Editing AI for Sports Analytics (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Player performance prediction with gradient boosting:'''
<syntaxhighlight lang="python">
import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import mean_absolute_error
import numpy as np

# Load player season statistics
df = pd.read_csv("player_stats.csv")  # Points, assists, rebounds, minutes, age, etc.

# Feature engineering for next-season performance prediction
df = df.sort_values(['player_id', 'season'])
df['age_squared'] = df['age'] ** 2  # Aging curve is non-linear
df['career_games'] = df.groupby('player_id').cumcount()

# Rolling averages (3-season window)
for stat in ['points', 'assists', 'rebounds', 'minutes']:
    df[f'{stat}_3yr_avg'] = df.groupby('player_id')[stat].transform(
        lambda x: x.shift(1).rolling(3).mean()
    )

# Target: next season points per game
df['target'] = df.groupby('player_id')['points'].shift(-1)
df = df.dropna()

feature_cols = [c for c in df.columns if c not in ['player_id', 'season', 'target', 'name']]
X, y = df[feature_cols], df['target']

# Temporal cross-validation (never train on future data)
tscv = TimeSeriesSplit(n_splits=5)
maes = []
for train_idx, val_idx in tscv.split(X):
    model = lgb.LGBMRegressor(n_estimators=200, learning_rate=0.05, num_leaves=63)
    model.fit(X.iloc[train_idx], y.iloc[train_idx])
    preds = model.predict(X.iloc[val_idx])
    maes.append(mean_absolute_error(y.iloc[val_idx], preds))
print(f"Mean MAE: {np.mean(maes):.2f} ± {np.std(maes):.2f}")
</syntaxhighlight>

; Sports AI tools and platforms
: '''Player tracking''' → Hawk-Eye (tennis/cricket), Second Spectrum (NBA), Stats Perform
: '''Soccer analytics''' → StatsBomb, Wyscout, SkillCorner (optical tracking)
: '''Wearables/load''' → Catapult, STATSports, Polar Team Pro
: '''Biomechanics''' → Tempus Ex Machina (pose AI), Simi Motion
: '''Fan analytics''' → OptaAnalyst, SportsLine, Fantasy Pros AI
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