This paper considers the use of observed and predicted match statistics as inputs to forecasts of the outcomes of football matches. It is shown that, were it possible to know the match statistics in advance, highly informative forecasts of the match outcome could be made. Whilst, in practice, match statistics are never available prior to the match, this leads to a simple philosophy. If match statistics can be predicted pre-match, and if those predictions are accurate enough, it follows that informative match forecasts can be made. It is shown that Generalised Attacking Performance (GAP) ratings, introduced in a recent paper, provide a suitable methodology for predicting match statistics in advance and that they are informative enough to provide information beyond that reflected in the odds. A long term and robust gambling profit is demonstrated when the forecasts are combined with two betting strategies.
Since March 2020, the environment surrounding football has changed dramatically because of the COVID-19 pandemic. After a few months' break, re-scheduled matches were held behind closed doors without spectators. The main objective of this study is a quantitative evaluation of ``crowd effects'' on home advantage, using the results of these closed matches. The proposed analysis uses pairwise comparison method to reduce the effects caused by the unbalanced schedule. The following conclusions were drawn from the statistical hypothesis tests conducted in this study: In four major European leagues, …
We investigate the potential occurrence of change points - commonly referred to as "momentum shifts" - in the dynamics of football matches. For that purpose, we model minute-by-minute in-game statistics of Bundesliga matches using hidden Markov models (HMMs). To allow for within-state correlation of the variables considered, we formulate multivariate state-dependent distributions using copulas. For the Bundesliga data considered, we find that the fitted HMMs comprise states which can be interpreted as a team showing different levels of control over a match. Our modelling framework enables infe…
Forecasting the future development in quality and value of professional football players
1 Delft Institute of Applied Mathematics, Delft University of Technology, 2628 CD Delft, The Netherlands；\ 2 Data Science \& Computer Vision, SciSports, 3703 HX Zeist, The Netherlands
The COVID-19 pandemic has left its marks in the sports world, forcing the full-stop of all sports-related activities in the first half of 2020. Football leagues were suddenly stopped and each country was hesitating between a relaunch of the competition and a premature ending. Some opted for the latter option, and took as the final standing of the season the ranking from the moment the competition got interrupted. This decision has been perceived as unfair, especially by those teams who had remaining matches against easier opponents. In this paper, we introduce a tool to calculate in a fairer w…
The FIFA Men's World Cup Tournament (WCT) is the most important football (soccer) competition, attracting worldwide attention. A popular practice among football fans in Brazil is to organize contests in which each participant informs guesses on the final score of each match. The participants are then ranked according to some scoring rule. Inspired by these contests, we created a website to hold an online contest, in which participants were asked for their probabilities on the outcomes of upcoming matches of the WCT. After each round of the tournament, the ranking of all users based on a proper…
Beijing Sport University（北京体育大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Universidad Politécnica de Madrid（马德里理工大学）
Characterizing playing style is important for football clubs on scouting, monitoring and match preparation. Previous studies considered a player's style as a combination of technical performances, failing to consider the spatial information. Therefore, this study aimed to characterize the playing styles of each playing position in the Chinese Football Super League (CSL) matches, integrating a recently adopted Player Vectors framework. Data of 960 matches from 2016-2019 CSL were used. Match ratings, and ten types of match events with the corresponding coordinates for all the lineup players whos…
Football forecasting models traditionally rate teams on past match results, that is based on the number of goals scored. Goals, however, involve a high element of chance and thus past results often do not reflect the performances of the teams. In recent years, it has become increasingly clear that accounting for other match events such as shots at goal can provide a better indication of the relative strengths of two teams than the number of goals scored. Forecast models based on this information have been shown to be successful in outperforming those based purely on match results. A notable we…
The approaches routinely used to model the outcomes of football matches are characterised by strong assumptions about the dependence between the number of goals scored by the two competing teams and their marginal distribution. In this work, we argue that the assumptions traditionally made are not always based on solid arguments. Although most of these assumptions have been relaxed in the recent literature, the model introduced by Dixon and Coles in 1997 still represents a point of reference in the betting industry. While maintaining its conceptual simplicity, we propose a modification of the …
This report is devoted to the forecast of the UEFA EURO 2020, Europe's continental football championship, taking place across Europe in June/July 2021. We present the simulation results for this tournament, where the simulations are based on a zero-inflated generalized Poisson regression model that includes the Elo points of the participating teams and the location of the matches as covariates and incorporates differences of team-specific skills. The proposed model allows predictions in terms of probabilities in order to quantify the chances for each team to reach a certain stage of the tourna…
Automated Detection of Match Phases in Football from Spatio-Temporal Tracking Data Using Graph Neural Networks
Institute of Statistics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院统计研究所）；Karlsruher Sport-Club（卡尔斯鲁厄体育俱乐部）；Broad Institute of MIT & Harvard（麻省理工学院与哈佛大学生物医学 Broad 研究所）；German Football Association (DFB)（德国足球协会）；Chair for Sports Analytics, Saarland University（萨尔兰大学体育分析讲席）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Match forecasts in UEFA club competitions: Elo ratings versus Transfermarkt valuations
Institute of Economics, ELTE（匈牙利罗兰大学经济研究所）；Institute for Computer Science and Control (SZTAKI), Hungarian Research Network (HUN-REN)（匈牙利研究网络计算机科学与控制研究所）；Hungarian University of Sports Science（匈牙利体育科学大学）；Corvinus University of Budapest (BCE)（布达佩斯考文纽斯大学）
本文比较了 Elo 评分与 Transfermarkt 估值在预测欧足联俱乐部赛事比赛结果上的表现，发现两者单独使用时准确性相近，结合使用略有提升，且基于 Elo 评分的种子排位接近最优。
TU Dortmund University（多特蒙德工业大学）；Molde University College（莫尔德大学学院）；Ghent University（根特大学）；Technical University of Munich（慕尼黑工业大学）；Universität Innsbruck（因斯布鲁克大学）
Three state-of-the-art statistical ranking methods for forecasting football matches are combined with several other predictors in a hybrid machine learning model. Namely an ability estimate for every team based on historic matches; an ability estimate for every team based on bookmaker consensus; average plus-minus player ratings based on their individual performances in their home clubs and national teams; and further team covariates (e.g., market value, team structure) and country-specific socio-economic factors (population, GDP). The proposed combined approach is used for learning the number…
