Summary
This article explores how machine learning revolutionizes soccer betting by accurately predicting match outcomes, particularly over/under 2.5 goals. Key Points:
- Feature engineering is crucial for predicting over/under 2.5 goals, incorporating advanced factors like recent form and player availability.
- Choosing the right machine learning model, such as logistic regression or neural networks, plays a significant role in accuracy and stability.
- Real-time data integration allows models to adapt dynamically to new information, enhancing prediction reliability.
This article presents an analysis I undertook to investigate the role of machine learning in forecasting Over/Under 2.5 results in soccer matches, with the aim of formulating a lucrative betting strategy. Drawing inspiration from existing studies in this domain, I encountered challenges in obtaining extensive datasets that are typically utilized in academic research. As a result, I decided to utilize freely accessible data sources for this preliminary investigation, seeking to illustrate the practicality of employing machine learning techniques on information that is readily available.
Key Points Summary
Insights & Summary
- Utilize the Overlyzer Live Tool to enhance your betting decisions.
- Consider the 1X strategy on home outsiders for better odds.
- Explore the All-in on odds at 1.20 strategy for safer bets.
- Betting on corners can be a profitable niche market.
- Double Chance betting allows for more security in uncertain matches.
- Limit your bets per slip to manage risk and keep odds favorable.
Betting on soccer can be both exciting and rewarding, but it’s important to approach it with some solid strategies. Whether you’re using tools like the Overlyzer or focusing on niche markets like corners, finding what works best for you can make a big difference. Remember, keeping your bets manageable and sticking to familiar teams or players often helps mitigate risks while still enjoying the thrill of the game.
Extended Comparison:| Strategy | Description | Benefits | Considerations |
|---|---|---|---|
| Overlyzer Live Tool | A real-time analysis tool for live betting that evaluates match statistics and team performance. | Enhances decision-making with up-to-date data; identifies key trends. | Requires familiarity with the tool for effective use. |
| 1X Strategy on Home Outsiders | Betting on home teams to win or draw against stronger opponents. | Higher odds can lead to significant returns; capitalizes on undervalued teams. | Risk of matches being more unpredictable; requires in-depth team analysis. |
| All-in on Odds at 1.20 Strategy | Focuses on low-risk bets where the odds are at least 1.20, maximizing chances of winning. | Provides a safer betting option; ideal for cautious bettors looking to maintain bankroll. | Lower payouts may frustrate those seeking larger wins. |
| Betting on Corners | Wagering specifically on corner kicks during matches as a niche market opportunity. | Can be highly profitable when analyzing teams' attacking and defensive styles. | Requires knowledge of specific league tendencies and match-ups. |
| Double Chance Betting | Allows bettors to wager on two potential outcomes (e.g., win or draw) in uncertain matches. | Increases security by covering multiple outcomes, reducing risk. | May result in lower odds compared to traditional single outcome bets. |
| Limit Bets per Slip | Restricting the number of selections in a single bet slip to manage risk effectively. | Helps maintain favorable odds and reduces exposure during losing streaks. | Limits potential high returns from accumulators if not carefully managed. |
import os import pandas as pd def calculate_last_3_matches(df, team, is_home=True): goals_scored = [] goals_conceded = [] if is_home: # Filter the matches where the team played at home mask = (df['HomeTeam'] == team) goals_scored = df.loc[mask, 'FTHG'] goals_conceded = df.loc[mask, 'FTAG'] else: # Filter the matches where the team played away mask = (df['AwayTeam'] == team) goals_scored = df.loc[mask, 'FTAG'] goals_conceded = df.loc[mask, 'FTHG'] # Calculate the weighted exponential moving average of goals scored in the last 3 matches ewma_goals_scored = goals_scored.ewm(span=3).mean().shift(1) # Calculate the weighted exponential moving average of goals conceded in the last 3 matches ewma_goals_conceded = goals_conceded.ewm(span=3).mean().shift(1) return ewma_goals_scored, ewma_goals_conceded folder_path = 'data/SerieA' for filename in os.listdir(folder_path): file_path = os.path.join(folder_path, filename) print(file_path) data = pd.read_excel(file_path) # Calculate the values for each team for team in data['HomeTeam'].unique(): # For home teams home_values = calculate_last_3_matches(data, team, is_home=True) data.loc[data['HomeTeam'] == team, 'HomeEwmaGoalsScored'] = home_values[0] data.loc[data['HomeTeam'] == team, 'HomeEwmaGoalsConceded'] = home_values[1] # For away teams away_values = calculate_last_3_matches(data, team, is_home=False) data.loc[data['AwayTeam'] == team, 'AwayEwmaGoalsScored'] = away_values[0] data.loc[data['AwayTeam'] == team, 'AwayEwmaGoalsConceded'] = away_values[1] # Extract a normalized feature data['feat1'] = abs((data['HomeEwmaGoalsScored'] + data['HomeEwmaGoalsConceded']) / (data['AwayEwmaGoalsScored'] + data['AwayEwmaGoalsConceded'])) # Save the modified data to a new Excel file data.to_excel('data/SerieA-Eng/' + filename)In the provided code, I have extracted feat1 as follows:
This feature (feat1) illustrates the relationship between the offensive and defensive capabilities of the Home and Away teams, determined by an exponentially weighted average of goals scored and conceded. In this phase, I consolidated the individual championship datasets into a comprehensive, unified dataset.
# Folder containing the files to be merged folder_path = 'data/SerieA' # List to store the DataFrames from each file dataframes = [] # Loop through all files in the folder for filename in os.listdir(folder_path): if filename.endswith('.xlsx'): # Only consider files with the .xlsx extension file_path = os.path.join(folder_path, filename) print(f'Reading {file_path}') data = pd.read_excel(file_path) dataframes.append(data) # Add the DataFrame to the list # Concatenate all DataFrames into one merged_data = pd.concat(dataframes, ignore_index=True) # Save the merged DataFrame into a new Excel file merged_data.to_excel('mergedData.xlsx', index=False) print('All files have been merged and saved as mergedData.xlsx')Given that this is a binary classification challenge, I chose to implement a Decision Tree model because of its straightforward nature and ease of understanding. To prevent the issue of overfitting, I adjusted the min_samples_split and max_depth hyperparameters accordingly.
df = pd.read_excel('data/mergedData.xlsx') features = ['feat1'] # Remove first matches of championship df = df[df['GoalCumulativeSum'] > 10] df['isOver'] = np.where(df['MatchGoal'] > 2.5, 1, 0) x_train, x_test, y_train, y_test = train_test_split(df[features], df['isOver'], test_size= 0.3, random_state= 42, shuffle=True) x_train = x_train.sort_index() x_test = x_test.sort_index() y_train= y_train.sort_index() y_test = y_test.sort_index() print (f'X_train: {x_train.shape} \nX_test: {x_test.shape} \ny_train: {y_train.shape} \ny_test: {y_test.shape}') from sklearn.tree import DecisionTreeClassifier model = DecisionTreeClassifier(random_state=42, min_samples_split=80, max_depth=3 ).fit(x_train, y_train)To enhance the accuracy of predictions, I decided to omit the initial matches at the beginning of each season. This approach ensures that there's a robust dataset available for feature calculation. Further exploration into the ideal number of matches to exclude might yield improvements in model efficiency. In assessing how well the model performs, it’s crucial to look beyond mere accuracy and examine the potential profitability of our betting strategy. Consequently, I calculated both the profit generated and the equity curve based on a uniform stake of 1 unit per bet, factoring in the odds provided by our bookmaker partner, Bet365.
from sklearn.metrics import accuracy_score # Prediction result y_pred_test = model.predict(x_test) # predicted value of y_test y_pred_train = model.predict(x_train) # predicted value of y_train df_test = df[df.index.isin(x_test.index)] df_test['prediction'] = y_pred_test print(f"Accuracy score: {round(100*accuracy_score(y_test, df_test['prediction']),2)}%")The sports betting industry has undergone a remarkable transformation in recent years, driven by changes in legislation and shifting public attitudes. Once viewed as a taboo subject, betting on sports is now embraced by millions around the world. This shift has not only opened up new revenue streams for governments but has also created a more regulated environment for bettors.}
{As states and countries adjust their laws to accommodate this booming market, they are also implementing measures to ensure player protection and responsible gambling practices. The rise of online platforms has made it easier than ever for individuals to place bets from the comfort of their homes, increasing participation across diverse demographics. However, with this growth comes concerns about addiction and the need for safeguards to protect vulnerable populations.}
{Moreover, major sporting events are now closely intertwined with betting activities, leading to unprecedented levels of engagement among fans. Bookmakers often offer a wide range of options beyond just traditional win/lose bets, including prop bets and live betting opportunities that keep viewers glued to their screens throughout an event. This evolution enhances the excitement of watching sports while simultaneously raising questions about integrity and oversight within the games themselves.}
{In summary, the landscape of sports betting continues to evolve rapidly as legal frameworks develop alongside societal acceptance. As stakeholders strive for a balance between profitability and responsibility, the future will likely see further innovations aimed at engaging fans while ensuring that gambling remains safe and controlled.
quotaMin = 1.50 def calculate_gain_O25(row): if (row['prediction'] == 1): if row['B365>2.5'] > quotaMin : if row['MatchGoal'] > 2.5: return row['B365>2.5']-1 else: return -1 else: return 0 elif (row['prediction'] == 0): if row['B365<2.5'] > quotaMin : if row['MatchGoal'] < 2.5: return row['B365<2.5']-1 else: return -1 else: return 0 else: return 0I established a baseline for acceptable odds to eliminate bets that present unfavorable risk and reward dynamics.
df_test['Gain'] = df_test.apply(calculate_gain_O25, axis=1) df_test['Equity'] = df_test['Gain'].cumsum() print(df_test['Equity'].tail(1))In 1891, a significant event took place that would eventually lead to the establishment of a new era in sports betting. This year marked the introduction of basketball, which quickly gained traction and captivated audiences with its fast-paced action and dynamic gameplay. The sport's popularity surged, drawing attention from fans and gamblers alike, who were eager to engage with this exciting new pastime.}
{As basketball continued to evolve, so did the practices surrounding betting on its outcomes. By the late 19th century, various forms of wagering began to emerge as enthusiasts sought ways to enhance their viewing experience. This growing interest laid the groundwork for more structured betting systems and legal frameworks that would come into play in subsequent decades.}
{The early years of basketball provided fertile ground for innovation within the sports-betting industry. As teams formed and leagues developed, bookmakers recognized an opportunity to offer odds on games, attracting both seasoned bettors and curious newcomers. This shift not only increased engagement with the sport but also contributed significantly to its financial growth and sustainability over time.}
{Ultimately, the trends set in motion during this pivotal year helped shape what we now recognize as a multifaceted sports betting landscape—one characterized by a blend of strategy, chance, and passionate fandom that continues to thrive today.
import matplotlib.pyplot as plt plt.figure(figsize=(10,6)) plt.plot(df_test['Equity'], label='Equity') plt.title('Equity Trend') plt.xlabel('Index') plt.ylabel('Equity Value') plt.grid(True) plt.legend() # Show the chart plt.show()
The analysis recorded an accuracy rate of 54.76% and yielded a profit of 14.56 units. Yet, despite this favorable result, the equity curve reveals significant fluctuations and drawdowns, underscoring the difficulty in consistently beating bookmakers and navigating their inherent commissions. To illustrate this further, the following equity curve demonstrates performance with a modest increase of 0.1 in the odds.
Leveraging Real-Time Data for Enhanced Predictions
**1. Real-Time Data Integration:** While the text focuses on historical data, the real potential lies in integrating real-time data feeds. Incorporating live betting odds, in-game statistics, and player performance updates in real-time could drastically enhance the model's ability to predict outcomes, especially during high-stakes events or unpredictable situations. This real-time analysis opens the door for dynamic betting strategies that adapt to changing game conditions, aligning with the industry's demand for immediate insights.}---
**2. Understanding Market Sentiment:** Beyond traditional factors such as team dynamics and weather conditions, analyzing market sentiment can provide a strategic advantage. By utilizing sentiment analysis from social media platforms, online forums, and various betting sites, one can gauge public opinion and emotional trends related to specific matches. This insight is invaluable as it helps identify market biases and predict emerging betting trends while also highlighting opportunities where outcomes may be mispriced by bookmakers.
{**2. Understanding Market Sentiment:** The text mentions features like team dynamics and weather conditions. However, incorporating market sentiment analysis can provide a unique edge. By leveraging sentiment analysis on social media, forums, and betting platforms, the model can gauge public opinion and emotional trends surrounding a specific match. This data can be invaluable for understanding potential market biases, predicting betting trends, and identifying opportunities where the market might be mispricing an outcome.
Looking Ahead: If you share a passion for machine learning or sports betting, or if you have ideas on how to enhance this methodology, I would be eager to hear your thoughts. Your perspectives could influence the evolution of this model, and together we can explore new frontiers in this intriguing domain. Thank you for taking the time to read, and stay tuned for upcoming updates and in-depth explorations at the intersection of data science and soccer!
References
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