PassAI: explainable artificial intelligence algorithm for soccer pass analysis using multimodal information resources
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912999589019648 |
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| author | Takamido, Ryota Ota, Jun Nakamoto, Hiroki |
| author_facet | Takamido, Ryota Ota, Jun Nakamoto, Hiroki |
| contents | This study developed a new explainable artificial intelligence algorithm called PassAI, which classifies successful or failed passes in a soccer game and explains its rationale using both tracking and passer's seasonal stats information. This study aimed to address two primary challenges faced by artificial intelligence and machine learning algorithms in the sports domain: how to use different modality data for the analysis and how to explain the rationale of the outcome from multimodal perspectives. To address these challenges, PassAI has two processing streams for multimodal information: tracking image data and passer's stats and classifying pass success and failure. After completing the classification, it provides a rationale by either calculating the relative contribution between the different modality data or providing more detailed contribution factors within the modality. The results of the experiment with 6,349 passes of data obtained from professional soccer games revealed that PassAI showed higher classification performance than state-of-the-art algorithms by >5% and could visualize the rationale of the pass success/failure for both tracking and stats data. These results highlight the importance of using multimodality data in the sports domain to increase the performance of the artificial intelligence algorithm and explainability of the outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08945 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PassAI: explainable artificial intelligence algorithm for soccer pass analysis using multimodal information resources Takamido, Ryota Ota, Jun Nakamoto, Hiroki Human-Computer Interaction 68T99 J.4 This study developed a new explainable artificial intelligence algorithm called PassAI, which classifies successful or failed passes in a soccer game and explains its rationale using both tracking and passer's seasonal stats information. This study aimed to address two primary challenges faced by artificial intelligence and machine learning algorithms in the sports domain: how to use different modality data for the analysis and how to explain the rationale of the outcome from multimodal perspectives. To address these challenges, PassAI has two processing streams for multimodal information: tracking image data and passer's stats and classifying pass success and failure. After completing the classification, it provides a rationale by either calculating the relative contribution between the different modality data or providing more detailed contribution factors within the modality. The results of the experiment with 6,349 passes of data obtained from professional soccer games revealed that PassAI showed higher classification performance than state-of-the-art algorithms by >5% and could visualize the rationale of the pass success/failure for both tracking and stats data. These results highlight the importance of using multimodality data in the sports domain to increase the performance of the artificial intelligence algorithm and explainability of the outcomes. |
| title | PassAI: explainable artificial intelligence algorithm for soccer pass analysis using multimodal information resources |
| topic | Human-Computer Interaction 68T99 J.4 |
| url | https://arxiv.org/abs/2503.08945 |