Batting average: measuring how often a portfolio beats its benchmark period by period
Most performance metrics ask how much a portfolio outperformed. Batting average asks how often. A portfolio manager with a batting average of 0.60 beats the benchmark in 60 per cent of measured periods—regardless of whether the wins are large or the losses are small. It is a frequency measure, and frequency matters: consistent, moderate outperformance is often more sustainable than volatile, lumpy outperformance driven by a handful of large bets.
What batting average measures
Batting average counts the number of periods in which the portfolio return exceeds the benchmark return, then divides by the total number of periods measured. It says nothing about the size of the gap—only whether the portfolio was ahead or behind in each interval. The metric originates in baseball, where it measures the fraction of at-bats resulting in a hit. Applied to investing, the analogy is imperfect but intuitive: each measurement period is an at-bat; a period of outperformance is a hit.
The formula
Batting Average = Number of periods where Rp > Rb ÷ Total periods
Where Rp is the portfolio return and Rb is the benchmark return in each period. A result of 1.0 would mean the portfolio beat the benchmark in every single period; 0.0 means it never did. In practice, values between 0.45 and 0.65 are typical for active strategies. A value above 0.55 over a statistically meaningful sample (100+ periods) is considered noteworthy.
How to interpret batting average
Batting average must be read alongside a measure of win/loss magnitude. A manager with a batting average of 0.45—below 0.50—can still generate positive active return if the winning periods are substantially larger than the losing ones. This is the relationship captured by the win rate and payoff ratio: frequency and magnitude are two distinct dimensions of edge. Batting average isolates the frequency dimension alone. Periods can be daily, weekly, monthly, or annual; the choice affects the number. Monthly batting averages are most common in institutional reporting because they balance granularity against noise.
Rolling batting average
A single lifetime batting average can obscure meaningful variation across time. A rolling window—typically 12 or 36 months—reveals whether outperformance consistency is stable or whether it clusters in particular market regimes. A manager who beats the benchmark in 70 per cent of months in bull markets but only 40 per cent in bear markets has a structurally different profile from one with 55 per cent in both. Rolling batting average surfaces this regime dependency.
Limitations
Batting average ignores the size of outperformance or underperformance entirely. A period where the portfolio returns 0.01 per cent above the benchmark counts the same as a period where it returns 5 per cent above. This makes the metric easy to game: a manager who hedges aggressively to avoid losing periods while capping gains can score a high batting average while destroying value on a cumulative basis. Batting average should therefore always be considered alongside cumulative active return and the information ratio. It is also sensitive to the length and frequency of the measurement period—daily batting averages and monthly batting averages for the same strategy will differ, sometimes substantially.
Batting average in pfolio
Batting average is not currently displayed in pfolio Insights. Monthly portfolio and benchmark returns are available in the data export, from which batting average can be calculated manually over any measurement period.
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