Predicting the risk of injury of professional football players with machine learning

Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementSports analytics is quickly changing the way sports are played. With the rise of sensor data and new tracking technologies, data is collected at an unprecedented degree which allows for a plethora of innovative analytics possibilities, with the goal of uncovering hidden trends and developing new knowledge from data sources. This project creates a prediction model which predicts a player’s muscular injury in a professional football team using GPS and self-rating training data, by following a Data Mining methodology and applying machine learning algorithms. Different sampling techniques for imbalanced data are described and used. An analysis of the quality of the results of the different sampling techniques and machine learning algorithms are presented and discussed

Large Scale Analysis of Offensive Performance in Football - Using Synchronized Positional and Event Data to Quantify Offensive Actions, Tactics, and Strategies

Offensive performances in football have always been of great focus for fans and clubs alike as evidenced by the fact that nearly all Ballon d’Or winners have been forwards or midfielders. With the increase in availability of granular data, evaluating these performances on a deeper level than just goals scored or gut instinct has become possible. The domain of sports analytics has recently emerged, exploring how applying data science techniques or other statistical methods to sports data can improve decision making within sporting organizations. This thesis follows the footsteps of other sports like baseball or basketball where, at first, offensive performances were analyzed. It consists of four studies exploring various levels of offensive performance, ranging from basic actions to team-level strategy. For that, it uses a dataset part of larger research program that also explores the automatic detection of tactical patterns. This dataset mainly consists of positional and event data from eight seasons of the German Bundesliga and German Bundesliga 2 between the seasons 2013/2014 and 2020/2021. In total this amounts to 4, 896 matches, with highly accurate player and ball positions for every moment of the match and detailed logs of every action that occurred, thus making it one of the largest football datasets to be analyzed at this level of granularity. In a first step, this thesis shows how the two different data sources can be synchronized. With this synchronized data it is possible to better quantify individual basic actions like shots or passes. For both actions new metrics (Expected Goals and Expected Passes) were developed, that use the contextual information to quantify the chance quality and passing difficulty. Using this improved quantification of individual actions, the subsequent studies evaluate offensive performance on a tactical pattern level (how goals are scored) and on a strategy level (what team formations are particular effective offensively). Besides their usage on the performance side, these metrics have also been adapted from broadcasters to enhance their data story telling: Expected goals and expected passes are shown during every Bundesliga match to a worldwide audience, thus bringing the field of sports analytics to millions of fans

Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi

Comparative Analysis of Student Learning: Technical, Methodological and Result Assessing of PISA-OECD and INVALSI-Italian Systems .

PISA is the most extensive international survey promoted by the OECD in the field of education, which measures the skills of fifteen-year-old students from more than 80 participating countries every three years. INVALSI are written tests carried out every year by all Italian students in some key moments of the school cycle, to evaluate the levels of some fundamental skills in Italian, Mathematics and English. Our comparison is made up to 2018, the last year of the PISA-OECD survey, even if INVALSI was carried out for the last edition in 2022. Our analysis focuses attention on the common part of the reference populations, which are the 15-year-old students of the 2nd class of secondary schools of II degree, where both sources give a similar picture of the students

Enhancement of bees algorithm for global optimisation

This research focuses on the improvement of the Bees Algorithm, a swarm-based nature-inspired optimisation algorithm that mimics the foraging behaviour of honeybees. The algorithm consists of exploitation and exploration, the two key elements of optimisation techniques that help to find the global optimum in optimisation problems. This thesis presents three new approaches to the Bees Algorithm in a pursuit to improve its convergence speed and accuracy. The first proposed algorithm focuses on intensifying the local search area by incorporating Hooke and Jeeves’ method in its exploitation mechanism. This direct search method contains a pattern move that works well in the new variant named “Bees Algorithm with Hooke and Jeeves” (BA-HJ). The second proposed algorithm replaces the randomly generated recruited bees deployment method with chaotic sequences using a well-known logistic map. This new variant called “Bees Algorithm with Chaos” (ChaosBA) was intended to use the characteristic of chaotic sequences to escape from local optima and at the same time maintain the diversity of the population. The third improvement uses the information of the current best solutions to create new candidate solutions probabilistically using the Estimation Distribution Algorithm (EDA) approach. This new version is called Bees Algorithm with Estimation Distribution (BAED). Simulation results show that these proposed algorithms perform better than the standard BA, SPSO2011 and qABC in terms of convergence for the majority of the tested benchmark functions. The BA-HJ outperformed the standard BA in thirteen out of fifteen benchmark functions and is more effective in eleven out of fifteen benchmark functions when compared to SPSO2011 and qABC. In the case of the ChaosBA, the algorithm outperformed the standard BA in twelve out of fifteen benchmark functions and significantly better in eleven out of fifteen test functions compared to qABC and SPSO2011. BAED discovered the optimal solution with the least number of evaluations in fourteen out of fifteen cases compared to the standard BA, and eleven out of fifteen functions compared to SPSO2011 and qABC. Furthermore, the results on a set of constrained mechanical design problems also show that the performance of the proposed algorithms is comparable to those of the standard BA and other swarm-based algorithms from the literature

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports