Analyzes greyhound races, fetches data, and predicts winners/placings for upcoming races based on form, odds, and simple models.
When activated (e.g., user says "predict Monmore R5 greyhounds" or "upcoming greyhound predictions"), follow these steps:
Parse user input: Extract race details like track (e.g., Monmore, Towcester), race number/date, or "upcoming" for today's races.
Fetch data:
Analyze data:
Predict:
Handle errors: If no data, say "Couldn't fetch race info—try specifying track/date."
If your OpenClaw supports code_execution, include this in instructions to run a basic model. Paste data into a DataFrame.
import pandas as pd from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler
data = pd.DataFrame({ 'trap': [1, 2, 3, 4, 5, 6], 'win_rate': [0.4, 0.3, 0.35, 0.2, 0.45, 0.25], # Wins/races 'avg_position': [2.1, 3.0, 2.5, 4.0, 1.8, 3.5], 'recent_form_score': [0.8, 0.6, 0.7, 0.4, 0.9, 0.5] # Custom score from form }) data['winner'] = [1, 0, 0, 0, 0, 0] # Dummy target for training (use historic data)
X = data[['trap', 'win_rate', 'avg_position', 'recent_form_score']] y = data['winner'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) scaler = StandardScaler().fit(X_train) X_train = scaler.transform(X_train) model = LogisticRegression().fit(X_train, y_train)
new_data = pd.DataFrame(...) # Fill with fetched data preds = model.predict_proba(scaler.transform(new_data))[:, 1] top_dog = new_data.iloc[preds.argmax()]['dog_name'] print(f"Predicted winner: {top_dog}")
ZIP package — ready to use