Start of the third set of the final of the Australian Open. Daniel Medvedev He has won two sets and is ahead 0-1 in the third. The television image shows the probability of victory on the track: 96% of the Russian, 4% for the Spanish Rafael Nadal. The rest is the story of an epic comeback and the milestone of the ‘Grand Slam’ number 21 for the Spanish. But did Nadal beat the mathematical algorithm? Several experts in data analytics applied to sport analyze to EFE the keys.
That 4% that he win predictor of the Australian tournament at that time led to a comeback for Manacorí – at the beginning of the match it gave him a 36% chance of winning – has been the subject of all kinds of jocular comments on social networks, all of them made a posteriori, when the tenacity of player with the largest tournaments in the history of men’s tennis turned an almost lost match into a epic triumph.
However, judging by the experts consulted by EFE, the percentage was justified. In 338 matches disputed by the Spanish tennis player in tournaments Grand Slamthe main four of the circuit, of 19 situations in which Nadal he had started losing 0-2, he had only come back two; and in 13 of them in which he faced a player from the top ten of the ATP circuit he had not prevailed in any of them. Until Sunday.
An algorithm is not beaten or beaten. What an algorithm does is based on information, such as Rafa Nadal’s results history, to see how he has fared in that situation. Nadal had never won in that situation. Does that mean that 4% is not going to win a game? No, but that game, in that situation, played 100 times, he would have won it in 4″, explains to EFE Jesus Lagos, partner of ScoutAnalysta consultancy that provides data services to Spanish and European football clubs.
In the open era, from 1974only six players had come back from two sets down in a major tournament final: Bjorn Borg (Roland Garros 1974), Ivan Lendl (Roland Garros 1984), Andre Agassi (Roland Garros 1999), Gaston Gaudio (Roland Garros 2004), Dominic Thiem (US Open 2020) and Novak Djokovic (Roland Garros 2021).
“To be frank, the 4% was very generous”adds Salva Carmona, CEO of the analytics company specializing in football Driblab, which works with clubs, player agents and federations. “From now on, what we all have to think about is whether we are going to have to include other variables in the prediction model, such as fatigue, how long they have run, or if we only take into account the result. There are things that the model does not take into account. And then there is the Nadal factor, who is not just any tennis player, he is a player with 21 Grand Slams“, Add.
For the data analyst of the athlete representation agency YouFirst, Sarah Carmona, this case is a sign that the data in sport is “a complement” and should not be treated as if it were an absolute truth. “That 4% gives circumstantial information, a probability that does not have to be fulfilled. Although the normal thing would have been for Nadal not to win, mainly because of the dynamics of the match, but with Nadal we talked about being out of series. From a competitive animal with a mind as worked as his game, ”she points out.
HOW TO SQUEEZE 4%
The key, says Jesús Lagos, is to understand how Nadal got to squeeze that 4%. “The grace would be to find out under what patterns that 4% occurs. If it is because you get fewer services and the rival fails more, for example. But that in real time is complicated, and there artificial intelligence adds more value, ”he explains.
The Australian Open analyzes your data through a company called Game Insight Group, formed by the Australian tennis federation and the University of Victoria in Melbourne. In addition, in this area it has the sponsorship of the technology consultancy Infosys, also a sponsor of the circuit ATPto which it offers its technological platform for data visualization.
This company recently revealed some data that helps to understand how Nadal squeezed that 4% chance. After having an average of 55% success rate on your first serve in the first two sets, in the third the Spanish champion raised his effectiveness with the serve to 82%. From 11% accuracy with his forehand in the first set to 35% in the fourth.
An example of working with that data to maximize performance is the team of the Olympic and world badminton champion Caroline Marinled by your coach Ferdinand Rivas. “They analyze what he calls the sequences, If a player hits the shuttlecock right, right, left and up, what is the probability of that happening?which allows you to get ahead of yourself and turn it almost into a game of chess”, explains Lagos.
Another element that shines in the case of Rafael Nadal is the mental toughness. A key that, according to experts, is currently not possible to translate into data that is incorporated into a probabilistic model. “You can’t get into the model if there isn’t a provider of psychological data, and as far as I know, at least in football there isn’t. In soccer we usually have in mind the idea of playing at home or away, but in tennis they always play away. The weather, or the quality of the playing field, is not taken into account either, ”says Salvador Carmona.
A BLOW TO A FLOURISHING SECTOR
The big data analytics industry (big data) applied to sport is a flourishing business. According to the American consultancy Markets and Marketsthese services will grow 22% annually until they add a market size greater than the USD 5.2 billion in 2024.
The achievement of Rafael Nadal Given the probability associated with it by the algorithm, can it affect the credibility of the sector in any way? “I think it will remain a joke, but it hurts us as a sector. What has happened with Nadal happens with this type of predictions in football. There are companies that sell to clubs that a player is going to score 25 goals, and they don’t get it right. They generate a lot of noise and a lot of dissatisfaction,” he says. Jesus Lagosfrom ScoutAnalyst.
For Salvador Carmona surely this case is used “as a throwing weapon” against the industry, but it also generates interest that can help the public get a better idea of what data analytics is. “There is a lot of information overload, so there will be people who will be curious and will read about it”says the founder of Driblab.
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