Using Deep Convolutional Neural Networks to Predict Goal-Scoring Opportunities in Soccer

Deep learning approaches have successfully been applied to several image recognition tasks, such as face, object, animal and plant classification. However, almost no research has examined on how to use the field of machine learning to predict goal-scoring opportunities in soccer from position data. In this paper, we propose the use of deep convolutional neural networks (DCNNs) for the above stated problem. This aim is actualized using the following steps: 1) development of novel algorithms for finding goal-scoring opportunities and ball possession which are used to obtain positive and negative examples. The dataset consists of position data from 29 matches played by a German Bundlesliga team. 2) These examples are used to create original and enhanced images (which contain object trails of soccer positions) with a resolution size of $256 \times 256$ pixels. 3) Both the original and enhanced images are fed independently as input to two DCNN methods: instances of both GoogLeNet and a 3-layered CNN architecture. A K-nearest neighbor classifier was trained and evaluated on ball positions as a baseline experiment. The results show that the GoogLeNet architecture outperforms all other methods with an accuracy of 67.1%

サッカーPK戦におけるゲーム理論上の最適戦略とプロの戦略との差異に関する考察

人や企業は様々な条件下で最適な行動を取るのだろうか．取らないのであればそれはなぜか．その原因を求めることは，実際の個人・企業等の理解を大きく助ける．また，ゲーム理論はスポーツや経済学そしてその他の社会科学の理解に大きく関わってきた．本研究は比較的データが集めやすく混合戦略を適用できるサッカーのPK戦に注目し，独自の確率を考慮した利得表を作成した．その利得表を用いてPK戦におけるキッカーの最適戦略を求め，最適戦略と実際の戦略とのズレを明らかにした．そのズレの原因を求める為にデータセット内の各データ項目についての確率分布を比較するというアプローチをした．データはインターネット動画サイトより収集した，プロ選手による2001年〜2017年の間の世界各国のPK戦150試合（計1539人分）を使用した．実験結果として，最適戦略と実際の戦略との間にズレが存在することが分かった．またそのズレには国籍・スコア差の関与が示唆された．その結果から，サッカーPK戦における最適戦略と実際の戦略との間におけるズレの原因を推定した．本手法はスポーツ分野以外への応用も期待できる

Deep learning for animal recognition

Deep learning has obtained many successes in different computer vision tasks such as classification, detection, and segmentation of objects or faces. Many of these successes can be ascribed to training deep convolutional neural network architectures on a dataset containing many images. Limited research has explored deep learning methods for performing recognition or detection of animals using a limited number of images. This thesis examines the use of different deep learning techniques and conventional computer vision methods for performing animal recognition or detection with relatively small training datasets and has the following objectives: 1) Analyse the performance of deep learning systems compared to classical approaches when there exists a limited number of images of animals; 2) Develop an algorithm for effectively dealing with rotation variation naturally present in aerial images; 3) Construct a computer vision system that is more robust to illumination variation; 4) Analyse how important the use of different color spaces is in deep learning; 5) Compare different deep convolutional neural-network algorithms for detecting and recognizing individual instances (identities) in a group of animals, for example, badgers. For most of the experiments, effectively reduced neural network recognition systems are used, which are derived from existing architectures. These reduced systems are compared to standard architectures and classical computer vision methods. We also propose a color transformation algorithm, a novel rotation-matrix data-augmentation algorithm and a hybrid variant of such a method, that factors color constancy with the aim to enhance images and construct a system that is more robust to different kinds of visual appearances. The results show that our proposed algorithms aid deep learning systems to become more accurate in classifying animals for a large number of different animal datasets. Furthermore, the developed systems yield performances that significantly surpass classical computer vision techniques, even with limited amounts of available images for training