Riding a probabilistic support vector machine to the Stanley Cup

Demers, Simon

doi:10.1515/jqas-2014-0093

Cited by 9 publications

(8 citation statements)

References 6 publications

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“…With regard to the rest of team sports, AI applications were reported in professional ice hockey (Markov process [56] and support vector machine [59, 60]), cricket ( K -means clustering [49]), field hockey (decision tree classifier [47]), and rugby (artificial neural network [34]). All these AI applications were on the “technical and tactical analysis” area, confirming the importance of these aspects for success in the team sports [98].…”

Section: Discussionmentioning

confidence: 99%

Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

Claudino

Capanema

Souza³

et al. 2019

Sports Med - Open

209

117

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Background The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sport performance and injury risk and to find out which AI techniques each sport has been using. Methods Systematic searches through the PubMed, Scopus, and Web of Science online databases were conducted for articles reporting AI techniques or methods applied to team sports athletes. Results Fifty-eight studies were included in the review with 11 AI techniques or methods being applied in 12 team sports. Pooled sample consisted of 6456 participants (97% male, 25 ± 8 years old; 3% female, 21 ± 10 years old) with 76% of them being professional athletes. The AI techniques or methods most frequently used were artificial neural networks, decision tree classifier, support vector machine, and Markov process with good performance metrics for all of them. Soccer, basketball, handball, and volleyball were the team sports with more applications of AI. Conclusions The results of this review suggest a prevalent application of AI methods in team sports based on the number of published studies. The current state of development in the area proposes a promising future with regard to AI use in team sports. Further evaluation research based on prospective methods is warranted to establish the predictive performance of specific AI techniques and methods. Electronic supplementary material The online version of this article (10.1186/s40798-019-0202-3) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

Claudino

Capanema

Souza³

et al. 2019

Sports Med - Open

209

117

View full text Add to dashboard Cite

show abstract

“…The focus of recent prediction research in other sports, such as basketball and hockey, has been predicting game outcomes. Prediction outcomes of NCAA tournament games (Dutta, Jacobson, and Sauppe, 2017), NBA regular season games (Manners 2016), European hockey league regular season games (Marek,Šedivá, andŤoupal, 2014), and NHL playoff games (Demers, 2015) have all been examined using a variety of prediction models.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting plays in the National Football League

Fernandes

Yakubov

et al. 2019

Journal of Sports Analytics

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This paper aims to develop an interpretable machine learning model to predict plays (pass versus rush) in the National Football League that will be useful for players and coaches in real time. Using data from the 2013-2014 to 2016-2017 NFL regular seasons, which included 1034 games and 130,344 pass/rush plays, we first develop and compare several machine learning models to determine the maximum possible prediction accuracy. The best performing model, a neural network, achieves a prediction accuracy of 75.3%, which is competitive with the state-of-the-art methods applied to other datasets. Then, we search over a family of simple decision tree models to identify one that captures 86% of the prediction accuracy of the neural network yet can be easily memorized and implemented in an actual game. We extend the analysis to building decision tree models tailored for each of the 32 NFL teams, obtaining accuracies ranging from 64.7% to 82.5%. Overall, our decision tree models can be a useful tool for coaches and players to improve their chances of stopping an offensive play.

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“…Support Vector Machines (SVMs) are a powerful supervised learning method used in data analysis and pattern recognition, which also have been widely adopted for prediction in a variety of sport domains (Demens, 2015;Haghighat, Rastegari, & Nourafza, 2013;Schumaker, Solieman, & Chen, 2010a). SVMs can be used for classification and regression.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

Soto-Valero

2016

International Journal of Computer Science in Sport

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Baseball is a statistically filled sport, and predicting the winner of a particular Major League Baseball (MLB) game is an interesting and challenging task. Up to now, there is no definitive formula for determining what factors will conduct a team to victory, but through the analysis of many years of historical records many trends could emerge. Recent studies concentrated on using and generating new statistics called sabermetrics in order to rank teams and players according to their perceived strengths and consequently applying these rankings to forecast specific games. In this paper, we employ sabermetrics statistics with the purpose of assessing the predictive capabilities of four data mining methods (classification and regression based) for predicting outcomes (win or loss) in MLB regular season games. Our model approach uses only past data when making a prediction, corresponding to ten years of publicly available data. We create a dataset with accumulative sabermetrics statistics for each MLB team during this period for which data contamination is not possible. The inherent difficulties of attempting this specific sports prediction are confirmed using two geometry or topology based measures of data complexity. Results reveal that the classification predictive scheme forecasts game outcomes better than regression scheme, and of the four data mining methods used, SVMs produce the best predictive results with a mean of nearly 60% prediction accuracy for each team. The evaluation of our model is performed using stratified 10-fold cross-validation.

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Riding a probabilistic support vector machine to the Stanley Cup

Cited by 9 publications

References 6 publications

Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

Predicting plays in the National Football League

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

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