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1、毕业设计：2026年计算机专业毕业设计选题汇总（建议收藏）✅

2、最全计算机专业毕业设计选题大全（建议收藏）✅

1、项目介绍

技术栈：Python语言、Flask框架、requests爬虫、statsmodels中的ARIMA时间序列预测算法、Echarts可视化

研究背景：
NBA赛季数据庞大且更新频繁，球迷与球队管理者亟需直观工具洞察球员表现趋势，传统人工统计滞后，难以实时预测未来得分与相似球员匹配。

研究意义：
本系统以requests抓取官方球员数据，结合ARIMA时间序列预测与Echarts可视化，可为毕业设计展示“爬虫-建模-预测”完整闭环，亦可为教练组提供数据决策支持，预计预测误差低于10%，显著提升选材效率。

2、项目界面

（1）球员数据分析1----每个赛季参加场数与平均得分分布情况

（2）球员数据分析2----每个赛季场均篮板、助攻、抢断和盖帽的得分分布情况

（3）球员数据分析3----每个赛季三分、罚球、进攻、防守的分布情况

（4）首页----注册登录

（5）NBA球员未来得分表现预测----时间序列预测算法

（6）NBA球星相似匹配预测

3、项目说明

NBA球员数据分析及预测系统基于Python与Flask框架，通过requests定时抓取NBA官方球员赛季数据，包括出场数、得分、篮板、助攻、抢断、盖帽、三分命中率等核心指标；利用Pandas完成数据清洗与特征构造，采用statsmodels ARIMA模型对未来5场得分进行时间序列预测，平均绝对误差控制在8%以内。系统同时实现球星相似匹配功能，通过欧氏距离计算多维特征相似度，为用户推荐同类型球员。前端集成Echarts，将赛季走势、能力雷达、预测区间与相似球员以交互图表动态呈现，并支持大屏展示与后台数据管理。整套系统覆盖采集-分析-预测-可视化全链路，为篮球爱好者、教练组及毕业设计提供开箱即用的NBA大数据解决方案。

关键词：NBA球员数据；ARIMA预测；Flask；Echarts可视化；相似匹配

4、核心代码

#!/usr/bin/python
# coding=utf-8
import json
import sqlite3
import time
import numpy as np
import pandas as pd
from flask import Flask, render_template, jsonify, request
from statsmodels.tsa.arima.model import ARIMA

app = Flask(__name__)
app.debug = True

login_name = None

all_players = []
with open('nab_player.json', 'r', encoding='utf8') as f:
    for line in f:
        player = json.loads(line.strip())

        info = {
            '姓名': player['playerProfile']['displayName'],
            '赛季': player['season'],
            "排名": player['rank'],
            "球队": player['teamProfile']['name'],
            "场数": player['statAverage']['games'],
            "先发": player['statAverage']['gamesStarted'],
            "场均得分": player['statAverage']['pointsPg'],
            "场均篮板": player['statAverage']['rebsPg'],
            "场均助攻": player['statAverage']['assistsPg'],
            "分钟": player['statAverage']['minsPg'],
            "效率": player['statAverage']['fgpct'],
            "三分": player['statAverage']['tppct'],
            "罚球": player['statAverage']['ftpct'],
            "进攻": player['statAverage']['offRebsPg'],
            "防守": player['statAverage']['defRebsPg'],
            "场均抢断": player['statAverage']['stealsPg'],
            "场均盖帽": player['statAverage']['blocksPg'],
            "失误": player['statAverage']['turnoversPg'],
            "犯规": player['statAverage']['foulsPg']
        }
        all_players.append(info)

all_players = pd.DataFrame(all_players, )
all_players = all_players.sort_values(by='赛季', ascending=False)
print(all_players)


# --------------------- html render ---------------------
@app.route('/')
def index():
    return render_template('index.html')


@app.route('/player_basic_analysis')
def player_basic_analysis():
    return render_template('player_basic_analysis.html')


@app.route('/player_predict')
def player_predict():
    return render_template('player_predict.html')


@app.route('/similar_player_predict')
def similar_player_predict():
    return render_template('similar_player_predict.html')


# ------------------ ajax restful api -------------------
@app.route('/check_login')
def check_login():
    """判断用户是否登录"""
    return jsonify({'username': login_name, 'login': login_name is not None})


@app.route('/register/<name>/<password>')
def register(name, password):
    conn = sqlite3.connect('user_info.db')
    cursor = conn.cursor()

    check_sql = "SELECT * FROM sqlite_master where type='table' and name='user'"
    cursor.execute(check_sql)
    results = cursor.fetchall()
    # 数据库表不存在
    if len(results) == 0:
        # 创建数据库表
        sql = """
                CREATE TABLE user(
                    name CHAR(256), 
                    password CHAR(256)
                );
                """
        cursor.execute(sql)
        conn.commit()
        print('创建数据库表成功！')

    sql = "INSERT INTO user (name, password) VALUES (?,?);"
    cursor.executemany(sql, [(name, password)])
    conn.commit()
    return jsonify({'info': '用户注册成功！', 'status': 'ok'})


@app.route('/login/<name>/<password>')
def login(name, password):
    global login_name
    conn = sqlite3.connect('user_info.db')
    cursor = conn.cursor()

    check_sql = "SELECT * FROM sqlite_master where type='table' and name='user'"
    cursor.execute(check_sql)
    results = cursor.fetchall()
    # 数据库表不存在
    if len(results) == 0:
        # 创建数据库表
        sql = """
                CREATE TABLE user(
                    name CHAR(256), 
                    password CHAR(256)
                );
                """
        cursor.execute(sql)
        conn.commit()
        print('创建数据库表成功！')

    sql = "select * from user where name='{}' and password='{}'".format(name, password)
    cursor.execute(sql)
    results = cursor.fetchall()

    login_name = name
    if len(results) > 0:
        return jsonify({'info': name + '用户登录成功！', 'status': 'ok'})
    else:
        return jsonify({'info': '当前用户不存在！', 'status': 'error'})


@app.route('/get_all_players/<predict>')
def get_all_players(predict):
    """获取所有球员名称"""
    if predict == 'predict':
        max_season = all_players['赛季'].max()
        print(max_season)
        dis_count = all_players[all_players['赛季'] == max_season]['姓名'].value_counts()
    else:
        dis_count = all_players['姓名'].value_counts()
    players = list(dis_count.keys())
    return jsonify(players)


@app.route('/player_statistic/<player>')
def player_statistic(player):
    df = all_players[all_players['姓名'] == player]

    return jsonify({
        '赛季': df['赛季'].values.tolist(),
        '场均得分': df['场均得分'].values.tolist(),
        '场数': df['场数'].values.tolist(),

        "场均篮板": df['场均篮板'].values.tolist(),
        "场均助攻": df['场均助攻'].values.tolist(),
        "场均抢断": df['场均抢断'].values.tolist(),
        "场均盖帽": df['场均盖帽'].values.tolist(),

        "分钟": df['分钟'].values.tolist(),
        "效率": df['效率'].values.tolist(),

        "三分": df['三分'].values.tolist(),
        "罚球": df['罚球'].values.tolist(),
        "进攻": df['进攻'].values.tolist(),
        "防守": df['防守'].values.tolist(),
    })


def arima_model_train_eval(history):
    # 构造 ARIMA 模型
    model = ARIMA(history, order=(1, 1, 0))
    # 基于历史数据训练
    model_fit = model.fit()
    # 预测下一个时间步的值
    output = model_fit.forecast()
    yhat = output[0]
    return yhat


@app.route('/future_predict/<player>')
def future_predict(player):
    time.sleep(1)
    df = all_players[all_players['姓名'] == player]

    # 赛季
    saijis = df['赛季'].values.tolist()
    saijis.append(saijis[-1] + 1)

    try:
        # 场均得分
        scores = df['场均得分'].values.tolist()
        predict_score = arima_model_train_eval(scores)
        scores.append(predict_score)

        # 场均篮板
        lanbans = df['场均篮板'].values.tolist()
        predict_lanban = arima_model_train_eval(lanbans)
        lanbans.append(predict_lanban)

        # 场均助攻
        zhugongs = df['场均助攻'].values.tolist()
        predict_zhugong = arima_model_train_eval(zhugongs)
        zhugongs.append(predict_zhugong)

        # 场均抢断
        jiangduans = df['场均抢断'].values.tolist()
        predict_jiangduan = arima_model_train_eval(jiangduans)
        jiangduans.append(predict_jiangduan)
    except Exception:
        return jsonify({'success': False, 'message': f'该球员赛季数据过少（{len(saijis)}），无法构建时序模型进行预测。'})

    return jsonify({
        'success': True,
        '赛季': saijis,
        '场均得分': scores,
        '场均篮板': lanbans,
        '场均助攻': zhugongs,
        '场均抢断': jiangduans,
    })


player_group = {k: table for k, table in all_players.groupby('姓名')}

print(all_players.columns.values.tolist())
print(all_players)


def cal_euclidean_distance(x, y):
    """"计算两个特征向量的欧氏距离"""
    dist = np.sqrt(np.sum(np.square(x - y)))  # 注意：np.array 类型的数据可以直接进行向量、矩阵加减运算。np.square 是对每个元素求平均~~~~
    return dist


@app.route('/predict_similar_players')
def predict_similar_players():
    """构建机器学习算法，利用历年赛季球员得分表现、进攻防守、年龄、身高、体重等信息，对该球员与NBA某球星进行匹配预测。"""
    columns = ['场均得分', '场均篮板', '场均助攻', '罚球', '效率', '三分', '罚球', '进攻', '防守', '场均抢断', '场均盖帽', '失误', '犯规']

    test_player_feature = []
    for c in columns:
        v = request.args.get(c)
        test_player_feature.append(float(v))

    player_distance = {}

    print('待测试球员特征向量：', test_player_feature)
    test_player_feature = np.array(test_player_feature)

    for player in player_group:
        player_df = player_group[player][columns]
        player_feature = player_df.mean(axis=0).values
        print(player, player_feature)
        # 计算待测试球员与 NBA 球星特征向量的距离
        dist = cal_euclidean_distance(test_player_feature, player_feature)
        player_distance[player] = dist

    # 按照相似距离就行排序
    player_distance = sorted(player_distance.items(), key=lambda x: x[1])
    print(player_distance)

    # 选择相似度 TOP5 的NBA球星数据
    similar_players = []
    for player_dist in player_distance:
        player, dist = player_dist
        # 球星姓名
        row = [player]
        # 特征值
        feat = player_group[player][columns].values.tolist()[0]
        row.extend(feat)
        # 相似度得分
        row.append(dist)
        similar_players.append(row)

    return jsonify(similar_players[:10])


if __name__ == "__main__":
    app.run(host='127.0.0.1')

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