스포츠데이터

스포츠 데이터 3. Data Collection & Cleaning
3.1. Sourcing Data
Data is the fuel propelling your sports betting engine. For major leagues—such as the NFL, NBA, MLB, NHL, and the English Premier League—you can rely on:
Official league websites for daily stats and historical archives
Sports media outlets (e.g., ESPN, BBC Sport) for detailed game recaps and news
Stat-focused platforms (like Stats Perform or Sportradar) that offer advanced analytics
Betting-specific APIs that deliver odds and line movements in real time
Typical datasets include box-score metrics (points, rebounds, yards, goals), advanced stats (e.g., Player Efficiency Rating or Expected Goals), and contextual elements (injuries, rest days, home/away splits). The richer your data, the more robust the insights your model can glean.
3.2. Data Cleaning and Preparation
Raw data often arrives riddled with errors, missing fields, and inconsistent naming conventions. Serious bettors must address these issues meticulously. For instance, if the same team is labeled “NYG” in one dataset and “NY Giants” in another, you must standardize. Missing values might require imputation techniques, while obviously extreme outliers (a quarterback magically throwing 50 touchdowns in a single game record) may warrant deeper investigation or removal. By methodically scrubbing your data, you prevent false signals and ensure each variable lines up accurately. Proper organization during this step arms your model with trustworthy information, reducing the risk of bizarre predictions caused by messy inputs.

4. Exploratory Data Analysis & Feature Engineering
4.1. Exploratory Data Analysis (EDA)
With data in hand, it’s time to explore. This is where statistical methods and visualizations help you spot trends, uncover correlations, and identify potential explanatory variables. For instance:
Home vs. Away Performance: Some teams perform exceptionally better on home turf, aided by the crowd atmosphere, travel reduction, or familiarity with local conditions.
Impact of Key Players: A star player’s presence might amplify offensive efficiency drastically.
Streaks & Slumps: You might observe whether recent winning streaks correlate with certain advanced metrics or if slumps coincide with intangible factors.
Tools like Python’s Matplotlib or Seaborn can graph distributions of points per game, yardage gains, or possession time, unveiling hidden stories in each dataset. Such revelations set the stage for constructing meaningful features that resonate with the sport’s underlying dynamics.
4.2. Crafting Predictive Features
Feature engineering converts raw data into refined variables that illuminate patterns. Instead of merely feeding “points scored” into the model, consider rolling averages over the last few games, weighting recent performances more heavily, or factoring in rest days between matches.
For example:
In football: You might track quarterback efficiency, defensive pressure rates, or red-zone conversion percentages.
In soccer: “Expected Goals” (xG) could join pass accuracy while factoring in a star striker’s shot conversion rate.
In basketball: Player usage rates, pace, and net rating lineups become relevant.
The main objective is to craft features that spotlight non-obvious nuances. Doing so lets your model interpret crucial game contexts that aren’t visible in basic box scores. Even small improvements in feature design can tip the scales between a losing strategy and consistent, long-term profitability.