or at least using video camera or radars or lasers. I’m not sure actually which technology would be the ones that I want. But for instance, for sure, I would love to have positional data, so we capture every time a player touches the ball, I would love to know where is, where he was when he touched that ball, where his partner was, and when the two opponents were, which in terms of analysis of tactics. And that will bring a whole new dimension. So for us, bringing AI into this is really a complementary exercise. And again, I’m talking about the professional layer, for the amateur layer, there’s a lot of talk about bringing some statistics to the amateurs. And again, the project started because me, as a real amateur, I was frustrated to not have any feedback. And I know because I’ve talked to a lot of people, a lot of people would love to have their statistics after they’ve played a match. And clearly the manual part that we do is not scalable. We can do it to some extent, but we cannot cover millions of people. That’s just not feasible. So there, for the amateur level, then technology will have to develop. Will it be AI as in video, or will it be more like what Babelak did, some form of wearable? Personally, I don’t have the answer to that question. I don’t know. I’m not sure. AI in video, which is where I think most of the industry seems to be going to, is actually necessarily the answer, because it requires a lot of investment. It’s also a very heavy infrastructure of calculation server data, which is very heavy. And I think that intuitively, like wearables nowadays, you can have with something that you wear in the racquet or on your wrist, quite a good level of information about how you play, especially because I think amateurs, for the majority of them, what they want is to improve their game. The result, at the end of the day, if they’ve lost 6262, to know that they’ve done 55 errors or 35, okay, it’s useful, but they just know they’re not playing well enough. They want to improve their bandeja. So if you had something that somehow gave them some insight on this, is why you’re missing your bandeja. And I think that could work as well. So for the amateur market, for sure, technology will have to come. Otherwise, data for amateurs will not develop. Will it be AI? Will it be more wearables or a combination of both? I don’t know. My impression is, for the professional level, it will be still a combination of manual tagging that we do and hopefully improved and more efficient with some form of AI, probably at this time, with the video recognition element of it to really complement it, to have the speed of the ball. This sort of thing would be really interesting to have.

Minter Dial: I suspect you’re right. I mean, the wearables integrating that must be really interesting. And when you look at how AI is working, for example, in medicine, parsing through reams of data to be able to find causality or coincidences, if you will, that might. Between the, well, in the 55 points we played, we had 16 times this sequence, and we lost 14 times. And the reason why was that we were not@the.net together or something like that. To be able to find that kind of granularity, if you can sort of get the AI to help you through the morass of data, because youre trying to find certain things. Help me find why we lost these points. What was going on? If you add inside the wearables, where they say, well, on those 14 points you lost, you were hitting at 42% speed or whatever, that would be interesting. Well, I love that.

Vianney Dubois: Just on that note, I had a discussion with a guy or a psychologist, so he was working with a few professional padel players to help them on the psychology element. And he was mentioning something like this. He said, I was watching the match of one of my players, and on the important points, my impression was that his volleys were just slower. That would be a great indicator to have. This is the average speed of your backhand volley. And look, on the important points, clearly, you have, you tense up, and the same shot that you play in the direction is half the speed. So that’s something would be great. But to have sufficient data for an AI to find these patterns, wow, you need to have a hell of a database to start with.

Minter Dial: Yeah, but, you know, fair point. All right, so, yeah, the future. Vianney, one of the things that really intrigues me about padel intelligence live is that you’ve got the ability to, and you count every shot, really. But you really lean into the trickiest of questions, which is a forced error, unforced error, and in pretty much every amateur conversation I have, that remains completely speculative, because what might be an unforced error for me is a forced error for someone else or vice versa because of the levels, you know, Tapia, he misses a behind the shot. No, look, shot, that’s a. Well, that’s an unforced air. Well, yeah, but I mean, he’s going to get angry because that’s the sort of shot he makes, the miracle, you know, fantasia kind of shots he does. So how do you gauge forced and unforced errors? Because at the end of the day, that seems to me the nerd de la guerre in Palais, because as you know, my cousin always says, nellie, the whole game is about not fucking up.

Vianney Dubois: That’s a really good question. So we have definitions for what’s a winner, what’s an forced error and what’s an unforced error. So if you think that’s the, that’s the three levels, starting with the winner, you’re forcing an error, then you’re just an unforced error. And they’re not like completely separate entities. If you. It’s more like, if you look into, in mathematics, it’s more like a one sort of bell curve and another one. There’s a bit that overlaps in two. So there’s an element of overlap. Is it a winner or is it a forced error? There’s a bit of an overlap. Even though we try to be as precise as we can, so there will always be an element of subjectivity. And I think it’s okay. We did one survey once and we surveyed about ten top level professional coaches with whom we work. So we send them a few videos and we asked, okay, with this, I can’t remember which question it was, was this a winner or forced error? Or forced error? And forced error. And by now we got 75% of one way and 25% of the other. So even top level coaches, they don’t necessarily agree. So there will always be an element of subjectivity. We can’t move away from this. We try to have clear, reasonably clear definitions. So for us, an enforced error, if we start with the. Well, start with the easier a winner. If you win the point and the opponent doesn’t touch the ball before the ball bounces the second time, that’s an automatic winner if the opponent touches the ball, but in a way that for him to bring the ball back would really have required, like a miracle, like an extraordinary shot, it’s still a winner. It’s not really an error, at least.

Minter Dial: At least for that person.

Vianney Dubois: Right.

Minter Dial: Because what is extraordinary for some.

Vianney Dubois: Yeah, of course. But still, if we look at the professional level, I don’t know. Sometimes one time in 100, Papka will sort of get even. Him will get it back. So he’s still a winner. And then we move little by little into the fourth error. So we have to determine first, was it an error? Did the player actually do something wrong? If there’s nothing wrong, if the ball comes to his face and he blocks it with his racket and the ball bounces out, he didn’t do anything. He protected himself. So that’s that. So first he had to do something wrong, and then we have to determine that he should have had that shot. So, like, if you played the same thing again ten times, the majority of the time, he will get it right. If that’s the case, then if he gets it wrong, then it’s a forced error, and then unforced error is for sure. If we start from the easy one is you have no pressure, you decide to take some risk, and you have the choice of all the shots that you want. And if you make an error, that’s a clear unforced error. So, usually for us, unforced error, you have the choice of several types of shots, and you add some risk somehow. If you do and you make an error, that’s a clear unforced error. And again, there will be an overlap where sometimes one would say, that’s unforced, and some of the times some say that’s false. It’s a question of judging the amount of pressure that was put on the player. And, yeah, of a one match, for sure, even. We constantly measure our performance. The person who takes the statistics to say, okay, how did you judge that point? On average, what is the percentage of enforced errors that you give compared to me? Compared to him? We try to always modulate it over one match. There will always be small differences, but over the entire season, like, we’ve. We. I think Tapya has played 73 matches this year. These small differences of a few points in a match, they evened themselves out for sure. But that’s. That’s, uh. Yeah, that’s how we. We see the world.

Minter Dial: Yeah, I get it. And. And, well, and all the power, too, to keep with that, with the manual scoring. Of all the benchmark numbers that you have, I was wondering, just for you, Vienna, which ones do you think are the money balls? Do you feel like there are some numbers that stand out, some benchmark numbers that are like, aha, let’s start with this, or is it really a case by case, different, you know, different numbers for different players every time?

Vianney Dubois: Well, firstly, it depends on the position of the player. So we have benchmarks for men and women but within this we have benchmark for left side players. We have benchmark for right side players. And within the right side players we have benchmarks for right handed right side players. And left handed right side players love it. It’s difficult to compare Chingoto with the Quayo. They both play on the right. But then probably 100% benchmark would be wrong. So based on the position, of course then we will look at the different numbers. If you analyse a left side player, I’m looking at his. Basically what I’m trying to see is he should win many points and so at what cost does it come? Of course I expect he will make a lot of winners in absolute terms as a percentage of all the times he touches the ball. And at what cost. So how many errors is he making in front of the winners? So that’s really, that’s for the left side players. That’s really the, that’s the key indicator for the right side players is really about how many assists they give to their partner. And maybe I need to define an assist. And this is like in football, you know, you.

Minter Dial: I love. I love the fact that you have assists. I was cherishing that. I play when I’m playing with good players. Of course I always play on the right as well. So I’m just, you know, there and I’m just there to make it a little bit difficult. Get everything back. Don’t fuck it up. And if you can make it a little bit difficult, give the assist because they pop up and your partner kills.

Vianney Dubois: Exactly. So. And to be fair to, to give credit to where he came from. And the idea of introducing the assist was from Pablo Crossetti. I don’t know if, you know, he’s like one of the main coaches. He coached Tapia for many years. He then coached Augsburger lieback. So he’s one of the main professional coaches and he’s engaged with us from the very beginning and he told us you need something like this. Otherwise you’re really missing a huge aspect of the match, of the game, especially for right side player. Yeah, a right side player for me again is how many errors is he. I mean it should not cost much in a match. A good right side player should hardly cost anything if you want. And then sometimes you would want him to make winners but at least to bring winning opportunities to his partner. That would be, if you want in a high level sort of vision, how would look at the world based on the type of player that it is. And then the more you know the player, you know his strengths and his weaknesses, that then I will zoom in to the sort of things that I think sometimes are the areas that he might be more frail about something which I think we’ve published about it. So it’s not a secret, something that was quite extraordinary for me. So cue you is obviously a very attacking player and very good that with this match. In the matches that he played this year against Galland, I think his efficiency with his smash was as low as 35%. So he would smash ten times, he would win the .3 times with the winner, with three winners. Outside of the matches, he played against all the other when he played against all the other players. But Galan, I think his smash efficiency was something like 65%. So there was a huge difference in quail smash efficiency between the matches he played against Galland and the matches he played against anyone else. So, of course, then when you know these kind of things, you watch, you look at the match, you know in much more detail. So at a high level, this is how we watch the world. And the more we know the players, then sometimes we know which are the things that you need to follow in more detail that can make it, you know, if the player is playing well that day or if he’s not. In the same vein last year, and it was true for the most part this year as well. Something again about Tapia. In last year, in all the matches that they won, Tapia was touching on average, 54% of all the goals. In all the matches that they lost, Tapia was touching on average 46% of all the goals. So clearly, last year, at the high level, the detail, we don’t know if you wanted to play to win against them, you had to play against more towards quail, which takes some courage, clearly then, to play against Tapia. And the first few finals this year that they played against Shingalan, that’s exactly what they did. In fact, they were playing like 60 or 65% to Cuello. I saw that, and that worked at the beginning. Now you seem to. The trend has changed slightly. They’re still playing more towards cue, but probably Tapia and cue and their coach have worked out the way to respond to it. So this is interesting also to follow from a statistics point of view. And the more you are into it, then you will look into different types of indicators during the match, because this is where excitement is.

Minter Dial: My first reaction is to think, well, that’s counterintuitive, that it would be a Gallan issue, because Galan playing on the left, you would imagine, isn’t really on the receiving end as many smashes. It’d be more chingoto. And the second comment I’m thinking is, in my just non data specific analysis, it seemed that Galan was on the downtrend, on the other side, so that whereas it used to be, let’s focus on the little guy Chingo. But, you know, every freaking ball comes back in some games, he doesn’t even make any errors. And it seemed that Galand’s confidence has been going down recently, at least. Maybe the last final was good, but it was the first time he got back in sensations, it seemed up until then, he looked like he was a vulnerable piece of the pie.

Vianney Dubois: Well, it’s funny you said, I’ve actually looked in some detail about what happened in the last four finals, which korean tap yet won against the previous four or five. So there was eight before they lost four, I think. I can’t remember the exact numbers, but that’s the idea, to try and see what changed. And in fact, there’s. Chingoto and Quenyu have played more or less at a high level, on a fairly reasonably consistent level. The two that really changed is Galland, who’s clearly had a bit of a not so great and making more errors than before. But Tapja is played way better. I mean, in terms of statistics, he improved into almost everything. But the one indicator that really changed enormously is how they’ve played both him and Arturo, but how him personally has played when they had break points against them. So they’ve played, I think. I don’t have the exact numbers. I think they somehow sort of considered something like 40 or 45% of all the break points against them in the previous, in the first seven finals and only like, 20% in the last four. And Tapya personally, he’s made so many winners and very few enforced errors in those points in the last four finals than they did before. So the key in the last few matches were how both, especially Tapia, played the important points in those matches.

Minter Dial: It’s phenomenal. Your system, padel intelligence, allows you. You talk about contributions, you talk about, you know, the people who are the key point players. I mean, best champions know how to show up at the key points. Great stuff, Vienna. It really is something. I could probably wax on and go into the nerdy details with you for a long time, and I look forward to sharing a beer and talking more. Great stuff. I’m really, really thrilled to have gotten to know you. I love having some data points, certainly as a journalist. And hopefully for padel in general, it will help. Vinny, tell us, tell everybody who is interested to go and cheque out what you’re up to, your data sets, panel intelligence and everything, and maybe give us a scoop on what’s coming up in the future.

Vianney Dubois: So we will launch our own website. So now we have a platform, but it wasn’t really designed to be public, so some people have managed to somehow get access to it, and it’s fine with us, but it’s not something we’re pushing a lot. The only thing that we externally focus is with our Instagram account, where we post a little bit. But we are launching our own website within a month or so.

Minter Dial: That sounds good.

Vianney Dubois: I don’t know if you play golf, but my benchmark is golf. So if you go into the big tours, like PGA Tour or DP World Tour, in golf, you go, you have a stats part. You have the list of all the players. You click on the player and you can see the detail of the stats. So we will have this. So we’ll have all the players, you can click on them. You have the detail of their stats. You can do head to head analysis, compare the stats of one player with the other at the player level, at the pair level. Then you have, like, a summary of all the matches, statistics for each of the players. So that’s something that we launching in a month or so. That’s a big scoop for us.

Minter Dial: I love it. And so the Instagram account is called pattern intelligence.

Vianney Dubois: Just all in that?

Minter Dial: Yeah. And padelintelligence live for the current site and more on that. I have this imagination going wild about fantasy padel, where you can start picking your teams and let’s go fast. Well, anyway, looking forward to staying in touch, following up whatever you’re doing. Thank you so much for coming on the show. It’s been a great pleasure to have you on Vienna. I love what you’re up to.

Vianney Dubois: I always like to talk about what we do. Thank you so much.

Minter Dial: Vamos.