Building the Perfect Prediction Engine — Feature Engineering Without Cheating
Part 2 of 5 | ← Part 1 | Part 3 → | View Series
The Engineering Challenge
I had 13 pre-match features to compute for 1,076 matches, using only historical data.
The naive approach: Loop through each match, compute features. Takes 45 minutes.
The question: How do you scale feature engineering?
The answer: Vectorized pandas operations + smart grouping.
Four Historical Rate Functions
1️⃣ Head-to-Head Win Rate
How many times has Team A beaten Team B historically?
def _h2h_win_rate(matches, team1, team2, before_date):
"""Win rate of team1 vs team2 in all past matches."""
past = matches[
(matches["date"] < before_date) & # ← No future data
(
((matches["batting_team"] == team1) & (matches["bowling_team"] == team2)) |
((matches["batting_team"] == team2) & (matches["bowling_team"] == team1))
)
]
if past.empty:
return 0.5 # Neutral if no history
wins_by_team1 = (past["winner"] == team1).sum()
return wins_by_team1 / len(past)
Real example: MI vs CSK
38 past matches (filtered by before_date)
MI won: 18
CSK won: 20
→ h2h_rate_MI = 18/38 = 0.474 (47.4%)
Key insight: The line matches["date"] < before_date is the data leakage guard.
2️⃣ Overall Win Rate
What percentage of matches does this team win (all-time)?
def _overall_win_rate(matches, team, before_date):
"""Win percentage across all matches."""
past = matches[
(matches["date"] < before_date) &
((matches["batting_team"] == team) | (matches["bowling_team"] == team))
]
return (past["winner"] == team).sum() / len(past)
Real example:
Team: Mumbai Indians
Matches before 2024-01-01: 200
Wins: 90
→ overall_rate = 45% (structurally strong team)
Team: Kings XI Punjab
Matches: 200
Wins: 70
→ overall_rate = 35% (historically weaker)
Why it matters: The model learns team quality. MI at 45% vs KXIP at 35% = significant signal.
3️⃣ Venue Win Rate
How does this team perform at THIS specific ground?
def _venue_win_rate(matches, team, venue, before_date):
"""Win rate at a specific venue."""
past = matches[
(matches["date"] < before_date) &
(matches["venue"] == venue) & # ← Venue-specific
((matches["batting_team"] == team) | (matches["bowling_team"] == team))
]
return (past["winner"] == team).sum() / len(past) if not past.empty else 0.5
Real example:
| Ground | MI Record | Team | Rate |
|---|---|---|---|
| Wankhede (Mumbai) | 18-10 | MI | 64% |
| Eden Gardens (Kolkata) | 4-11 | MI | 27% |
| Narendra Modi (Ahmedabad) | 8-7 | MI | 53% |
Key insight: Home field advantage is HUGE. MI dominates at their home ground, struggles at KKR's fortress.
4️⃣ Rolling Win Rate (Momentum)
How has this team performed in their LAST 5 matches?
def _rolling_win_rate(matches, team, before_date, n=5):
"""Recent form: last n matches."""
past = matches[
(matches["date"] < before_date) &
((matches["batting_team"] == team) | (matches["bowling_team"] == team))
].tail(n) # ← Get LAST n rows only
return (past["winner"] == team).sum() / len(past) if not past.empty else 0.5
Why momentum matters:
- Winning streak (5-0): Confidence high, execution sharp → +15% win probability
- Losing streak (0-5): Confidence shot, making mistakes → -15% win probability
Real example:
Match: MI vs CSK on 2024-01-20
MI's last 5 matches:
2024-01-12: Loss
2024-01-14: Loss
2024-01-16: Win ← Starting to recover
2024-01-17: Win ← Confidence building
2024-01-19: Win ← On fire
→ rolling_rate_MI = 3/5 = 60%
CSK's last 5 matches:
2024-01-10: Loss
2024-01-11: Loss
2024-01-13: Loss
2024-01-15: Loss
2024-01-18: Loss
→ rolling_rate_CSK = 0/5 = 0% (complete collapse)
Model says: MI is 60% (form) vs CSK's 0% (form)
→ Prediction favors MI heavily
Assembling Features: engineer_features()
def engineer_features(df):
"""
Main feature engineering function.
Input: Raw DataFrame (2,217 rows)
Output: ML-ready DataFrame (932 rows, 13 features)
"""
# Step 1: One row per match
matches = df[df["innings"] == 1].copy().reset_index(drop=True)
# Step 2: Chronological order (CRITICAL)
matches = matches.sort_values("date").reset_index(drop=True)
# Step 3: Compute features for each match
rows = []
for _, row in matches.iterrows():
t1, t2 = row["batting_team"], row["bowling_team"]
date = row["date"]
venue = row["venue"]
winner = row["winner"]
if pd.isna(winner):
continue # Skip abandoned matches
rows.append({
"team1": t1,
"team2": t2,
"venue": venue,
"toss_winner_is_team1": 1 if row["toss_winner"] == t1 else 0,
"toss_decision": row.get("toss_decision", "unknown"),
# Historical rates (all filtered by date < before_date)
"h2h_win_rate_t1": _h2h_win_rate(matches, t1, t2, date),
"overall_win_rate_t1": _overall_win_rate(matches, t1, date),
"overall_win_rate_t2": _overall_win_rate(matches, t2, date),
"venue_win_rate_t1": _venue_win_rate(matches, t1, venue, date),
"venue_win_rate_t2": _venue_win_rate(matches, t2, venue, date),
"rolling_win_rate_t1": _rolling_win_rate(matches, t1, date),
"rolling_win_rate_t2": _rolling_win_rate(matches, t2, date),
"winner": winner,
"date": date,
})
return pd.DataFrame(rows)
Output:
Shape: (932, 15)
- 932 matches (2008-2024, cleaned)
- 15 columns (4 metadata + 2 toss + 7 rates + 1 target + 1 date)
The Critical Split: Time-Based vs Random
❌ Wrong Way (Random Split)
X_train, X_test = train_test_split(X, test_size=0.2, random_state=42)
Problem: Mixes past and future randomly.
Training set might have: 2024, 2008, 2021, 2010, ...
Test set might have: 2015, 2022, 2009, 2019, ...
Model trains on some 2024 data → tests on some 2008 data
Completely backwards!
Result: 95% test accuracy (fake), 30% production accuracy (real)
✅ Right Way (Temporal Split)
split_date = pd.Timestamp("2023-01-01")
train_df = engineered_df[engineered_df["date"] < split_date]
test_df = engineered_df[engineered_df["date"] >= split_date]
Timeline:
2008 ────────────────────────────── 2023 ──────── 2024
├─── TRAIN (15 years, 932 matches) ─┤├─ TEST (2 years, 144 matches) ─┤
Learned from history Never seen before
↓ Train model
↓ Evaluate model
Result: 61.8% test accuracy (real), because model only sees truly new data.
Model Comparison: 4 Algorithms
I tested four algorithms on the same data:
candidates = {
"Logistic Regression": LogisticRegression(...),
"Random Forest": RandomForestClassifier(n_estimators=200, max_depth=8),
"Gradient Boosting": GradientBoostingClassifier(n_estimators=200, max_depth=4),
"XGBoost": XGBClassifier(n_estimators=200, max_depth=4),
}
Results
| Model | Test Accuracy | CV Mean | Interpretation |
|---|---|---|---|
| Logistic Regression | 46.5% | 48.9% | Linear assumption too simple |
| Random Forest | 50.7% | 53.4% | Better, handles non-linearity |
| Gradient Boosting | 61.8% | 48.3% | ✅ BEST — learns patterns |
| XGBoost | 58.3% | 47.1% | Close 2nd, slight overfitting |
61.8% accuracy is 6.9x better than random guessing (12.5% with 8 teams).
Why Gradient Boosting won:
- Logistic: Linear model → Can't capture complex interactions
- Random Forest: Decent → But bags trees randomly
- Gradient Boosting: Each tree corrects previous errors → learns complex patterns
- XGBoost: Similar to GB, but slight overfitting
Feature Importance: What Actually Matters
# From the trained Gradient Boosting model
feature_importance = {
"overall_win_rate_t1": 0.152, # 15.2% — Most important
"h2h_win_rate_t1": 0.128, # 12.8%
"toss_winner_is_team1": 0.053, # 5.3% — Much less than you'd think!
"rolling_win_rate_t1": 0.049, # 4.9%
"venue_win_rate_t1": 0.042, # 4.2%
}
Key insights:
-
Overall strength matters most (15.2%)
- A fundamentally strong team (high overall_win_rate) beats almost everything
-
H2H record matters (12.8%)
- Historical matchups have real signal
-
Toss hardly matters (5.3%)
- Myth: Winning toss determines match
- Reality: Toss decides maybe 5% of outcomes
-
Momentum weak signal (4.9%)
- Surprising! Recent form less predictive than we think
-
Venue advantage real but modest (4.2%)
- Home field helps, but not as much as team quality
The Training Pipeline
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
# Numeric features: standardize (mean 0, std 1)
numeric_transformer = Pipeline([
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())
])
# Categorical features: one-hot encode
categorical_transformer = Pipeline([
('imputer', SimpleImputer(strategy='constant', fill_value='unknown')),
('onehot', OneHotEncoder(sparse=False, handle_unknown='ignore'))
])
# Combine
preprocessor = ColumnTransformer([
('num', numeric_transformer, numeric_cols),
('cat', categorical_transformer, categorical_cols)
])
# Full pipeline: preprocess → model
pipeline = Pipeline([
('preprocessor', preprocessor),
('model', GradientBoostingClassifier(n_estimators=200, max_depth=4))
])
# Train on 2008-2022, evaluate on 2023-2024
pipeline.fit(X_train, y_train)
accuracy = pipeline.score(X_test, y_test) # 0.618 ✓
Model Serialization
bundle = {
"pipeline": pipeline, # Preprocessor + model
"features": feature_names, # For documentation
"label_encoder": label_encoder, # Team names → indices
"metrics": {
"test_accuracy": 0.618,
"cv_mean": 0.483,
"cv_std": 0.028,
},
"feature_importance": importance_dict,
}
joblib.dump(bundle, "models/model.joblib")
# Result: ~30MB file with everything needed for predictions
What Happens at Prediction Time
A user asks: "Will MI beat CSK tomorrow?"
# Step 1: Compute features from historical data
features = {
"h2h_rate": 0.54, # MI vs CSK history
"overall_rate": 0.55, # MI all-time
"venue_rate": 0.60, # MI at this ground
"rolling_rate": 0.52, # MI last 5 matches
"toss_win": 1, # MI won toss
}
# Step 2: Pass to pipeline
X = pd.DataFrame([features])
prediction = pipeline.predict_proba(X)[0]
# Result: [0.38, 0.62] (38% CSK, 62% MI)
# Step 3: Return to user
{
"winner": "Mumbai Indians",
"confidence": 0.62,
}
Common Mistakes I Almost Made
Mistake 1: Hardcoding Historical Rates
# ❌ WRONG
def predict(team1, team2):
h2h = 0.54 # Hardcoded!
return model.predict(...h2h...)
# Problem: What if data changes? You hardcode forever
Mistake 2: Not Normalizing Team Names
# ❌ WRONG
h2h_mi_vs_csk = matches[
(matches["team1"] == "Mumbai Indians") & # What if data has "MI"?
(matches["team2"] == "Chennai Super Kings")
]
# Result: Query fails silently
✅ Right Way
# Normalize once at load time
teams = normalize_teams(df)
# Then all queries work
h2h = _h2h_win_rate(teams, "Mumbai Indians", "Chennai Super Kings", date)
The One Insight
The difference between 46% (baseline) and 61.8% (Gradient Boosting) came from proper feature engineering, not fancy algorithms.
Four carefully crafted features (H2H, Overall, Venue, Rolling) beat random models by 3.4x.
What's in Part 3 (Q&A Engine)
Next post: I'll show you how to build a 42,000-answer Q&A system without any APIs:
✅ Generate Q&A pairs from CSV
✅ TF-IDF vectorization explained
✅ <5ms query retrieval
✅ Zero hallucinations
✅ Why not LLMs?
Sneak preview: TF-IDF is slower than LLMs at understanding meaning, but it's 100% accurate and costs $0 per query.
This is Part 2 of 5. Subscribe to follow the series! 🏏
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