Considering analysis steps

I'm not entirely sure here; I want to avoid making specific claims and keep things general. I think it might be good to offer some potential next steps. Should I pull league-wide minutes and visualize those, if I have permission to run scripts? However, I’m unsure if the environment has network access. I could propose a simple analysis plan if data is provided, keeping formatting light—maybe 5-8 bullet points with insights and next steps.Crafting analysis points

I want to highlight some analysis points for consideration. First, there’s the impressive 243% growth likely stemming from a small base; I'll need to check absolute minutes and share by team and player.

Next, I'll assess the quality of minutes—looking at starts, 60+ minute appearances, and crunch time versus garbage time.

Distribution is key, too, as I explore Gini/Pareto principles for how many reach key minute thresholds. Then, I'll look at transitions from U21 to senior teams, comparing my findings with neighboring leagues but avoiding specific assertions.

I'll address structural drivers like salary caps and youth usage policies and consider the potential risks, such as short-termism and coaching changes. Lastly, I'll keep an eye on trends for 2025, focusing on minute trends, club pipelines, and metrics related to Asian competitions.Offering analysis help

I want to offer some help here. If the user shares their dataset, I can create a notebook to audit minutes and produce charts that highlight the top U21 contributions. Alternatively, I can draft a short outline for a social media article. Given the user’s exclamatory title-like line, it seems they might want a social media post.

I'll propose an outline and key charts, and I'll ask them if they want a short comment, longer article, or visualization. I'll keep my bullets concise. To match the sentiment of "数据不说谎，中国足球离春天近了！", I could respond with something like, "是积极信号，但别过度解读," and aim for a 100-200 word Chinese analytical comment with key bullet points.