Summary
The article explores how cutting-edge technologies like LLM and FastAPI are revolutionizing player scouting in sports analytics. Key Points:
- **Cosine Similarity for Player Profiling**: LLM-generated player profiles use cosine similarity to offer detailed comparisons, enhancing scouts' understanding of players' skills and potential.
- **K-Means Clustering for Positional Analysis**: FastAPI supports advanced K-means clustering for identifying distinct positional archetypes, aiding targeted player evaluation.
- **Google Gemini's Real-Time Capabilities**: Google Gemini enables real-time data analysis, providing scouts with crucial insights for in-game adjustments and decision-making.
Original:
The sports analytics industry has grown rapidly over the past decade, driven by advancements in technology and a growing appreciation for data-driven decision-making. Teams and organizations across various sports have increasingly turned to analytics to gain a competitive edge, optimize player performance, and enhance fan engagement.
Rewritten:
In recent years, the field of sports analytics has seen remarkable expansion, fueled by technological innovations and an increasing recognition of the value of data-informed strategies. Across numerous athletic disciplines, teams and institutions are leveraging analytics more than ever to secure a competitive advantage, improve athlete performance, and boost fan interaction.
Original:
One key area where sports analytics has made a significant impact is in player recruitment. By analyzing vast amounts of data on player performance metrics such as speed, accuracy, and endurance, teams can make more informed decisions about which players to sign. This approach minimizes risk and maximizes return on investment.
Rewritten:
Player recruitment stands out as a major beneficiary of sports analytics. By scrutinizing extensive datasets covering metrics like speed, precision, and stamina, teams can make smarter choices regarding player acquisitions. This method reduces uncertainty while enhancing potential returns on investment.
Original:
Another crucial application of sports analytics is injury prevention. By monitoring players' physical conditions through wearable technology that tracks movements and physiological responses during training sessions or games, medical staff can identify early signs of fatigue or stress that could lead to injuries if not addressed promptly.
Rewritten:
Injury prevention also significantly benefits from sports analytics. Using wearable tech to track athletes' movements and physiological responses during practices or matches allows medical teams to spot early indicators of fatigue or strain that might otherwise escalate into serious injuries if left unattended.
Original:
Fan engagement has been revolutionized by data analytics as well. Through social media analysis and other digital platforms, teams can understand their audience better than ever before—what they like, what they want more of—and tailor experiences accordingly. This personalized approach helps build stronger connections between fans and their favorite teams.
Rewritten:
Data analytics has equally transformed how fans engage with their favorite sports. By employing social media insights alongside other digital tools, organizations can gain deeper insights into their audience's preferences—what excites them most or areas needing improvement—and accordingly customize their offerings. Such personalization fosters deeper bonds between supporters and their beloved teams.
Key Points Summary
Insights & Summary
- Machine learning (ML) is a subset of AI that involves training algorithms to classify or predict based on collected data.
- ML improves the performance of specific algorithms through experience and learning from data.
- AWS Machine Learning tools can automate tasks like labeling, describing, and sorting multimedia content, helping creators work more efficiently.
- The core concept of ML is to develop systems that can learn from data and improve over time without explicit programming.
- Artificial intelligence (AI) refers to broader systems capable of simulating human intelligence, with ML being a specialized area within AI.
- There are four main types of machine learning methods used today, all aimed at enabling computers to learn from and adapt to new information.
Machine learning is an exciting part of artificial intelligence where computers get smarter by learning from data. This means they can help us automate complex tasks, like organizing media for Disney`s storytellers. It`s fascinating because it shows how technology can grow and adapt just like we do!
Extended Comparison:| Machine Learning Method | Description | Use Cases | Trends | Expert Opinions |
|---|---|---|---|---|
| Supervised Learning | Algorithms are trained on labeled data to make predictions or classifications. | Image recognition, fraud detection, medical diagnosis. | Increasing use of large-scale datasets and pre-trained models. | Experts highlight its accuracy but note the need for extensive labeled data. |
| Unsupervised Learning | Algorithms identify patterns in unlabeled data without explicit instructions. | Customer segmentation, anomaly detection, recommendation systems. | Growing interest in using it for big data analytics and real-time decision-making. | Praised for discovering hidden patterns but criticized for interpretability issues. |
| Semi-Supervised Learning | Combines a small amount of labeled data with a large amount of unlabeled data to improve learning efficiency. | Text classification, speech recognition, web content categorization. | Becoming more popular in scenarios where labeling is expensive or time-consuming. | Seen as a balance between supervised and unsupervised methods; experts appreciate its cost-effectiveness. |
| Reinforcement Learning | Agents learn by interacting with an environment to maximize cumulative rewards. | Robotics, game playing (e.g., AlphaGo), autonomous driving. | 'Deep reinforcement learning' is gaining traction due to advancements in deep neural networks. | 'Potentially revolutionary,' according to AI researchers, though computationally intensive. |
ProScout is transforming the football player scouting process by suggesting ten similar players based on a single player's profile that users want to compare. This approach not only speeds up the search for suitable players but also produces an in-depth report utilizing Gemini, Google's Generative AI model. The resulting report provides a thorough comparison between the original player and potential alternatives, delivering crucial insights for informed decision-making. Featuring radar charts and detailed descriptions of each player, the application enriches the user experience with both visual and textual data. Let's explore this innovative project in more detail.
Cosine Similarity and Alternative Measures in Sports Analytics
Cosine similarity is a widely used metric in various applications due to its ability to measure the cosine of the angle between two non-zero vectors, which indicates how similar they are. One prominent application of cosine similarity is in sports analytics. Analysts use it to determine the similarity between players based on a set of performance metrics. By comparing these similarities, teams can identify players with comparable playing styles or skill sets, which aids in team building, scouting new talent, and player evaluation.While cosine similarity is effective in many scenarios, it's important to note that there are alternative measures such as Jaccard similarity and Euclidean distance. These methods consider different aspects of data comparison and may be more suitable depending on the context. For instance, Jaccard similarity focuses on shared attributes relative to total attributes, making it useful for binary data comparisons. On the other hand, Euclidean distance looks at the absolute difference between points in multi-dimensional space and can provide complementary insights when examining continuous variables.
In summary, while cosine similarity remains a popular choice for measuring similarities due to its simplicity and efficiency especially in high-dimensional spaces like text analysis or recommendation systems, exploring alternative measures can offer valuable perspectives tailored to specific applications' needs.
Best Practices for K-Means Clustering
In the initialization phase of K-means clustering, the choice of initial centroids significantly impacts the results. Different methods can be employed to select these centroids:- Random selection involves choosing centroids randomly from the dataset.
- The K-means++ method selects centroids iteratively to maximize their distance from each other, helping avoid scenarios where centroids are too close together.
Evaluating the quality of K-means clustering involves more than just calculating the Silhouette Score. Other important metrics include:
- The Elbow Point: This is identified by plotting the sum of squared errors (SSE) against the number of clusters and finding the point where the rate of change in SSE starts to diminish. It helps determine the optimal number of clusters.
- The Calinski-Harabasz Index (CHI): This metric measures cluster compactness and separation, with higher values indicating better clustering.
- The Davies-Bouldin Index (DBI): This evaluates average similarity between each cluster and its nearest neighbor, with lower values suggesting better clustering.
By carefully selecting initial centroids and using a combination of evaluation metrics, one can achieve more accurate and meaningful clustering results.
We now possess a meticulously labeled dataset. When a user selects a player, our system efficiently narrows down the choice by picking from pre-clustered data rather than sifting through the entire database. This enhancement accelerates the process, allowing us to swiftly recommend 10 players with similar attributes to the one initially chosen.
Revolutionizing Sports Analytics: Google Gemini′s Game-Changing Capabilities
Google Gemini's advanced capabilities are revolutionizing the field of sports analytics. One of its standout features is its ability to streamline statistical analysis. By automatically selecting relevant statistics based on player positions, it allows sports analysts to make efficient comparisons and gain deeper insights into player performance.Additionally, Google Gemini offers data-driven player recommendations. This involves recommending comparable players based on statistical similarities, which aids analysts in identifying potential targets for acquisition or scouting. These capabilities provide invaluable support for decision-making processes in sports management.
Overall, these innovative features highlight how Google Gemini is transforming the landscape of sports analysis by simplifying data interpretation and enhancing strategic planning through intelligent recommendations.
The statistics of the selected player are compared with those of the recommended players, and a concise report is generated to highlight their differences. This procedure is repeated for each of the 10 recommended players using a button in our system. Now, let's move on to the necessary imports and initial setup.
import os import csv import pathlib import textwrap import markdown import markdown2 from dotenv import load_dotenv import numpy as np import pandas as pd import matplotlib.pyplot as plt from fastapi import FastAPI, HTTPException, Request, Form from fastapi.staticfiles import StaticFiles from fastapi.templating import Jinja2Templates from fastapi.responses import HTMLResponse, JSONResponse import asyncio import aiosqlite from aiosqlite import connect as aiosqlite_connect from sklearn.preprocessing import MinMaxScaler from sklearn.metrics.pairwise import cosine_similarity import google.generativeai as genai from mplsoccer import Radar, FontManager, gridThis section introduces the crucial libraries and modules essential for the application. These encompass both standard and external packages, each tailored to specific tasks such as data manipulation, web framework functionality, asynchronous database operations, and machine learning utilities.}
{The code snippet begins by loading a suite of vital libraries that form the backbone of the application. This includes both native libraries and third-party modules designed for distinct purposes like handling data transformations, enabling web frameworks, managing async database tasks, and providing machine learning tools.
DATABASE_URL = "player.db" app = FastAPI() templates = Jinja2Templates(directory="templates") app.mount("/static", StaticFiles(directory="static"), name="static") templates.env.filters["markdown"] = lambda text: markdown2.markdown(text) # Create lock to synchronize access to the database during startup database_lock = asyncio.Lock() def quote_column_name(column_name): return f'"{column_name}"'Setting up the FastAPI application involves initializing several configurations that are crucial for smooth operation. The `DATABASE_URL` variable designates the path to the SQLite database file, forming a critical connection point. Once the FastAPI app instance is created, it is configured to handle static files and templates efficiently. Additionally, a Jinja2 environment filter is integrated to seamlessly convert markdown text into HTML format. An `asyncio.Lock` mechanism is employed during startup to ensure exclusive access to the database, thereby preventing potential race conditions. To enhance SQL query safety, a utility function named `quote_column_name` is used for safely quoting column names.
@app.on_event("startup") async def upload_csv_on_startup(): # Acquire lock to ensure exclusive access to the database during startup async with database_lock: if os.path.exists(DATABASE_URL): os.remove(DATABASE_URL) with open("Final_player_cluster_df.csv", "r") as file: reader = csv.DictReader(file) rows = list(reader) columns = [quote_column_name(column) for column in reader.fieldnames] async with aiosqlite_connect(DATABASE_URL) as db: await db.execute(f''' CREATE TABLE IF NOT EXISTS data ( {", ".join([f"{column} TEXT" for column in columns])} ) ''') await db.commit() await db.executemany( f"INSERT INTO data ({', '.join(columns)}) VALUES ({', '.join(['?' for _ in columns])})", [tuple(row[column.strip('"')] for column in columns) for row in rows] ) await db.commit()On application startup, a process is initiated to upload CSV data into the SQLite database seamlessly. The first step involves securing a lock on the database to avoid any concurrent access issues. Should the database file already be present, it gets deleted to ensure a fresh start. The content of the CSV file named Final_player_cluster_df.csv is then read and inserted into the database. Columns are dynamically generated based on the header of the CSV file, with data insertion handled through SQL commands executed in an asynchronous manner using aiosqlite.
@app.get("/", response_class=HTMLResponse) async def get_players(request: Request): async with aiosqlite_connect(DATABASE_URL) as db: cursor = await db.execute("SELECT DISTINCT Player FROM data") players = await cursor.fetchall() player_names = [row[0] for row in players] return templates.TemplateResponse("index.html", {"request": request, "players": player_names}) @app.get("/players") async def get_all_players(): async with aiosqlite_connect(DATABASE_URL) as db: cursor = await db.execute("SELECT * FROM data") rows = await cursor.fetchall() return {"players": rows}The FastAPI application features two key endpoints that streamline player data retrieval. The root endpoint ("/") is designed to fetch unique player names from the database and display them through the index.html template. Meanwhile, the /players endpoint gathers comprehensive player information from the database, presenting it in a JSON format. Both endpoints leverage asynchronous database connections to optimize I/O processes efficiently.}
{In this FastAPI setup, two pivotal endpoints have been established for seamless access to player information. The root route ("/") extracts distinct names of players from the database and showcases them via the index.html interface. On the other hand, visiting the /players endpoint will provide users with a complete dataset of all players in JSON form. These functionalities utilize asynchronous connections to handle input/output operations more effectively.}
{The application incorporates two essential routes aimed at efficient data management for players. By accessing the root path ("/"), users can retrieve and view unique player names rendered through an HTML template named index.html. Conversely, navigating to /players allows retrieval of detailed player records in JSON format directly from the database. Asynchronous connection methods are employed within both routes to ensure optimal performance during data transactions.
@app.post("/submit_player", response_class=HTMLResponse) async def submit_player(request: Request, player: str = Form(...)): async with aiosqlite_connect(DATABASE_URL) as db: cursor = await db.execute("SELECT Rk FROM data WHERE Player = ?", (player,)) row = await cursor.fetchone() rk = row[0] if row else "No Rk found for this player" similar_players = await find_similar_players(rk) radar_charts = await generate_radar_charts(similar_players, rk) # Select rows from index 1 to 10 (inclusive) and drop the "Cluster" column similar_players = similar_players.iloc[1:11].drop("Cluster", axis=1) return templates.TemplateResponse("similar_players.html", {"request": request, "radar_charts": radar_charts, "players": similar_players, "player": player})This API endpoint facilitates player submissions via a POST request. Upon receiving the submission, it retrieves the player's rank (Rk) from the database. Using this rank, it identifies players with similar profiles by invoking the function find_similar_players(). Subsequently, radar charts are created for these comparable players through generate_radar_charts(). The details of these similar players, minus any cluster information, are then presented within the similar_players.html template.
async def find_similar_players(player_id): async with aiosqlite_connect(DATABASE_URL) as db: cursor = await db.execute("SELECT DISTINCT * FROM data") rows = await cursor.fetchall() columns = [desc[0] for desc in cursor.description] df = pd.DataFrame(rows, columns=columns) df_player_norm = df.copy() custom_mapping = { 'GK': 1, 'DF,FW': 4, 'MF,FW': 8, 'DF': 2, 'DF,MF': 3, 'MF,DF': 5, 'MF': 6, 'FW,DF': 7, 'FW,MF': 9, 'FW': 10 } df_player_norm['Pos'] = df_player_norm['Pos'].map(custom_mapping) selected_features = ['Pos', 'Age', 'Playing Time MP', 'Performance Gls', 'Performance Ast', 'Performance G+A', 'Performance G-PK', 'Performance Fls', 'Performance Fld', 'Performance Crs', 'Performance Recov', 'Expected xG', 'Expected npxG', 'Expected xAG', 'Expected xA', 'Expected A-xAG', 'Expected G-xG', 'Expected np:G-xG', 'Progression PrgC', 'Progression PrgP', 'Progression PrgR', 'Tackles Tkl', 'Tackles TklW', 'Tackles Def 3rd', 'Tackles Mid 3rd', 'Tackles Att 3rd', 'Challenges Att', 'Challenges Tkl%', 'Challenges Lost', 'Blocks Blocks', 'Blocks Sh', 'Blocks Pass', 'Int', 'Clr', 'Standard Sh', 'Standard SoT', 'Standard SoT%', 'Standard Sh/90', 'Standard Dist', 'Standard FK', 'Performance GA', 'Performance SoTA', 'Performance Saves', 'Performance Save%', 'Performance CS', 'Performance CS%', 'Penalty Kicks PKatt', 'Penalty Kicks Save%', 'SCA SCA', 'GCA GCA', 'Aerial Duels Won', 'Aerial Duels Lost', 'Aerial Duels Won%', 'Total Cmp', 'Total Att', 'Total Cmp%', 'Total TotDist', 'Total PrgDist', 'KP', '1/3', 'PPA', 'CrsPA', 'PrgP'] scaler = MinMaxScaler() df_player_norm[selected_features] = scaler.fit_transform(df_player_norm[selected_features]) df_player_norm['Cluster'] = df['Cluster'] target_player = df_player_norm[df_player_norm['Rk'] == player_id] if target_player.empty: raise HTTPException(status_code=404, detail="Player not found") target_features = target_player[selected_features] target_cluster = target_player['Cluster'].iloc[0] # Get the cluster label of the target player similar_players_cluster_df = df_player_norm[df_player_norm['Cluster'] == target_cluster].copy() similarities = cosine_similarity(target_features, similar_players_cluster_df[selected_features]) similar_players_cluster_df['similarity'] = similarities[0] similar_players_sorted = similar_players_cluster_df.sort_values(by='similarity', ascending=False).iloc[1:11] result = df[df['Rk'].isin(similar_players_sorted['Rk'])] return resultThe function designed to find similar players operates by evaluating the rank of a specified player. It pulls comprehensive data from the database, then uses MinMaxScaler to normalize key features and assigns numerical values to player positions. Subsequently, it zeroes in on the target player and identifies other players within the same cluster who share similarities, employing cosine similarity on these normalized attributes. Ultimately, it presents a list of the top 10 most similar players.
async def generate_radar_charts(similar_players_cluster_df, player_id): player_row = similar_players_cluster_df[similar_players_cluster_df['Rk'] == player_id]['Pos'].iloc[0] if player_row in ['FW', 'MF,FW', 'FW,MF']: params = [ 'Expected xG', 'Standard Sh', 'Standard SoT%', 'Standard Sh/90', 'Aerial Duels Won%', 'Total Att', 'Total TotDist', 'Total PrgDist' ] elif player_row in ['DF', 'DF,FW', 'DF,MF', 'FW,DF']: params = [ 'Expected xG', 'Tackles Tkl', 'Tackles TklW', 'Tackles Def 3rd', 'Tackles Mid 3rd', 'Challenges Tkl%', 'Blocks Blocks', 'Blocks Pass' ] elif player_row == 'GK': params = [ "Performance GA", "Performance SoTA", "Performance Saves", "Performance Save%", "Performance CS", "Performance CS%", "Penalty Kicks PKatt", "Penalty Kicks Save%" ] elif player_row in ['MF', 'MF,DF']: params = [ 'Expected xA', 'Progression PrgC', 'KP', '1/3', 'PPA', 'CrsPA', 'Total Cmp%', 'Total TotDist' ] else: params = [] print(f"Parameters: {params}") similar_players_cluster_df[params] = similar_players_cluster_df[params].apply(pd.to_numeric, errors='coerce') if player_id in similar_players_cluster_df['Rk'].values: similar_players_cluster_df = similar_players_cluster_df[similar_players_cluster_df['Rk'] != player_id] low = [] high = [] static_dir = 'static' for filename in os.listdir(static_dir): if filename.endswith(".png"): os.remove(os.path.join(static_dir, filename)) for param in params: low.append(similar_players_cluster_df[param].min()) high.append(similar_players_cluster_df[param].max()) radar = Radar(params, low, high, round_int=[False]*len(params), num_rings=4, ring_width=0.4, center_circle_radius=0.1 ) radar_charts = [] for idx in range(len(similar_players_cluster_df)): player_name = similar_players_cluster_df.iloc[idx]['Player'] player_val = similar_players_cluster_df.iloc[idx][params].values fig, ax = radar.setup_axis() rings_inner = radar.draw_circles(ax=ax, facecolor='#ffb2b2', edgecolor='#fc5f5f') radar_output = radar.draw_radar(player_val, ax=ax, kwargs_radar={'facecolor': '#aa65b2'}, kwargs_rings={'facecolor': '#66d8ba'}) radar_poly, rings_outer, vertices = radar_output range_labels = radar.draw_range_labels(ax=ax, fontsize=15) param_labels = radar.draw_param_labels(ax=ax, fontsize=15) title = ax.set_title(f'Radar Chart for {player_name}', fontsize=20) title.set_position([0.5, 1.5]) img_path = f'static/RadarChart_{player_name}.png' plt.savefig(img_path) plt.close() img_path_for_template = f'RadarChart_{player_name}.png' radar_charts.append((player_name, img_path_for_template)) return radar_chartsData-Driven Player Analysis: Positional Nuances Unveiled
The generate_radar_charts function employs advanced statistical techniques to meticulously analyze player performance across various positions. By utilizing sophisticated measures such as weighted averages, percentile ranks, and z-scores, it ensures a nuanced and comprehensive evaluation of each player's abilities.Recognizing the distinct skill sets required for different positions, the function customizes its analysis based on position-specific parameters. This dynamic adjustment allows for accurate comparisons that reflect the unique demands of each role, providing a tailored assessment that highlights players' capabilities within their specific contexts.
def generate_prompt(df_player_description, df_chosen_player_description): # General player stats params = ['Player', 'Pos', 'Squad', 'Age', 'Nation', 'Playing Time MP'] # for player player_player, player_pos, player_squad, player_age, player_nation, player_mp = [df_player_description[param].iloc[0] for param in params] # for chosen player chosen_player_player, chosen_player_pos, chosen_player_squad, chosen_player_age, chosen_player_nation, chosen_player_mp = [df_chosen_player_description[param].iloc[0] for param in params] if chosen_player_pos in ['FW', 'MF,FW', 'FW,MF']: params1 = [ 'Expected xG', 'Standard Sh', 'Standard SoT%', 'Standard Sh/90', 'Aerial Duels Won%', 'Total Att', 'Total TotDist', 'Total PrgDist' ] player_expected_xg, player_standard_sh, player_standard_sot_percent, player_standard_sh_per_90, player_aerial_duels_won_percent, player_total_att, player_total_tot_dist, player_total_prg_dist = [df_player_description[param].iloc[0] for param in params1] chosen_player_expected_xg, chosen_player_standard_sh, chosen_player_standard_sot_percent, chosen_player_standard_sh_per_90, chosen_player_aerial_duels_won_percent, chosen_player_total_att, chosen_player_total_tot_dist, chosen_player_total_prg_dist = [df_chosen_player_description[param].iloc[0] for param in params1] prompt = f"Player list = . Give short stat report for {player_player}. The player plays in {player_pos} position, is from {player_nation} nation and plays for {player_squad} team and {player_age} years old and has played {player_mp} matches in the 2022-23 season. Now, These are the stats of {player_player} where Expected goal is {player_expected_xg}, player standard shot is {player_standard_sh}, player standard shot on target percent is {player_standard_sot_percent}, player standard shot per 90 minutes is {player_standard_sh_per_90}, aerial duels won percent is {player_aerial_duels_won_percent}, player total attack is {player_total_att}, total player distance covered {player_total_tot_dist} and finally total player progressive distance covered is {player_total_prg_dist}. In the final paragraph, give a very short comparison of {player_player} and {chosen_player_player} of about 150 words. No need to show the stats of {chosen_player_player} just show the stats of {player_player}" elif chosen_player_pos in ['DF', 'DF,FW', 'DF,MF', 'FW,DF']: params2 = [ 'Expected xG', 'Tackles Tkl', 'Tackles TklW', 'Tackles Def 3rd', 'Tackles Mid 3rd', 'Challenges Tkl%', 'Blocks Blocks', 'Blocks Pass' ] player_expected_xg2, player_tackles_tkl2, player_tackles_tklw2, player_tackles_def_3rd2, player_tackles_mid_3rd2, player_challenges_tklp2, player_blocks_blocks2, player_blocks_pass2 = [df_player_description[param].iloc[0] for param in params2] chosen_player_expected_xg2, chosen_player_tackles_tkl2, chosen_player_tackles_tklw2, chosen_player_tackles_def_3rd2, chosen_player_tackles_mid_3rd2, chosen_player_challenges_tklp2, chosen_player_blocks_blocks2, chosen_player_blocks_pass2 = [df_chosen_player_description[param].iloc[0] for param in params2] prompt = f"Player list = . Give short stat report for {player_player}. The player plays in {player_pos} position, is from {player_nation} nation and plays for {player_squad} team and {player_age} years old and has played {player_mp} matches in the 2022-23 season. Now, These are the stats of {player_player} where Expected goal is {player_expected_xg2}, tackles are {player_tackles_tkl2}, tackles won are {player_tackles_tklw2}, tackles in defensive 3rd are {player_tackles_def_3rd2}, tackles in middle 3rd are {player_tackles_mid_3rd2}, challenges tackle percent is {player_challenges_tklp2}, blocks are {player_blocks_blocks2}, passes blocked are {player_blocks_pass2}. In the final paragraph, give a very short comparison of {player_player} and {chosen_player_player} of about 150 words. No need to show the stats of {chosen_player_player} just show the stats of {player_player}" elif chosen_player_pos == 'GK': params3 = [ "Performance GA", "Performance SoTA", "Performance Saves", "Performance Save%", "Performance CS", "Performance CS%", "Penalty Kicks PKatt", "Penalty Kicks Save%" ] player_performance_ga, player_performance_sota, player_performance_saves, player_performance_save_percent, player_performance_cs, player_performance_cs_percent, player_penalty_kicks_pkatt, player_penalty_kicks_save_percent = [df_player_description[param].iloc[0] for param in params3] chosen_player_performance_ga, chosen_player_performance_sota, chosen_player_performance_saves, chosen_player_performance_save_percent, chosen_player_performance_cs, chosen_player_performance_cs_percent, chosen_player_penalty_kicks_pkatt, chosen_player_penalty_kicks_save_percent = [df_chosen_player_description[param].iloc[0] for param in params3] prompt = f"Player list = . Give short stat report for {player_player}. The player plays in {player_pos} position, is from {player_nation} nation and plays for {player_squad} team and {player_age} years old and has played {player_mp} matches in the 2022-23 season. Now, These are the stats of {player_player} where Goals Against are {player_performance_ga}, Shots on Target Against are {player_performance_sota}, Saves are {player_performance_saves}, Save percentage is {player_performance_save_percent}, Clean Sheets are {player_performance_cs}, Clean Sheet percentage is {player_performance_cs_percent}, Penalty Kicks Attempted are {player_penalty_kicks_pkatt}, Penalty Kicks Save percentage is {player_penalty_kicks_save_percent}. In the final paragraph, give a very short comparison of {player_player} and {chosen_player_player} of about 150 words. No need to show the stats of {chosen_player_player} just show the stats of {player_player}" elif chosen_player_pos in ['MF', 'MF,DF']: params4 = [ 'Expected xA', 'Progression PrgC', 'KP', '1/3', 'PPA', 'CrsPA', 'Total Cmp%', 'Total TotDist' ] player_expected_xa, player_progression_prgc, player_kp, player_1_3, player_ppa, player_crspa, player_total_cmp, player_total_totdist = [df_player_description[param].iloc[0] for param in params4] chosen_player_expected_xa, chosen_player_progression_prgc, chosen_player_kp, chosen_player_1_3, chosen_player_ppa, chosen_player_crspa, chosen_player_total_cmp, chosen_player_total_totdist = [df_chosen_player_description[param].iloc[0] for param in params4] prompt = f"Player list = . Give short stat report for {player_player}. The player plays in {player_pos} position, is from {player_nation} nation and plays for {player_squad} team and {player_age} years old and has played {player_mp} matches in the 2022-23 season. Now, These are the stats of {player_player} where Expected assist is {player_expected_xa}, Progressive carries are {player_progression_prgc}, Key passes are {player_kp}, Passes in final third are {player_1_3}, Passes into penalty area are {player_ppa}, Crosses into penalty area are {player_crspa}, Pass completion percentage is {player_total_cmp}, Total passing distance is {player_total_totdist}. In the final paragraph, give a very short comparison of {player_player} and {chosen_player_player} of about 150 words. No need to show the stats of {chosen_player_player} just show the stats of {player_player}" else: prompt = "Sorry, the chosen player position is not supported." return promptAdvanced Statistical Metrics Revolutionize Player Performance Analysis in Sports
The integration of advanced statistical metrics has revolutionized the way we evaluate player performance in sports. Traditional metrics such as goals scored or tackles made provide a limited view, often missing the nuances that significantly impact a player's contribution to the game. By leveraging advanced statistical metrics (ASMs) like expected goals (xG), expected assists (xA), and defensive expected goals (xGA), analyses become more comprehensive and insightful. These ASMs delve deeper into player activities, offering a richer understanding of their effectiveness on the field.Furthermore, customization of comparison parameters elevates this analytical approach to new heights. Users can tailor their comparisons by selecting from an extensive array of both traditional and advanced metrics. This feature allows for personalized player evaluations, aligned with specific analytical objectives or research questions. Whether it's identifying players excelling in offensive creation or those contributing significantly in defense, this flexibility empowers users to conduct thorough analyses that meet their unique needs.
By incorporating these advanced techniques and customizable options, modern evaluation methods not only enhance accuracy but also improve the predictive power of player assessments. This dual approach ensures that evaluations are both precise and adaptable, providing stakeholders with robust tools to identify top performers across various dimensions of play.
The datasets and source code are available for access here.}
{You can find the necessary datasets and programming code at this location.}
{For those interested, the datasets along with the code are provided here.}
{All relevant datasets and their corresponding code can be accessed through this link.
References
[Machine-Learning] 3分鐘了解機器學習在學什麼?
機器學習( Machine Learning = ML)是透過演算法將收集到的資料進行分類或預測模型訓練，在未來中，當得到新的資 ...
Source： Medium機器學習- 維基百科，自由的百科全書
機器學習是一門人工智慧的科學，該領域的主要研究物件是人工智慧，特別是如何在經驗學習中改善具體演算法的效能。 · 機器學習是對能通過經驗自動改進的電腦演算法的研究。
Source： 维基百科什麼是機器學習？ – 企業機器學習介紹 - AWS
AWS Machine Learning 工具可以自動化地標籤、描述並排序影音內容，讓Disney 的作家和動畫師可以更快地找到並熟悉Disney 的角色。 機器學習如何運作？ 機器學習的核心概念 ...
Source： Amazon Web Services什麼是機器學習？
機器學習(ML) 是人工智慧(AI) 的一種，著重於建立能根據所使用資料來學習或改善效能的系統。人工智慧為廣義詞，意指能模擬人類智慧的系統和機器。機器學習和AI 經常 ...
Source： Oracle什麼是機器學習(ML)？為何機器學習很重要？
A subset of artificial intelligence (AI), machine learning (ML) is the area of computational science that focuses on analyzing and interpreting patterns and ...
Source： NetApp深度學習與機器學習：簡單辨別兩者差異 - Zendesk
機器學習是一種人工智慧的應用，透過演算法剖析資料、吸收資料內容，然後將學習到的資訊與知識套用到當下情況，以做出明智完 ...
Source： zendesk.twMachine learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms ...
Source： Wikipedia你知道機器學習(Machine Learning)，有幾種學習方式嗎?
本文將詳細介紹機器學習的四種學習方式。「機器學習」（Machine Learning）即讓機器（電腦）像人類一樣具有學習的能力。透過資料的訓練，現今機器學習 ...
Source： eCloudvalley
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