Shoot Like Ronaldo: Predict Soccer Penalty Outcome with Wearables

Chakma, Avijoy; Faridee, Abu Zaher Md; Roy, Nirmalya; Hossain, H M Sajjad

doi:10.1109/percomworkshops48775.2020.9156244

Cited by 7 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning gained immense popularity due to the generalizability and scalability characteristics compared to the traditional ML and SL analysis methodologies. Researchers successfully showed that the deep learning algorithms are better compared to the traditional ML algorithms (Chakma et al, 2020; Faridee et al, 2018), particularly in the domain of complex feature representation learning (Bengio et al, 2013) and performance (I. Ghosh, Ramamurthy, & Roy, 2020). In deep learning, the raw features are learned automatically by performing some nonlinear activation functions and shift‐invariant transformation functions, which helps retrieve better feature representation than the traditional learning algorithms.…”

Section: Methodologies In Sports Analyticsmentioning

confidence: 99%

“…Various state‐of‐the‐art ML techniques have been applied to sensory and computer vision‐based datasets to solve real‐time sports analytics inference. Deep learning, reinforcement learning approaches, and so on have proven to be more effective than the classical statistical learning (SL) techniques in extracting knowledge and discovering, learning, and inferring data activities enumerated by Chakma et al (2020) and I. Ghosh, Ramamurthy, and Roy (2020). The activities/shots can be defined as the players' micro‐complex and nonperiodicity limb movements, which eventually increases the complexity for real‐time tracking models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

Ghosh

Ramamurthy

Chakma

et al. 2023

WIREs Data Min & Knowl

Self Cite

View full text Add to dashboard Cite

The rapid and impromptu interest in the coupling of machine learning (ML) algorithms with wearable and contactless sensors aimed at tackling real‐world problems warrants a pedagogical study to understand all the aspects of this research direction. Considering this aspect, this survey aims to review the state‐of‐the‐art literature on ML algorithms, methodologies, and hypotheses adopted to solve the research problems and challenges in the domain of sports. First, we categorize this study into three main research fields: sensors, computer vision, and wireless and mobile‐based applications. Then, for each of these fields, we thoroughly analyze the systems that are deployable for real‐time sports analytics. Next, we meticulously discuss the learning algorithms (e.g., statistical learning, deep learning, reinforcement learning) that power those deployable systems while also comparing and contrasting the benefits of those learning methodologies. Finally, we highlight the possible future open‐research opportunities and emerging technologies that could contribute to the domain of sports analytics.This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Technologies > Internet of Things

show abstract

Section: Methodologies In Sports Analyticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

Ghosh

Ramamurthy

Chakma

et al. 2023

WIREs Data Min & Knowl

Self Cite

View full text Add to dashboard Cite

show abstract

“…Confounding ball contacts were not part of the session [ 31 ]. In [ 32 ], the direction of penalty shots was classified from accelerometer data using traditional machine learning models as well as a convolutional neural net (CNN). The proposed CNN architecture outperformed the traditional methods reaching an accuracy of 53%.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed CNN architecture outperformed the traditional methods reaching an accuracy of 53%. The dataset consisted of a real-life penalty shoot out of 4 players [ 32 ]. To the extent of our knowledge, there is no work investigating the sensor-based classification of different kick types under real-world conditions.…”

Section: Related Workmentioning

confidence: 99%

From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

Stoeve

Schuldhaus

Gamp

et al. 2021

Sensors

View full text Add to dashboard Cite

The applicability of sensor-based human activity recognition in sports has been repeatedly shown for laboratory settings. However, the transferability to real-world scenarios cannot be granted due to limitations on data and evaluation methods. On the example of football shot and pass detection against a null class we explore the influence of those factors for real-world event classification in field sports. For this purpose we compare the performance of an established Support Vector Machine (SVM) for laboratory settings from literature to the performance in three evaluation scenarios gradually evolving from laboratory settings to real-world scenarios. In addition, three different types of neural networks, namely a convolutional neural net (CNN), a long short term memory net (LSTM) and a convolutional LSTM (convLSTM) are compared. Results indicate that the SVM is not able to reliably solve the investigated three-class problem. In contrast, all deep learning models reach high classification scores showing the general feasibility of event detection in real-world sports scenarios using deep learning. The maximum performance with a weighted f1-score of 0.93 was reported by the CNN. The study provides valuable insights for sports assessment under practically relevant conditions. In particular, it shows that (1) the discriminative power of established features needs to be reevaluated when real-world conditions are assessed, (2) the selection of an appropriate dataset and evaluation method are both required to evaluate real-world applicability and (3) deep learning-based methods yield promising results for real-world HAR in sports despite high variations in the execution of activities.

show abstract