The Four Factors of Basketball - And Why They Matter for Betting

Dean Oliver's framework explains over 90% of basketball outcomes. Here's how we translate it into actionable signals.

In 2004, Dean Oliver published Basketball on Paper and introduced the Four Factors of basketball success. Two decades later, they remain the most robust framework for understanding why teams win and lose - and where betting markets misprice games.

The Four Factors, in order of importance, are: effective field goal percentage (eFG%), turnover rate, offensive rebounding percentage, and free throw rate. BANE's Efficiency Edge signal is built directly on this framework.

Factor 1: Effective Field Goal Percentage (eFG%)

eFG% adjusts standard field goal percentage to account for the extra value of three-pointers. The formula is simple: (FGM + 0.5 × 3PM) / FGA. This single metric correlates more strongly with winning than any other box score stat.

Oliver's research found that shooting efficiency is the single most important factor in point differential between teams. When one team has a meaningful eFG% advantage over another, that gap is the strongest predictor of the game's outcome.

Our Efficiency Edge signal fires when a team's adjusted efficiency diverges meaningfully from what the spread implies. When the math suggests the market is underpricing the shooting differential, that's where edge lives.

Factor 2: Turnover Rate (TO%)

Turnover rate measures turnovers per 100 possessions. Every turnover eliminates a scoring opportunity and often creates one for the opponent - making it worth approximately 1.0 to 1.1 points of expected value per occurrence.

Oliver identified turnover rate as the second most important factor. In practice, we find that turnover differential is especially predictive in college basketball, where the talent gap between starters and bench players is larger and pressing defenses can force critical mistakes in tournament settings.

The Efficiency Edge model incorporates turnover differentials when the gap between two teams is meaningful, particularly when paired with pace data that amplifies the effect.

Factor 3: Offensive Rebounding Percentage (OREB%)

Offensive rebounding creates extra possessions. An offensive rebound is essentially a free possession - a second chance to score without the other team getting the ball. OREB% measures how often a team grabs their own misses.

Oliver ranked this as the third most important factor. In our analysis, offensive rebounding edges become most predictive in games with lower eFG% - when both teams are struggling to shoot, the team that gets second chances has a compounding advantage.

We flag games where one team has a significant offensive rebounding advantage while their opponent struggles on the defensive glass. This mismatch creates a structural advantage the market sometimes undervalues.

Factor 4: Free Throw Rate (FTR)

Free throw rate is typically measured as FTA/FGA - how often a team gets to the line relative to their shot attempts. Getting to the free throw line does two things: it generates high-efficiency scoring (league average FT% is around 72%), and it puts the opposing team in foul trouble.

Oliver ranked this as the least impactful of the four, but free throw rate becomes highly significant in close games and in tournament settings where foul trouble can neutralize a team's best player.

How We Use the Four Factors

Our Efficiency Edge signal doesn't just compare raw stats - it compares each team's four factors against the spread-implied expectation. The process works like this:

First, we calculate each team's offensive and defensive ratings across all four factors using multiple adjusted efficiency data sources. Second, we compute the net advantage across all four factors using a proprietary weighting system informed by Oliver's original research. Third, we convert that composite advantage into an implied point spread. Finally, we compare our four-factors-implied spread against the market spread.

When our implied spread differs meaningfully from the market, and the direction favors one side, the Efficiency Edge signal fires with confidence proportional to the gap.

Limitations

The Four Factors are powerful but not exhaustive. They don't capture defensive scheme matchups, coaching adjustments, travel fatigue, or the psychological dynamics of tournament play. That's why BANE runs multiple independent signals - Efficiency Edge, Power Model, and Regression Fade all incorporate shooting efficiency from different angles. When they converge on the same side, that's Multi-Model Confluence: our highest-conviction play type.

When multiple independent signals from BANE converge on the same side, it surfaces a Confluence flag for analyst review. When our analysts agree with the convergence, that's our highest-conviction pick type - multiple analytical frameworks and human judgment pointing the same direction.