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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

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the yea...

When COVID-19 hit, organizations using traditional analytics techniques that rely heavily on large amounts of historical data realized one important thing: Many of these models are no longer relevant. Essentially, the pandemic changed everything, rendering a lot of data useless. In turn, forward-looking data and analytics teams are pivoting from traditional AI techniques relying on “big” data to a class of analytics that requires less, or “small” and more varied. Leggi tutto:  https://www.gartner.com/smarterwithgartner/gartner-top-10-data-and-analytics-trends-for-2021

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

The choice of the most appropriate unsupervised machine-learning method for “heterogeneous” or “mixed” data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of “ready-to-use” tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and deg...

"Organizations that allocate the right resources, influence, and authority to the CDO are four times more likely to be successful using data to transform the business, according to Laney’s research. By comparison, organizations turning to the CIO to oversee data management and strategy are only half as likely to see business impact from advanced analytics initiatives." Here's why:  https://mitsloan.mit.edu/ideas-made-to-matter/making-business-case-a-chief-data-officer

Il presente testo ha come obiettivo principale quello di rispondere a una domanda che spesso studenti e ricercatori si trovano ad affrontare: cosa faccio con tutti questi dati? Le tecniche di data mining offrono una risposta a questo quesito soprattutto perché la maggioranza dei software statistici disponibili al giorno d’oggi incorporano delle routine che rendono l’applicazione di questi modelli molto semplice.  La breve guida proposta si rivolge a studenti che non hanno confidenza con l’analisi dei dati e che necessitano di farsi un’idea di cosa possono dire i database e soprattutto di quali sono gli strumenti più idonei per rispondere alle domande di ricerca. Il testo si compone di diversi paragrafi che attraverso definizioni ed esempi mostreranno al lettore come estrarre informazioni dai database in modo semplice e scoprire pertanto alcuni meccanismi che vengono utilizzati nel mondo della ricerca, ma non solo, per comprendere al meglio il comportamento degli attori delle r...