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Data Analytics in Sports Sciences: Changing the Game

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 24503

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Dr. Filipe Manuel Clemente

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Polytechnic Institute of Viana do Castelo, School of Sport and Leisure, 4960-320 Melgaço, Portugal
Interests: football; soccer; match analysis; performance analysis; network analysis
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Special Issue Information

Dear Colleagues,

Currently, many monitoring instruments are used daily in sports sciences to track information about the training load, sports performance, well-being, readiness, and lifestyle of athletes. This information generates an enormous amount of data that, without the correct process, will not provide useful information for coaches and sports scientists working with athletes. For this reason, data analytics and statistics have increased in popularity in sports sciences, namely, by applying new methods that help to quickly understand the most determinant information and generate useful insights for the practice.

The use of non-linear statistics, artificial intelligence, Bayesian statistics, and machine learning is not often reported on in a sports sciences context. However, there is still a need for more applications and scientific research about how to properly use these methods, techniques, and approaches to consistently better understand their usability in sports. Trying to push forward innovative approaches, this Special Issue calls for original articles, systematic reviews, and meta-analyses that can meaningfully contribute to the field of data analytics in sports using data treatment and data processing approaches. Specific articles bridging the gap between science and practice are particularly welcome.

Dr. Filipe Manuel Clemente
Guest Editor

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Keywords

non-linear statistics
big data
data analytics
machine learning
sports sciences
Bayesian statistics

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Published Papers (5 papers)

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Research

18 pages, 646 KiB

Open AccessArticle

Transfering Targeted Maximum Likelihood Estimation for Causal Inference into Sports Science

by Talko B. Dijkhuis and Frank J. Blaauw

Entropy 2022, 24(8), 1060; https://doi.org/10.3390/e24081060 - 31 Jul 2022

Cited by 1 | Viewed by 2705

Abstract

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. Full article

(This article belongs to the Special Issue Data Analytics in Sports Sciences: Changing the Game)

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13 pages, 493 KiB

Open AccessArticle

A Bayesian Approach to Predict Football Matches with Changed Home Advantage in Spectator-Free Matches after the COVID-19 Break

by Jaemin Lee, Juhuhn Kim, Hyunho Kim and Jong-Seok Lee

Entropy 2022, 24(3), 366; https://doi.org/10.3390/e24030366 - 4 Mar 2022

Cited by 8 | Viewed by 7103

Abstract

Since the coronavirus disease 2019 (COVID-19) pandemic, most professional sports events have been held without spectators. It is generally believed that home teams deprived of enthusiastic support from their home fans experience reduced benefits of playing on their home fields, thus becoming less likely to win. This study attempts to confirm if this belief is true in four major European football leagues through statistical analysis. This study proposes a Bayesian hierarchical Poisson model to estimate parameters reflecting the home advantage and the change in such advantage. These parameters are used to improve the performance of machine-learning-based prediction models for football matches played after the COVID-19 break. The study describes the statistical analysis on the impact of the COVID-19 pandemic on football match results in terms of the expected score and goal difference. It also shows that estimated parameters from the proposed model reflect the changed home advantage. Finally, the study verifies that these parameters, when included as additional features, enhance the performance of various football match prediction models. The home advantage in European football matches has changed because of the behind-closed-doors policy implemented due to the COVID-19 pandemic. Using parameters reflecting the pandemic’s impact, it is possible to predict more precise results of spectator-free matches after the COVID-19 break. Full article

(This article belongs to the Special Issue Data Analytics in Sports Sciences: Changing the Game)

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12 pages, 1364 KiB

Open AccessArticle

The “Hockey” Assist Makes the Difference—Validation of a Defensive Disruptiveness Model to Evaluate Passing Sequences in Elite Soccer

by Leander Forcher, Matthias Kempe, Stefan Altmann, Leon Forcher and Alexander Woll

Entropy 2021, 23(12), 1607; https://doi.org/10.3390/e23121607 - 30 Nov 2021

Cited by 7 | Viewed by 3128

Abstract

With the growing availability of position data in sports, spatiotemporal analysis in soccer is a topic of rising interest. The aim of this study is to validate a performance indicator, namely D-Def, measuring passing effectiveness. D-Def calculates the change of the teams’ centroid, centroids of formation lines (e.g., defensive line), teams’ surface area, and teams’ spread in the following three seconds after a pass and therefore results in a measure of disruption of the opponents’ defense following a pass. While this measure was introduced earlier, in this study we aim to prove the usefulness to evaluate attacking sequences. In this study, 258 games of Dutch Eredivisie season 2018/19 were included, resulting in 13,094 attacks. D-Def, pass length, pass velocity, and pass angle of the last four passes of each attack were calculated and compared between successful and unsuccessful attacks. D-Def showed higher values for passes of successful compared to unsuccessful attacks (0.001 < p ≤ 0.029, 0.06 ≤ d ≤ 0.23). This difference showed the highest effects sizes in the penultimate pass (d = 0.23) and the maximal D-Def value of an attack (d = 0.23). Passing length (0.001 < p ≤ 0.236, 0.08 ≤ d ≤ 0.17) and passing velocity (0.001 < p ≤ 0.690, −0.09 ≤ d ≤ 0.12) showed inconsistent results in discriminating between successful and unsuccessful attacks. The results indicate that D-Def is a useful indicator for the measurement of pass effectiveness in attacking sequences, highlighting that successful attacks are connected to disruptive passing. Within successful attacks, at least one high disruptive action (pass with D-Def > 28) needs to be present. In addition, the penultimate pass (“hockey assist”) of an attack seems crucial in characterizing successful attacks. Full article

(This article belongs to the Special Issue Data Analytics in Sports Sciences: Changing the Game)

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16 pages, 1526 KiB

Open AccessArticle

Effects of Match Location, Quality of Opposition and Match Outcome on Match Running Performance in a Portuguese Professional Football Team

by José E. Teixeira, Miguel Leal, Ricardo Ferraz, Joana Ribeiro, José M. Cachada, Tiago M. Barbosa, António M. Monteiro and Pedro Forte

Entropy 2021, 23(8), 973; https://doi.org/10.3390/e23080973 - 29 Jul 2021

Cited by 40 | Viewed by 4455

Abstract

The aim of this study was to analyze the effects of match location, quality of opposition and match outcome on match running performance according to playing position in a Portuguese professional football team. Twenty-three male professional football players were monitored from eighteen Portuguese Football League matches during the 2019–2020 season. Global positioning system technology (GPS) was used to collect time-motion data. The match running performance was obtained from five playing positions: central defenders (CD), fullbacks (FB), central midfielders (CM), wide midfielders (WM) and forwards (FW). Match running performance was analyzed within specific position and contextual factors using one-way analysis of variance (ANOVA) for repeated measures, standardized (Cohen) differences and smallest worthwhile change. CM and WM players covered significantly greater total distance (F = 15.45, p = 0.000, η² = 0.334) and average speed (F = 12.79, p < 0.001, η² = 0.294). WM and FB players covered higher distances at high-speed running (F = 16.93, p = 0.000, η² = 0.355) and sprinting (F = 13.49; p < 0.001, η² = 0.305). WM players covered the highest number of accelerations (F = 4.69, p < 0.001, η² = 0.132) and decelerations (F = 12.21, p < 0.001, η² = 0.284). The match running performance was influenced by match location (d = 0.06–2.04; CI: −0.42–2.31; SWC = 0.01–1.10), quality of opposition (d = 0.13–2.14; CI: –0.02–2.60; SWC = 0.01–1.55) and match outcome (d = 0.01–2.49; CI: −0.01–2.31; SWC = 0.01–0.35). Contextual factors influenced the match running performance with differential effects between playing positions. This study provides the first report about the contextual influence on match running performance in a Portuguese professional football team. Future research should also integrate tactical and technical key indicators when analyzing the match-related contextual influence on match running performance. Full article

(This article belongs to the Special Issue Data Analytics in Sports Sciences: Changing the Game)

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15 pages, 1997 KiB

Open AccessArticle

Early Prediction of Physical Performance in Elite Soccer Matches—A Machine Learning Approach to Support Substitutions

by Talko B. Dijkhuis, Matthias Kempe and Koen A. P. M. Lemmink

Entropy 2021, 23(8), 952; https://doi.org/10.3390/e23080952 - 25 Jul 2021

Cited by 16 | Viewed by 3800

Abstract

Substitution is an essential tool for a coach to influence the match. Factors like the injury of a player, required tactical changes, or underperformance of a player initiates substitutions. This study aims to predict the physical performance of individual players in an early phase of the match to provide additional information to the coach for his decision on substitutions. Tracking data of individual players, except for goalkeepers, from 302 elite soccer matches of the Dutch ‘Eredivisie’ 2018–2019 season were used to enable the prediction of the individual physical performance. The players’ physical performance is expressed in the variables distance covered, distance in speed category, and energy expenditure in power category. The individualized normalized variables were used to build machine learning models that predict whether players will achieve 100%, 95%, or 90% of their average physical performance in a match. The tree-based algorithms Random Forest and Decision Tree were applied to build the models. A simple Naïve Bayes algorithm was used as the baseline model to support the superiority of the tree-based algorithms. The machine learning technique Random Forest combined with the variable energy expenditure in the power category was the most precise. The combination of Random Forest and energy expenditure in the power category resulted in precision in predicting performance and underperformance after 15 min in a match, and the values were 0.91, 0.88, and 0.92 for the thresholds 100%, 95%, and 90%, respectively. To conclude, it is possible to predict the physical performance of individual players in an early phase of the match. These findings offer opportunities to support coaches in making more informed decisions on player substitutions in elite soccer. Full article

(This article belongs to the Special Issue Data Analytics in Sports Sciences: Changing the Game)

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