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      How Does the Past of a Soccer Match Influence Its Future? Concepts and Statistical Analysis

      research-article
      1 , 2 , * , 1 , 2
      PLoS ONE
      Public Library of Science

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          Abstract

          Scoring goals in a soccer match can be interpreted as a stochastic process. In the most simple description of a soccer match one assumes that scoring goals follows from independent rate processes of both teams. This would imply simple Poissonian and Markovian behavior. Deviations from this behavior would imply that the previous course of the match has an impact on the present match behavior. Here a general framework for the identification of deviations from this behavior is presented. For this endeavor it is essential to formulate an a priori estimate of the expected number of goals per team in a specific match. This can be done based on our previous work on the estimation of team strengths. Furthermore, the well-known general increase of the number of the goals in the course of a soccer match has to be removed by appropriate normalization. In general, three different types of deviations from a simple rate process can exist. First, the goal rate may depend on the exact time of the previous goals. Second, it may be influenced by the time passed since the previous goal and, third, it may reflect the present score. We show that the Poissonian scenario is fulfilled quite well for the German Bundesliga. However, a detailed analysis reveals significant deviations for the second and third aspect. Dramatic effects are observed if the away team leads by one or two goals in the final part of the match. This analysis allows one to identify generic features about soccer matches and to learn about the hidden complexities behind scoring goals. Among others the reason for the fact that the number of draws is larger than statistically expected can be identified.

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          Most cited references4

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          Who Is the Best Player Ever? A Complex Network Analysis of the History of Professional Tennis

          We considered all matches played by professional tennis players between 1968 and2010, and, on the basis of this data set, constructed a directed and weighted network of contacts. The resulting graph showed complex features, typical of many real networked systems studied in literature. We developed a diffusion algorithm and applied it to the tennis contact network in order to rank professional players. Jimmy Connors was identified as the best player in the history of tennis according to our ranking procedure. We performed a complete analysis by determining the best players on specific playing surfaces as well as the best ones in each of the years covered by the data set. The results of our technique were compared to those of two other well established methods. In general, we observed that our ranking method performed better: it had a higher predictive power and did not require the arbitrary introduction of external criteria for the correct assessment of the quality of players. The present work provides novel evidence of the utility of tools and methods of network theory in real applications.
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            Trends and random fluctuations in athletics.

            Improvements in the results of athletic competitions are often considered to stem from better training and equipment, but elements of chance are always present in athletics and these also contribute. Here we distinguish between these two effects by estimating the range into which athletic records would have fallen in the absence of systematic progress and then comparing this with actual performance results. We find that only 4 out of 22 disciplines have shown a systematic improvement, and that annual best results worldwide show saturation in some disciplines.
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              Usefulness of Dismissing and Changing the Coach in Professional Soccer

              Whether a coach dismissal during the mid-season has an impact on the subsequent team performance has long been a subject of controversial scientific discussion. Here we find a clear-cut answer to this question by using a recently developed statistical framework for the team fitness and by analyzing the first two moments of the effect of a coach dismissal. We can show with an unprecedented small statistical error for the German soccer league that dismissing the coach within the season has basically no effect on the subsequent performance of a team. Changing the coach between two seasons has no effect either. Furthermore, an upper bound for the actual influence of the coach on the team fitness can be estimated. Beyond the immediate relevance of this result, this study may lead the way to analogous studies for exploring the effect of managerial changes, e.g., in economic terms.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                30 November 2012
                : 7
                : 11
                : e47678
                Affiliations
                [1 ]Institute of Physical Chemistry, WWU Muenster, Muenster, Germany
                [2 ]Center of Nonlinear Science (CeNoS), WWU Muenster, Muenster, Germany
                Universidad Carlos III de Madrid, Spain
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Analyzed the data: AH OR. Contributed reagents/materials/analysis tools: AH OR. Wrote the paper: AH OR.

                Article
                PONE-D-12-22433
                10.1371/journal.pone.0047678
                3511508
                23226200
                adf0120d-5cb5-498e-81f2-85fb79c7deb1
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 July 2012
                : 14 September 2012
                Page count
                Pages: 7
                Funding
                No current external funding sources for this study.
                Categories
                Research Article
                Mathematics
                Probability Theory
                Events (Probability Theory)
                Markov Model
                Stochastic Processes
                Statistics
                Confidence Intervals
                Statistical Methods
                Statistical Theories
                Physics
                Interdisciplinary Physics
                Social and Behavioral Sciences
                Psychology
                Behavior

                Uncategorized
                Uncategorized

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