My Cookie Mix

In sports, where real-time decisions and precision are critical, the ability to analyze images through object detection and tracking can be a game-changer. For example, imagine instantly determining the winner in a cycling race or tracking the ball movement in a soccer match—all with the help of AI and visual workflows. These capabilities enable faster decision-making, reduce human error, and deliver detailed performance insights that go beyond the limits of human observation. In this blog post, we’ll explain how to: Use pre-trained vision models to analyze and process visual data efficiently. Dive into two specific use cases using KNIME workflows: Object detection in cycling: Identify the lead cyclist in a race. Object tracking in soccer: Track the ball's movement across video frames. Read more:  https://www.knime.com/blog/ai-in-sports-analytics-object-detection

Spain won its fourth men’s UEFA European Championship title by breaking English hearts with a 2–1 win over the Three Lions in the 2024 final. A late goal by substitute Mikel Oyarzabal clinched the victory for the Spanish at the Olympiastadion in Berlin on Sunday 14 July, 2024. The title victory seemed well deserved, as they won practically all of their matches. But what were Spain’s chances of winning before the tournament? Read more:  https://medium.com/low-code-for-advanced-data-science/was-spain-s-win-at-euro-2024-predictable-080bfca59b4f

Riuscirà l’intelligenza artificiale a scovare il Cristiano Ronaldo del futuro ? O, più modestamente, ad affiancare – più di quanto non accada già oggi con i diversi strumenti a disposizione, fra cui sterminale video library – talent scout e osservatori calcistici nell’individuare i talenti più affini al gioco e agli schemi di una certa società? IBM pensa di sì e da un po’ di mesi ha messo all’opera la sua piattaforma watsonx su due strumenti dedicati proprio all’indagine e al suggerimento dei giovani calciatori più promettenti e di quelli più adeguati a un certo tipo di gioco. Lo ha fatto tempo fa, e sul secondo fronte, con il Siviglia (si chiama Scout advisor) e ultimamente, in Italia, con l’ Empoli (il tool a disposizione della società toscana è stato invece battezzato Talent scouting). Leggi tutto:  https://www.wired.it/article/ai-talent-scout-empoli/

StatsBomb ha rilasciato un dataset gratuito con i dati dei 34 incontri imbattuti del Bayer Leverkusen nella Bundesliga 2023/24; sotto la guida di Xabi Alonso la società ha vinto - con 5 giornate di anticipo - il suo primo campionato. Il dataset include circa 3.400 eventi per partita e i dati tattici "360" , fondamentali per analisi più approfondite sul controllo spaziale e il contesto posizionale dei giocatori. StatsBomb - Bayer Leverkusen defensive activity heatmap. Bundesliga 2023/24 Il dataset contiene gli stessi dati sugli disponibili per i clienti StatsBomb, con una media di circa 3.400 eventi per partita per tutti e 34 gli incontri di campionato del Leverkusen. Nel rilascio sono inclusi anche i dati StatsBomb 360 , i dati tattici che contengono la posizione di tutti i giocatori nel fotogramma visibile attorno a ciascun evento. I dati 360 permettono un'analisi più approfondita e significativa rispetto a quella possibile utilizzando solo i dati sugli eventi Per acced...

Think about De Zerbi’s Brighton knocking it about deep in their own defensive third, baiting the opposition. It amounts to nothing if they can’t move the ball forwards quickly and accurately when they manufacture an opportunity to do so. But of course, passing the ball quickly, accurately and into more advanced areas of the pitch is not an easy thing to do. Players that can do so reliably and frequently aren’t easy to come by. Which got me thinking…. how can we use data to identify these types of players? Leggi tutto:  https://statsbomb.com/articles/soccer/identifying-fast-passers-using-data/

Last time we saw that we could use logistic regression to estimate team ratings based on past results and some extra indicators such as rest days and whether it was a divisional game or not. Furthermore, the additional consideration of wind speed led to a significant improvement of the model. The model improved by 5 percentage points. The full article can be found here: Who is the best team in the NFL? ( https://medium.com/low-code-for-advanced-data-science/who-is-the-best-team-in-the-nfl-b9724cd3deee ) The downside of this additional indicator was that we could not (yet) use it for predictions, as we did not yet have any forecast data available for the weather data. Moreover, the data from nfldata lacked values that we imputed using the mean value of the wind speed. For that reason, this time we will be looking at the automatic collection of wind speed data. Read more:  https://medium.com/low-code-for-advanced-data-science/winning-nfl-betting-with-knime-wind-data-and-regression-a...

The history of NFL power rankings reflects the evolution of sports analytics and data-driven decision-making. Before the advent of formal power rankings, fans and sports commentators informally ranked teams based on their observations and subjective judgments. These rankings were often based on factors like win-loss records or point differentials. With the advancement of computing and statistical analysis in the late 20th century, some experts started to create power rankings that incorporated statistical data. Today, power rankings are an integral part of the NFL fan experience, providing a more objective perspective on team performance and helping fans engage in informed debates about the league’s best teams. Leggi tutto: https://medium.com/low-code-for-advanced-data-science/who-is-the-best-team-in-the-nfl-b9724cd3deee

Prima parte: In the fast-paced world of sports betting and analytics, gaining access to accurate and up-to-date NFL (National Football League) results and odds data is crucial. Web scraping is a powerful technique that allows you to extract this valuable information from websites efficiently. In this introduction, we will explore how to leverage KNIME, an open-source, versatile, and no-code data analytics platform, to perform web scraping of NFL results and odds. Leggi tutto:  https://medium.com/low-code-for-advanced-data-science/scraping-nfl-data-with-knime-part-1-2f05ac9b0940 Seconda parte: Last time we saw how easy it is to use KNIME and XPath to scrape the results of an entire NFL season. [...] This time we will go one step further and scrape all available seasons. [...] We can see what seasons are available on the USA Today website. Now our task will be to select all the seasons one by one and execute the workflow we created in the last tutorial to save all the games with the...

Football or Soccer is one of the world’s most famous sports with the most incredible fan following. For context, approximately 1.5 billion people viewed the recent world cup final at the end of 2022. So what makes football more entertaining than other sports? The answer is simple. Football is unpredictable. At any moment in the game, a player can score and completely turn the game around, take Mbappe’s brilliant goals in the world cup final, which completely changed the game’s tide. Despite such unpredictability, you’d be surprised to know that you can indeed predict the outcome of a football game to a particular accuracy. This is usually done through machine learning, and thus, I will be exploring how various machine learning models can be harnessed to achieve my aim of predicting the outcome of football games! Leggi tutto: https://python.plainenglish.io/creating-machine-learning-models-to-predict-football-game-outcomes-70b6bf02885c

 To enhance my skills in applying data science concepts and tools, I create small projects for myself, emphasizing a hands-on learning approach. One such project was the development of a football prediction model, which evolved into an accumulation of several smaller projects. For all these projects, I utilize KNIME, a low-code, no-code data science platform. KNIME offers a combination of standard KNIME nodes by default and the ability to incorporate code (Python/R/Java) when necessary, making it my go-to solution. Additionally, KNIME Analytics Platform is available at no cost. You can download it for free here. Among these projects, the one that provided me with the most enjoyment (although I wish it brought me money) involves predicting the outcome of football matches without the need for coding. Read more:  https://medium.com/low-code-for-advanced-data-science/can-i-predict-the-outcome-of-a-football-match-and-make-money-ec9996d3092f

In the dynamic world of soccer, success hinges on a myriad of factors – talent, tactics, team spirit, and, increasingly, data. Welcome to the era of 'Soccer Analytics: How Data is Changing the Game.' The fusion of soccer and data analytics is revolutionizing the sport, providing profound insights into player performance, team strategies, and talent scouting. In this era, where every kick, pass, and run is meticulously analyzed, data is the unseen game-changer, the 12th player on the pitch, silently influencing decisions that impact the outcome of the game. The advent of data analytics in soccer marks a paradigm shift from traditional decision-making based on intuition and observation to an evidence-based approach grounded in statistical analysis. From the physical data detailing a player's speed and distance covered, to the technical data illuminating pass accuracy and shot efficiency, to the tactical data revealing player positions and heat maps, the power of data is now...

Not all networks are social! Graph theory flexed its muscles with the rise of social networks. But what can it do for sports analytics? What if we model soccer passes as a network? Can we learn which team is more likely to win? Can we identify critical players to pressure the opposing team? Can we identify opportunities to improve our team’s performance? To find out, we can use the Statsbomb API to access free data on every pass in the 2018 World Cup. Read more:  https://www.kdnuggets.com/2022/11/graph-theory-scout-soccer.html

Data Master Yodime shows Lucky how to use his interest in football as motivation to gain experience in Data Science by collecting football matches to predict the winner of the UEFA Euro 2020 with the open source data science tool, KNIME Analytics Platform. Read more:  https://medium.com/low-code-for-advanced-data-science/if-you-want-to-be-a-data-scientist-change-hobbies-61a4da749205

Data alone is not enough to discover new things. Data does not automatically lead to insights. Analyzing and interpreting data is hard work.  The challenge is to interpret the data correctly. And this is not about technology, processes and methods, but primarily about football know-how, openness and curiosity. To gain real and valuable insights, it takes numerous iterations between soccer knowhow and data knowhow.  Leggi tutto:  https://www.footballytics.ch/post/data-analytics-in-football

What are analytics? The dictionary tells us the word entered English in the late sixteenth century from Aristotle’s analytiká , an Ancient Greek root meaning “sports talk for nerds who’ve never won a tackle.” Since formal syllogisms aren’t much help in scouting left backs, in soccer we usually reserve the term for data analytics—you know, stats. Tables and figures. Messi vizzes that’ll do numbers on Twitter. Anything that sets out to ruin the beautiful game by turning it into math class, that’s analytics. The best reason to try to measure the sport is the same reason people used to say it couldn’t be measured: soccer is hard. Even coaches and analysts and scouts who’ve spent their lives learning to watch it won’t see games quite the same way. There are too many moving parts, too many possibilities to hold in your head at once. Had we but world enough and time, you might rewatch each match over and over to pause and study it and it’d still be impossible to see and remember it all. And ...

If you're new to data analytics in soccer and want to learn about data sources and datasets available to you, how to get to them and what to expect, you might find the below article of interest. It is meant to give you a quick primer and introduction to the most popular sources known to me at this point, different types of data and their basic applications. Read more:  https://www.linkedin.com/pulse/soccer-analytics-data-beginners-guide-christian-kotitschke/

Until recently tactical analysis in elite soccer were based on observational data using variables which discard most contextual information. Analyses of team tactics require however detailed data from various sources including technical skill, individual physiological performance, and team formations among others to represent the complex processes underlying team tactical behavior. Accordingly, little is known about how these different factors influence team tactical behavior in elite soccer. In parts, this has also been due to the lack of available data. Increasingly however, detailed game logs obtained through next-generation tracking technologies in addition to physiological training data collected through novel miniature sensor technologies have become available for research. This leads however to the opposite problem where the shear amount of data becomes an obstacle in itself as methodological guidelines as well as theoretical modelling of tactical decision making in team sports ...