Offering tracking analysis
Since the user is coding, I can help with analyzing a CSV of tracking positions to compute distances. I might ask if they’re talking about a specific match. If they share tracking data or stats, I can analyze that.
I’ll provide a short explanation of "run intensity," including total distance, high-speed running, and work rate. I’ll also include a concise Python code example to compute distances based on timestamped positions and typical benchmarks for soccer, like team distances of 100-120 km. Let's keep it collaborative and straightforward!Drafting concise code response
I need to avoid using too many bullet points in my final answer. I’ll acknowledge the user with something like, "Yes, run coverage and intensity are maximized." Then, I’ll ask for context: which match and if there are specific data points.
I’ll offer to analyze the CSV data with columns like player ID, time, x, and y to compute total distance, high-speed runs, and sprints. The code will use numpy/pandas to compute speed and highlight thresholds for high speed and sprints, keeping comments minimal. Alright, let's move forward with this!Providing distance computation code
I've drafted Python code to analyze tracking data from a CSV file. It sorts the data by player ID and time, computes distances using numpy for x and y positions, and calculates speeds.
The function identifies high-speed runs and sprints, returning total distances for each player as well. I note edge cases, like irregular sampling and zero time differences.
I'll propose benchmarks for soccer distances and, while referencing that high-speed sums typically vary, I’ll give rough values for team and player performance. This ensures clarity and utility for the user!
确实夸张！是哪个队、哪场比赛？有具体数据或热区图的话我可以帮你做下快速分析。
如果你有全队的定位/跑动数据（CSV：player_id,time_s,x_m,y_m），用下面这段 Python 就能算出全队总跑动、高速跑、冲刺距离，并列出跑动最多的球员：
import pandas as pd, numpy as np
# CSV 需包含: player_id,time_s(秒),x_m,y_m(单位:米); 若是经纬度需先转米或用haversine
df = pd.read_csv('tracking.csv').sort_values(['player_id','time_s'])
def metrics(g):
x, y, t = g['x_m'].to_numpy(), g['y_m'].to_numpy(), g['time_s'].to_numpy()
dx = np.diff(x, prepend=x[0]); dy = np.diff(y, prepend=y[0])
dt = np.diff(t, prepend=np.nan); dt[0] = np.nan
dist = np.hypot(dx, dy); dist[0] = 0.0
speed = np.divide(dist, dt, out=np.zeros_like(dist), where=~np.isnan(dt)) # m/s
hs = (speed >= 5.5) # 高速跑阈值≈19.8 km/h
sp = (speed >= 7.0) # 冲刺阈值≈25.2 km/h
return pd.Series({
'distance_km': dist.sum()/1000.0,
'hs_km': (dist*hs).sum()/1000.0,
'sprint_km': (dist*sp).sum()/1000.0
})
per_player = df.groupby('player_id', sort=False).apply(metrics).reset_index()
team = per_player[['distance_km','hs_km','sprint_km']].sum()
print('Team totals (km):')
print(team.to_dict())
print('\nTop runners:')
print(per_player.sort_values('distance_km', ascending=False).head(5))
参考值（足球，90 分钟）：全队总跑动常见在 100–120 km；明显高于这个就很“吓人”。你要是给我数据文件或比赛链接，我可以直接跑一版并解读给你看。