Modern soccer increasingly places trust in visual analysis and statistics rather than only relying on human experience. However, soccer is an extraordinarily complex game that there exist no widely-accepted quantitative analysis methods. And the statistics collection and visualization are time-consuming which result in numerous adjustments. To tackle this issue, we developed GreenSea, a visual-based assessment system designed for soccer game analysis, tactics, and training. The system uses broad learning system (BLS) to train the model, in order to avoid the time-consuming issue that traditional deep learning may suffer. Users are able to apply multiple views of a soccer game, and visual summarization of essential statistics using advanced visualization and animation are available. A marking system trained by BLS is designed to performance quantitative analysis. A novel recurrent discriminative broad learning system (RDBLS) is proposed to carry out a long-term tracking. In our RDBLS, the structure is adjusted to have better performance on binary classification problem of discriminative model. Several experiments are carried out to verify that our proposed RDBLS model can outperform the standard BLS and other methods. Two studies were conducted to verify the effectiveness of our GreenSea. The first study was on how GreenSea assists a youth training coach to assess each trainee's performance for selecting most potential players. The second study was on how GreenSea was used to help U20 Shanghai soccer team coaching staff analyze games and make tactics during the thirteenth National Games. Our studies have shown the usability of GreenSea and the values of our system to both amateur and expert users.
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