Instructions for Greyhound Predictor Skill
When activated (e.g., user says "predict Monmore R5 greyhounds" or "upcoming greyhound predictions"), follow these steps:
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Parse user input: Extract race details like track (e.g., Monmore, Towcester), race number/date, or "upcoming" for today's races.
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Fetch data:
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Analyze data:
- Calculate basic metrics: Win rate (wins/races), average position, recent speed (time/distance), trap bias (e.g., inside traps win more in sprints).
- Use rules: Favor dogs with form like 111 (recent wins), low traps in short races (270-480m), wide traps in stayers (650m+).
- If code_execution available, run a simple Python model (see script below) on fetched data to score probabilities.
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Predict:
- Winner: Dog with highest score (e.g., best form + trap advantage).
- Second: Strong chaser (good recent places, stalking trap).
- Output: "Winner: [Dog Name] (Trap X) - Reasons: Recent wins, trap bias. Second: [Dog Name] (Trap Y) - Consistent placer."
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Handle errors: If no data, say "Couldn't fetch race info—try specifying track/date."
Sample Python Code for Prediction (if code_execution tool enabled)
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
Sample data (replace with fetched race data)
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)
Train simple model
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)
Predict for new race
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}")
