Can Machine Learning Predict Soccer Match Results?

Capobianco, Giovanni; Giacomo, Umberto Di; Mercaldo, Francesco; Nardone, Vittoria; Santone, Antonella

doi:10.5220/0007307504580465

Cited by 7 publications

(2 citation statements)

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“…In 2018, a tree-based boosting model was proposed to predict biathlon shooting performance [8]. In 2019, a model using the Random Forest algorithm obtained a precision of 0.857 and a recall of 0.750 in soccer match prediction [9]. The model was trained with data acquirable both after and during the match.…”

Section: Machine Learning and Literature Reviewmentioning

confidence: 99%

Refereeing the Sport of Squash with a Machine Learning System

Ma,

Kabala

2024

MAKE

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Squash is a sport where referee decisions are essential to the game. However, these decisions are very subjective in nature. Disputes, both from the players and the audience, regularly occur because the referee made a controversial call. In this study, we propose automating the referee decision process through machine learning. We trained neural networks to predict such decisions using data from 400 referee decisions acquired through extensive video footage reviewing and labeling. Six positional values were extracted, including the attacking player’s position, the retreating player’s position, the ball’s position in the frame, the ball’s projected first bounce, the ball’s projected second bounce, and the attacking player’s racket head position. We calculated nine additional distance values, such as the distance between players and the distance from the attacking player’s racket head to the ball’s path. Models were trained on Wolfram Mathematica and Python using these values. The best Wolfram Mathematica model and the best Python model achieved accuracies of 86% ± 3.03% and 85.2% ± 5.1%, respectively. These accuracies surpass 85%, demonstrating near-human performance. Our model has great potential for improvement as it is currently trained with limited, unbalanced data (400 decisions) and lacks crucial data points such as time and speed. The performance of our model is almost surely going to improve significantly with a larger training dataset. Unlike human referees, machine learning models follow a consistent standard, have unlimited attention spans, and make decisions instantly. If the accuracy is improved in the future, the model can potentially serve as an extra refereeing official for both professional and amateur squash matches. Both the analysis of referee decisions in squash and the proposal to automate the process using machine learning is unique to this study.

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Section: Machine Learning and Literature Reviewmentioning

confidence: 99%

Refereeing the Sport of Squash with a Machine Learning System

Ma,

Kabala

2024

MAKE

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“…In literature there are several works focused about the most important factors that influence the results of a game. In (Capobianco et al,2019), the authors propose a new feature set aimed to model a soccer match. The set is related to characteristics obtainable not only at the end of the match, and it is used to predict the results of the match and the number of goal scored by the team that won the game.…”

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confidence: 99%

Machine Learning on Soccer Player Positions

Giacomo

Mercaldo

Santone

et al. 2022

International Journal of Decision Support System Technology

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During the last few years, sports analytics has been growing rapidly. The main usage of this discipline is the prediction of soccer match results, even if it can be applied with interesting results in different areas, such as analysis based on the player position information. In this paper, we propose an approach aimed to recognize the player position in a soccer match, predicting the specific zone in which the player is located in a specific moment. Similar objectives have never been considered yet with our best knowledge. We consider supervised machine learning techniques by considering a dataset obtained through video capturing and tracking system. The data analyzed refer to several professional soccer games captured at the Alfheim Stadium in Tromso, Norway. The approach can be used in real-time, in order to verify if a player is playing according to the guidelines of the coach. In the experimental analysis, three different types of classification have been performed, i.e., three different divisions of the field, reaching the best results with Random Tree Algorithm.

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