这个python案例怎么看这次攻守转换速度？

wen python案例 2026-08-20 4

本文目录导读：

找到攻守转换的判定逻辑
计算攻守转换速度
使用时间戳或帧数计算
可视化转换速度
计算关键指标
实战建议

要分析Python案例中的攻守转换速度,你需要从以下几个方面入手：

找到攻守转换的判定逻辑

在代码中找到判断攻守状态的函数或条件,通常会有类似这样的结构：

def is_attacking(state):
    # 判断当前是否处于进攻状态
    return state['possession'] == 'us' and state['in_opponent_half'] == True
def is_defending(state):
    return state['possession'] == 'opponent' or state['in_own_half'] == True

计算攻守转换速度

攻守转换速度 = 从防守状态变为进攻状态（或反之）的时间间隔

def calculate_transition_speed(states):
    transitions = []
    current_phase = None
    for state in states:
        if is_attacking(state):
            phase = 'attack'
        elif is_defending(state):
            phase = 'defend'
        else:
            continue
        if phase != current_phase:
            if current_phase is not None:
                transitions.append({
                    'from': current_phase,
                    'to': phase,
                    'time': state['timestamp']
                })
            current_phase = phase
    # 计算转换时间差
    speeds = []
    for i in range(1, len(transitions)):
        time_diff = transitions[i]['time'] - transitions[i-1]['time']
        speeds.append({
            'type': f"{transitions[i-1]['to']}->{transitions[i]['to']}",
            'speed': time_diff
        })
    return speeds

使用时间戳或帧数计算

如果案例中有时间戳,用时间戳差值计算：

transitions_speed = []
for i in range(len(states)-1):
    if states[i]['status'] != states[i+1]['status']:  # 状态变了
        time_diff = states[i+1]['timestamp'] - states[i]['timestamp']
        transitions_speed.append({
            'from': states[i]['status'],
            'to': states[i+1]['status'],
            'duration': time_diff
        })

可视化转换速度

import matplotlib.pyplot as plt
def visualize_transitions(transitions):
    attack_to_defend = [t['duration'] for t in transitions if t['type'] == 'attack->defend']
    defend_to_attack = [t['duration'] for t in transitions if t['type'] == 'defend->attack']
    plt.figure(figsize=(10, 5))
    plt.subplot(1, 2, 1)
    plt.hist(attack_to_defend, alpha=0.7, label='攻转守')
    plt.xlabel('时间(秒)')
    plt.ylabel('次数')
    plt.title('攻转守速度分布')
    plt.legend()
    plt.subplot(1, 2, 2)
    plt.hist(defend_to_attack, alpha=0.7, label='守转攻', color='green')
    plt.xlabel('时间(秒)')
    plt.ylabel('次数')
    plt.title('守转攻速度分布')
    plt.legend()
    plt.tight_layout()
    plt.show()

计算关键指标

def get_transition_metrics(transitions):
    attack_to_defend_times = [t['duration'] for t in transitions if t['from'] == 'attack' and t['to'] == 'defend']
    defend_to_attack_times = [t['duration'] for t in transitions if t['from'] == 'defend' and t['to'] == 'attack']
    return {
        'attack_to_defend_avg': np.mean(attack_to_defend_times) if attack_to_defend_times else None,
        'defend_to_attack_avg': np.mean(defend_to_attack_times) if defend_to_attack_times else None,
        'max_transition_speed': min(defend_to_attack_times + attack_to_defend_times) if transitions else None,
        'total_transitions': len(transitions)
    }