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| Paper | Proposal/Result |
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| [27] | Research of strategies most used for the recognition and classification of human movement patterns. |
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| [23] | Analysis of sports skills data with temporal series image data retrieved from films focused on table tennis. |
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| [33] | Guide the athletes on how to improve their performance and how to eliminate errors related to the selection of the proper running strategy through the differential evolution algorithm. |
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| [31] | Proposes a new clustering algorithm based on ant colony optimization. |
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| [35] | Proposed the development of an information extraction system wherein its purpose was to obtain data frames of multiple sports performance documents. |
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| [37] | Automatic generation of optimal food plans for athletes, through the particle swarm optimization algorithm. |
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| [47] | Proposed an automated personal trainer. |
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| [49] | Solution for automatic planning of training sessions. |
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| [39] | A new solution capable of adapting training plans. |
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| [41] | A framework to automatically analyze the physiological signals monitored during a test session. |
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| [43] | Implementation of artificial intelligence routines for automatic evaluation of exercises in weight training. |
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| [45] | Presented three geometric/temporal features of pen trajectories used in a cognitive skills training application for elite basketball players. |
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| [24] | An data mining algorithm to soccer tactics using association rules mining. |
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| [53] | Discussed the application of the association rule mining in sports management, especially, in cricket. |
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| [51] | Presented a relational-learning based approach for discovering strategies in volleyball matches based on optical tracking data. |
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| [36] | A generalized predictive model for predicting the results of the English Premier League. |
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| [30] | A data analysis to identify important aspects separating skilled golfers from poor. |
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| [38] | Compared the performance of algebraic methods to some machine learning approaches, particularly in the field of match prediction. |
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| [40] | A sports data mining approach, which helps discover interesting knowledge and predict results from sports games such as college football. |
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| [42] | Data mining techniques for predicting basketball results in the NBA (National Basketball Association). |
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| [28] | Developed a tool COP (Cricket Outcome Predictor), which outputs the win/loss probability of a match. |
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| [32] | Classify players into regular or All-Star players from the National Basketball Association and identify the most important features that make an All-Star player. |
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| [26] | Designed and built a big data analytics framework for sports behavior mining and personalized health services. |
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| [34] | Provides a prediction model of sports results based on knowledge discovery in database. |
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| [25] | A machine learning system with unsupervised learning and supervised learning components to analyze chess data. |
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| [44] | Concluded that the most important elements in basketball are two-point shots under the arch and defensive rebound. |
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| [29] | A data mining approach for classification and identification of golf swing from weight shift data. |
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| [52] | Describes machine learning techniques that assist cycling experts in the decision-making processes for athlete selection and strategic planning. |
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| [46] | Predict match outcomes in the 2015 Rugby World Cup. |
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| [48] | Presented a visualization system that uses statistics and movement analysis. Basically, the type of pattern of attack and play can be understood dynamically and visually. |
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| [50] | Conducted a study on a decision support system for techniques and tactics in sports. |
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