yoThe start of the third set of the Australian Open final. Daniil Medvedev wins two sets and leads 0-1 in the third set. The TV picture shows the probability of winning in the ring: 96% for the Russians, 4% for the Spaniard Rafael Neda.to. The rest is the story of an epic comeback and landmark number 21 in the “Grand Slam” of the Spaniards. ButDid Nadal beat the mathematical algorithm? Many experts in data analytics applied to sports analyze EFE keys.
The 4% that the Australian Championship “win predictor” at the time gave Manakuri back at the start of the match gave him a 36% chance of winning. It was the subject of all sorts of funny comments on social networks, and all of them came out after the event, when the perseverance of the player with the largest tournaments in the history of men’s tennis turned from an almost lost match into an epic victory.
However, based on experts consulted by EFE, The ratio was justified. In 338 matches played by the Spanish tennis player in the Grand Slam tournaments, the four major tournaments on the ring, Of the 19 instances in which Nadal started losing 0-2, he only came back twice; In 13 of them he faced a top ten player on the ATP circuit, none of which he had won. Until Sunday.
“The algorithm doesn’t beat or win. What the algorithm does depends on information, like Rafa Nadal’s scorecard, to see how he performed in that situation. Nadal has never won in that situation. Does this mean that 4% will not win a game? No, but that match, in this case, played 100 times, he would have won it in 4,” explains EFE Jesús Lagos, partner of ScoutAnalyst, a consulting firm that provides data services to Spanish and European football clubs.
Does this mean that 4% will not win a game? No, but that match, in this case, played 100 times, he would have won it in 4
In the open era, since 1974, Only six tennis players came from two groups in a grand slam finalBjörn Borg (Roland Garros 1974), Ivan Lendl (Roland Garros 1984), Andre Agassi (Roland Garros 1999), Gaston Gaudio (Roland Garros 2004), Dominic Thiem (US Open 2020), Novak Djokovic (Roland Garros 2021). “To be honest, 4% were very generous,” adds Salva Carmona, CEO of the football analytics firm Driblab.Which works with clubs, players’ agents and federations.
“From now on, what we all have to think about is whether we will have to include other variables in the prediction model, such as fatigue, how long they run, or if we only take the outcome into account. There are things that the model does not take into account. Then there’s the Nadal factor, and he’s not just a tennis player, he’s a player with 21 Grand SlamsHe adds: For data analyst at sports acting agency YouFirst Sarah Carmona, this case is a sign that data in sport is “complementary” and should not be treated as if it were an absolute truth.
There are things that the model does not take into account. Then there’s the Nadal factor, and he’s not just a tennis player, he’s a player with 21 Grand Slams
“4% gives situational information, which is a possibility that should not be realized. Although the natural thing would have been that Nadal would not have won, mainly due to the dynamics of the match, but with Nadal we are talking about not being involved in the series. From a rival animal with a mind that acts as its toy.”
How to squeeze 4%
The key, as Jesus Lagos says, is Understand how Nadal managed to squeeze the 4%. “The blessing is knowing the patterns under which the 4% occur, if it’s because you’re doing fewer services and the competitor failing more, for example. But that in real time is complicated, and there AI adds more value,” he explains. The Australian Open analyzes its data through a company called Game Insight Group, Formed by the Australian Tennis Federation and Victoria University of Melbourne. In addition, in this field, it is sponsored by the technology consulting company Infosys, which is also the sponsor of the ATP Circle, to which it offers its technology platform for data visualization.
This company recently revealed some data that helps understand how Nadal squeezed the 4% chance. Having had an average accuracy of 55% on his first serve in the first two sets, the Spanish champion raised his serving effectiveness to 82% in the third set. From 11% accuracy with a forehand in the first group to 35% in the fourth group.
You cannot enter the form if there is no psychic data provider. In football we usually have in mind the idea of playing at home or outside, but in tennis they always play far away. The weather or the quality of the playing field are not taken into consideration either.
a An example of working with this data to maximize performance is the team of Olympic and World Badminton Champion Carolina MarinLed by coach Fernando Rivas. “They analyze what he calls sequences, If a player hits the shuttlecock left, right, left and up, what is the probability of that happening, allowing you to go ahead and make it roughly chess?Lagos explains.
Another element that shines in the case of Rafa Nadal is mental strength. A key that, according to experts, cannot currently be translated into data Built in a probabilistic model. “It cannot be included in the model if there is no provider of psychological data, and as far as I know, at least in football there is none. In football we usually have in mind the idea of playing at home or outside, but in tennis they always play far away. The weather or the quality of the playing field are not taken into consideration either.Salvador Carmona says.
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