Prediction of football match results with Machine Learning

Football is one of the most popular sports in the world, so the perception of the game and the prediction of results is of general interest to fans, coaches, media and gamblers. Although predicting football results is a very complex task, the football betting business has grown over time. The unpredictability of football results and the growing betting business justify the development of prediction models to support gamblers. In this article, we develop machine learning methods that take multiple statistics of previous matches and attributes of players from both teams as inputs to predict the outcome of football matches. Several prediction models were tested, with the experimental results showing encouraging performance in terms of the profit margin of football bets.info:eu-repo/semantics/publishedVersio

Beyond Crowd Judgments: Data-driven Estimation of Market Value in Association Football

Association football is a popular sport, but it is also a big business. From a managerial perspective, the most important decisions that team managers make concern player transfers, so issues related to player valuation, especially the determination of transfer fees and market values, are of major concern. Market values can be understood as estimates of transfer fees—that is, prices that could be paid for a player on the football market—so they play an important role in transfer negotiations. These values have traditionally been estimated by football experts, but crowdsourcing has emerged as an increasingly popular approach to estimating market value. While researchers have found high correlations between crowdsourced market values and actual transfer fees, the process behind crowd judgments is not transparent, crowd estimates are not replicable, and they are updated infrequently because they require the participation of many users. Data analytics may thus provide a sound alternative or a complementary approach to crowd-based estimations of market value. Based on a unique data set that is comprised of 4217 players from the top five European leagues and a period of six playing seasons, we estimate players’ market values using multilevel regression analysis. The regression results suggest that data-driven estimates of market value can overcome several of the crowd’s practical limitations while producing comparably accurate numbers. Our results have important implications for football managers and scouts, as data analytics facilitates precise, objective, and reliable estimates of market value that can be updated at any time

Big datan hyödyntäminen jalkapalloseuran johtamisessa

Tiivistelmä. Jalkapallo on pitkään tullut jäljessä datalähtöisessä päätöksenteossa moniin muihin urheilulajeihin verrattuna. Viime vuosina dataa on kuitenkin alettu keräämään niin paljon, että sen käsittelemiseen tarvitaan big data -ratkaisuja. Tämän kandidaatintyön tavoitteena on selvittää, miten big dataa voidaan hyödyntää jalkapalloseuran johtamisessa. Tutkimus suoritettiin kuvailevan kirjallisuuskatsauksen muodossa, jossa käsiteltiin big datan käsitettä, jalkapalloseuran johtamista sekä lopulta sitä, millä tavoin big dataa voidaan hyödyntää jalkapalloseuran johtamisen eri toiminnoissa. Big dataa käsitellessä päädyttiin kaksihaaraiseen määritelmään käsitteelle. Big data voidaan määritellä ilmiönä kulttuurissa, teknologiassa ja tieteessä tai vaihtoehtoisesti datan piirteiden perusteella, joita ovat volyymi, vaihtelevuus ja nopeus. Jalkapallon johtamista käsiteltiin kirjallisuudessa esiintyvien nimikkeiden epäjohdonmukaisuuksien vuoksi kaikilla johtamisen tasoilla riippumatta johtajien työnimikkeistä. Jalkapalloseuran johtamisen havaittiin olevan suurelta osin ihmisjohtamista. Käytetyssä kirjallisuudessa esiintyneet johtamisen vastuut kerättiin yhtenäiseksi listaksi ja luokiteltiin strategisiin, taktisiin ja operatiivisiin vastuisiin. Big datalle löydettiin useita eri käyttömahdollisuuksia jalkapalloseuran johtamisessa. Puettavan teknologian kehittymisen myötä on ilmaantunut useita menetelmiä ja työkaluja joukkueen taktisen johtamisen tukemiseen. Näitä ovat joukkueen keskiön ApEn-analyysi, joukkueen käsittämän pinta-alan seuranta, verkkoteoriaan pohjautuva syöttökäyttäytymisanalyysi, erilaiset koneoppimisalgoritmit ja kokonaisvaltaisemmat työkalujen yhdistelmät. Johtamisen strategiselle osa-alueelle kehitetyt ratkaisut liittyivät rekrytointipäätösten tukemiseen mahdollistamalla datalähtöisen markkina-arvojen arvioimisen. Tämän tutkimuksen tuloksia voidaan pääasiallisesti hyödyntää big datan ja jalkapallon tutkimuksen edistämiseen. Tuloksia voidaan mahdollisesti myös yleistää muiden joukkueurheilulajien tutkimukseen.Using big data in the management of a football club. Abstract. Football has long lagged behind in data-driven decision-making compared with many other sports. In recent years, however, the amount of data to be processed has increased so much that big data solutions are needed. The aim of this Bachelor’s thesis is to find out how big data can be utilized in the management of a football club. The study was conducted in the form of a descriptive literature review. Addressed topics are the concept of big data, football club management, and finally how big data can be utilized in the various functions of football club management. A two-pronged definition was produced for the concept of big data. Big data can be defined as a phenomenon in culture, technology, and science, or alternatively, based on the characteristics of the data. The determining characteristics of big data are volume, variety, and velocity. Due to inconsistencies in managers’ job titles found in football clubs, management was addressed at all levels of club management regardless of the managers’ titles. The management in a football club was found to be largely people management. The management responsibilities that appeared in the literature were collected into a single list and classified into strategic, tactical, and operational responsibilities. Several different uses for big data were found in the management of a football club. With the development of wearable sensor technology, several methods and tools have emerged to support tactical management in football. These include the ApEn analysis of the team centroid, monitoring of the area covered by a team, passing behaviour analysis based on graph theory, various machine learning algorithms and more comprehensive tool combinations. The solutions developed for the strategic parts of football club management related to the supporting of recruitment decisions by enabling data-based assessment of market values. The results of this study can be used primarily to advance big data and football research. The results can possibly also be generalized to advance research on other team sports

Analysing data mining methods in sports analytics: a case study in NHL player salary prediction

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe deployment of Internet of Things has become a systematic phenomenon around the world, leading to the exponential growth of data and data analysis practices. This particular growth is being seen within the sporting industry as new hardware and software are continuously being developed for home and professional use. Though there are several use cases of effective data usage within elite sports, there remains the notion that professional sporting organizations should expand their resources to fully cease the possibility of competitive advantage, through effective data mining techniques. This project conducts a comprehensive analysis of extensive open-sourced NHL data, utilizing SAS’s established SEMMA process. Through the SEMMA process, this project yields a predictive data-mining model, designed to predict future player salaries. With player salaries within the NHL steadily increasing, reaching upwards of $10millon per year, a predictive model with an overall average error of$150,000 and Mean absolute error of $870,000 can grant team’s unique knowledge, which if used effectively within the NHL, can lead to superior decision making. Though there remain limitations due to unquantifiable variables linked to a player’s psychology, as a whole, concrete deductions show that if effectively analyzed, sporting organizations have the power to leverage data to develop a competitive advantage. Our research indicates concludes that organizations pushing towards developing an established data science department are increasing their odds of winning

Data-driven evaluation of on-field player performance in football using sensor and video technologies

Data has become increasingly relevant and used in football over the years. Technological development has made it possible to gather data from various aspects of the game. However, despite the growing popularity of sports analytics, relatively little research, especially qualitative, has been done on the topic. The purpose of this thesis is to create understanding and practices for taking advance of data for evaluation of on-field player performance in football using sensor and video technologies. This is done by identifying and combining technological possibilities with sports knowledge and suggesting an approach for data-driven evaluation of the on-field player performance. Review of previous literature and semi-structured theme interviews have been used as a method to achieve the purpose of the thesis. The findings of the thesis show that data can be used in the evaluation of on-field player performance in football by assessing players’ physical, technical, tactical, and mental attributes. These attributes have several different metrics, the value of which depends on several factors such as the team's objectives. Furthermore, an approach is presented in the thesis which suggests that the selection of team-specific attributes and metrics guides the user to consider which data is needed to be able to evaluate the desired metrics, which then can be linked to certain technologies and analytical solutions presented in the thesis

Data-Driven Analytics for Decision Making in Game Sports

Performance analysis and good decision making in sports is important to maximize chances of winning. Over the last years the amount and quality of data which is available for the analysis has increased enormously due to technical developments like, e.g., of sensor technologies or computer vision technology. However, the data-driven analysis of athletes and team performances is very demanding. One reason is the so called semantic gap of sports analytics. This means that the concepts of coaches are seldomly represented in the data for the analysis. Furthermore, sports in general and game sports in particular present a huge challenge due to its dynamic characteristics and the multi-factorial influences on an athlete’s performance like, e.g., the numerous interaction processes during a match. This requires different types of analyses like, e.g., qualitative analyses and thus anecdotal descriptions of performances up to quantitative analyses with which performances can be described through statistics and indicators. Additionally, coaches and analysts have to work under an enormous time pressure and decisions have to be made very quickly. In order to facilitate the demanding task of game sports analysts and coaches we present a generic approach how to conceptualize and design a Data Analytics System (DAS) for an efficient support of the decision making processes in practice. We first introduce a theoretical model and present a way how to bridge the semantic gap of sports analytics. This ensures that DASs will provide relevant information for the decision makers. Moreover, we show that DASs need to combine qualitative and quantitative analyses as well as visualizations. Additionally, we introduce different query types which are required for a holistic retrieval of sports data. We furthermore show a model for the user-centered planning and designing of the User Experience (UX) of a DAS. Having introduced the theoretical basis we present SportSense, a DAS to support decision making in game sports. Its generic architecture allows a fast adaptation to the individual characteristics and requirements of different game sports. SportSense is novel with respect to the fact that it unites raw data, event data, and video data. Furthermore, it supports different query types including an intuitive sketch-based retrieval and seamlessly combines qualitative and quantitative analyses as well as several data visualization options. Moreover, we present the two applications SportSense Football and SportSense Ice Hockey which contain sport-specific concepts and cover (high-level) tactical analyses