Efficiency of French football clubs and its dynamics

In the paper we evaluate the efficiency of French football clubs (Ligue 1) from 2004 to 2007 using Data Envelopment Analysis (DEA) with « Assurance Region ». Then, we study the dynamics of clubs’ performances. Contrary to previous works on other championships, best teams in competition or most profitable clubs are not the most efficient units in our sample. High average scores show that French First League is efficient. The first source of inefficiency in the Ligue 1 is linked to size problems and over-investments. Despite an average club performance stable over the period, we exhibit a deterioration of conditions in which clubs operate.Ligue 1, efficiency scores, Data Envelopment Analysis (DEA), Malmquist index, over-investment

Performance profiling as an intelligence-led approach to antidoping in sports

The efficient use of testing resources is crucial in the fight against doping in sports. The athlete biological passport relies on the need to identify the right athletes to test, and the right time to test them. Here we present an approach to longitudinal tracking of athlete performance to provide an additional, more intelligence-led approach to improve targeted antidoping testing. The performance results of athletes (male shot putters, male 100 m sprinters, and female 800 m runners) were obtained from a performance results database. Standardized performances, which adjust for average career performance, were calculated to determine the volatility in performance over an athlete's career. We then used a Bayesian spline model to statistically analyse changes within an athlete's standardized performance over the course of a career both for athletes who were presumed "clean" (not doped), and those previously convicted of doping offences. We used the model to investigate changes in the slope of each athlete's career performance trajectory and whether these changes can be linked to doping status. The model was able to identify differences in the standardized performance of clean and doped athletes, with the sign of the change able to provide some discrimination. Consistent patterns of standardized performance profile are seen across shot put, 100 m and 800 m for both the clean and doped athletes we investigated. This study demonstrates the potential for modeling athlete performance data to distinguish between the career trajectories of clean and doped athletes, and to enable the risk stratification of athletes on their risk of doping.Peer reviewe

Performance Assessment Strategies

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis

Performance Assessment Strategies:

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis

Performance Assessment Strategies:

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis

Técnicas de modelado matemático paramétrico y no paramétrico: un caso práctico de identificación de una máquina eléctrica

El modelado matemático es una característica muy importante en relación con el análisis y control de sistemas dinámicos. Además, la identificación del sistema es un enfoque para construir expresiones matemáticas a partir de datos experimentales tomados de procesos. En este contexto, este trabajo describe varias técnicas de modelado e identificación que son herramientas poderosas para determinar el comportamiento de los sistemas dinámicos en el tiempo. En Este trabajo se enfatiza las principales ventajas y/o desventajas que tienen las diferentes formulaciones matemáticas de modelación e identificación. También se presenta una revisión exhaustiva de las principales técnicas de modelado e identificación desde una perspectiva paramétrica y no paramétrica. Se formularon los modelos paramétricos y no paramétricos por medio de sus ecuaciones para aplicarlos en un caso de estudio. Los datos experimentales se toman de una máquina eléctrica, un motor de DC de una plataforma didáctica en la cual se aplican un conjunto de entradas conocidas para medir la velocidad del motor y utilizar estos datos como parte del proceso de modelación e identificación. El artículo concluye con las soluciones proporcionadas por la comparación de técnicas de modelación e identificación donde soluciones sencillas como los sistemas de primer orden son precisos para modelar un motor DC de dinámica lineal sobre otras formulaciones matemáticas más complejasMathematical modeling is an important feature concerning the analysis and control of dynamic systems. Also, system identification is an approach for building mathematical expressions from experimental data taken from processes performance. In this context, the contemporaneous state of the art describes several modelling and identification techniques which are excellent alternatives to determine systems behavior through time. This paper presents a comprehensive review of the main techniques for modeling and identification from a parametric and no parametric perspective. Experimental data are taken from an electrical machine that is a DC motor from a didactic platform. The paper concludes with the analysis of results taken from different identification procedures

Exploratory analysis of goalball: A regression based approach

The purpose of the study was to identify whether there was a relative quality effect for key performance indicators (KPI) in goalball. The study was completed from a performance analysis perspective and analysis was completed on two major international goalball tournaments (Paralympics 2012, European A 2013) and three IBSA qualifying events (Hungary 2013, Venice 2014, Malmo 2014). A regression-based analysis described by O’Donoghue & Cullinane (2011) was used to identify whether there was a relative quality (RQ) effect between KPI in goalball. Results showed that there was a low correlation between RQ and KPI in goalball. Although weak positive correlation was observed, a repeated measures anova showed trends for shots to pockets (F=3.280, p=0.053) and speed of shot (F=4.048, p<0.05), with a weak negative correlation for smooth shots (F=5.598, p<0.05). Thus, suggesting that teams of higher RQ score more goals, through faster more accurate shots. The regression was used to present a case study from one match between Russia and GB (RQ of +1.12, -1.12 respectively). The team with higher RQ performed well in desirable aspects of performance, exceeding the performance of 81.30% for speed of shot, 94.36% shots to pockets and 70.63% bounce shots of performances with that RQ. Despite the low correlation between RQ effect and KPI in goalball the regression-based analysis was shown to have an applied application, although caution would be expressed due to the variation experienced in the upper and lower estimates of the prediction equation

Mergers and Acquisitions Between Mutual Banks in Italy :An analysis of the effects on performance and productive efficiency

The paper is aimed at testing the hypothesis that the M&A wave over the past ten years has increased the level of efficiency of co-operative credit banks (CCBs), both in terms of overall performance and productive efficiency. The logical development is hinged on two steps: 1) an explorative analysis which is based on the observation of balance sheet ratios by quantiles, 2) a DEA application for estimating productive efficiency scores. The analysis refers to 94 CCBs which have been involved in M&As over the period 1995-1998 and is carried out on both merged and non-merged banks, either before concentration or in the subsequent years.

Transfering Targeted Maximum Likelihood Estimation for Causal Inference into Sports Science

Although causal inference has shown great value in estimating effect sizes in, for instance, physics, medical studies, and economics, it is rarely used in sports science. Targeted Maximum Likelihood Estimation (TMLE) is a modern method for performing causal inference. TMLE is forgiving in the misspecification of the causal model and improves the estimation of effect sizes using machine-learning methods. We demonstrate the advantage of TMLE in sports science by comparing the calculated effect size with a Generalized Linear Model (GLM). In this study, we introduce TMLE and provide a roadmap for making causal inference and apply the roadmap along with the methods mentioned above in a simulation study and case study investigating the influence of substitutions on the physical performance of the entire soccer team (i.e., the effect size of substitutions on the total physical performance). We construct a causal model, a misspecified causal model, a simulation dataset, and an observed tracking dataset of individual players from 302 elite soccer matches. The simulation dataset results show that TMLE outperforms GLM in estimating the effect size of the substitutions on the total physical performance. Furthermore, TMLE is most robust against model misspecification in both the simulation and the tracking dataset. However, independent of the method used in the tracking dataset, it was found that substitutes increase the physical performance of the entire soccer team