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Studying Sports Betting Analytics is Crucial for Informed Wagers
This guide explores how sports betting analytics can support more informed, data-driven decisions. By focusing on real examples, key techniques, and practical applications, you’ll learn the basics of how to find and use raw data, analyze odds, and identify value bets with greater accuracy. Whether you’re just starting or refining your approach, this guide offers the tools to make better decisions in an unpredictable industry.
Check out this guide on the RG Game Changers podcast on Spotify.
Imagine two NBA bettors, Alex and Jordan, both setting their sights on Game 1 of the NBA playoffs between the Cleveland Cavaliers and the Boston Celtics. They each plan to place a wager on the outcome—but their analysis methods differ greatly.
Alex’s Quick Glance Approach
Alex bases his decision on a combination of gut feeling and basic stats. He notes that Cleveland has had a reasonable recent win-loss record and that a few of their key scorers put up solid point totals over the last handful of games. Convinced that momentum and scoring numbers point in Cleveland’s favor, Alex places a bet on the Cavaliers to win outright, with little additional research into team matchups or deeper analytics.
Jordan’s Data-Driven Analysis
Jordan, on the other hand, takes a more analytical approach:
- Starting with Offensive and Defensive Ratings
- From NBA.com, Jordan discovers that Boston ranks highly in Defensive Rating (DRtg)—allowing relatively few points per 100 possessions.
- Why it Matters: Evaluating how efficiently teams defend gives insights into their overall strengths or weaknesses. For a team like the Celtics, their high Defensive Rating suggests they can limit Cleveland’s scoring opportunities.
- From NBA.com, Jordan discovers that Boston ranks highly in Defensive Rating (DRtg)—allowing relatively few points per 100 possessions.
- Evaluating Effective Field-Goal Percentage (eFG%) and True Shooting Percentage (TS%)
- Jordan compares both teams’ eFG% on Basketball-Reference to analyze the impact of three-point shots and checks their TS% on NBA.com to see free-throw efficiency. He notices that Boston’s top scorers excel at making three-pointers, and they have been averaging 17.4 three-pointers per game.
- Why it Matters: Analyzing eFG and TS% will give insight into how the Celtics have been shooting and could be used to make a prop bet on the number of field goals or three-pointers that a Boston player could potentially make.
- Jordan compares both teams’ eFG% on Basketball-Reference to analyze the impact of three-point shots and checks their TS% on NBA.com to see free-throw efficiency. He notices that Boston’s top scorers excel at making three-pointers, and they have been averaging 17.4 three-pointers per game.
- Pace
- Both teams' pace (possessions per 48 minutes) stats suggest that the Celtics are more willing to push the ball in transition. Jordan verifies this by examining transition vs. half-court scoring efficiency data, confirming that Boston often excels in fast-break situations.
- Matchup-Specific Factors
- Finally, Jordan examines how these two teams have fared against each other during the regular season and uses advanced shot-location data to see if the Cavs consistently defend the corners (where three-pointers are statistically more valuable) at a high level.
- Rather than relying on a single statistic, Jordan uses all of this information to make an informed wager that the Boston Celtics will score more than 17 three-pointers.
- Finally, Jordan examines how these two teams have fared against each other during the regular season and uses advanced shot-location data to see if the Cavs consistently defend the corners (where three-pointers are statistically more valuable) at a high level.
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While no metric guarantees the final outcome, Jordan's well-rounded, data-driven approach helps him better understand potential scenarios in Game 1.