In baseball, a hot start is almost always a lie. It takes far more at-bats than most fans realize before any stat stabilizes enough to tell you something real about a player.
Why Do Small Samples Mislead Us So Badly in Baseball?
Baseball is a random sport dressed up as a skill sport, and small samples amplify the randomness until it drowns out the signal entirely. Think about what a batting average actually measures: hits divided by at-bats. On any given swing, a ball hit at 105 mph can go straight to a fielder, while a weak 72 mph grounder finds a hole. Luck is always in the mix, and early in a season, it dominates.
Consider what it means to go 10-for-30 to start April. That's a .333 average, and fans and media will talk about it like it means something. But 30 at-bats represents maybe a week's worth of games. The margin of error on that sample is enormous. A player could hit .333 over 30 at-bats just by getting lucky on where balls land, not because he's suddenly a different hitter.
The problem compounds because our brains are wired to find patterns. We see three good games and we're convinced we've spotted a trend. Baseball databases give us enough numbers to tell a story, but not always enough numbers to make that story true.
How Many At-Bats Does It Actually Take for Batting Average to Stabilize?
Roughly 600 at-bats before batting average becomes a reliable predictor of true talent, which is essentially a full season's worth of data. This is one of the most cited findings in baseball analytics, supported by research on stat stabilization that FanGraphs has documented extensively. The concept is simple: a stat "stabilizes" when it's telling you more about the player than about luck.
For context, a full MLB season gives most regulars somewhere between 500 and 600 plate appearances. That means even a complete season of batting average data sits right at the edge of being meaningful. One bad month can swing it 20 or 30 points in either direction and that swing says almost nothing about the player's actual hitting ability.
This is why scouts and analysts have largely moved away from batting average as a primary evaluation tool. It stabilizes too slowly to be useful in-season, and even year-to-year it carries too much noise. A player who hits .310 one year and .270 the next probably didn't become a worse hitter. He might have just gotten unlucky on balls in play.
Which Stats Stabilize the Fastest?
Strikeout rate and walk rate stabilize much faster than batting average, often within 150 to 200 plate appearances. Research documented at FanGraphs shows that these "true outcome" stats are far less dependent on luck because they don't require a fielder to misplay a ball or a shift to fail. Either the batter swings and misses or he doesn't. Either the pitcher throws four balls or he doesn't.
Home run rate sits somewhere in the middle, stabilizing around 300 to 400 plate appearances. Power is a real, measurable skill that shows up faster than contact skill, partly because exit velocity and launch angle data confirm it even when the balls don't leave the park.
BABIP, batting average on balls in play, is the wild card. It's the stat that explains why batting average is so noisy. League average BABIP sits around .300, but individual players deviate from that based on speed, contact quality, and pure luck. Fast players who hit the ball hard can sustain a .340 BABIP. Slow pull hitters might stabilize around .270. But it takes hundreds of plate appearances to know where any individual player's true BABIP lands.
What Does This Mean for Judging a Player's April?
It means you should almost completely ignore batting average in April. A player hitting .180 through 60 at-bats is not necessarily struggling. A player hitting .380 is not necessarily locked in. Both of those numbers will almost certainly move toward something more ordinary as the sample grows.
What you should watch instead in small samples are the process stats. Is a hitter making hard contact? Baseball Savant tracks exit velocity on every batted ball from the moment the season starts. A hitter who's batting .190 but posting a 92 mph average exit velocity is probably fine. The hits will come. A hitter batting .310 with a 84 mph average exit velocity is on borrowed time.
Walk rate and strikeout rate matter early too. These tell you about a hitter's approach at the plate, and as established above, they stabilize fast. If a hitter who struck out 28% of the time last year is suddenly striking out 35% in his first 100 plate appearances, that's worth paying attention to. That kind of shift in strikeout rate has predictive power even in small samples.
How Do the Best Analysts Use Small Samples Without Getting Burned?
They treat early-season stats as weak signals rather than conclusions, and they combine them with what they already know. A player's prior track record carries enormous weight. If a proven hitter starts slowly, you need a lot of evidence before you conclude he's changed. If a player with no track record of getting on base suddenly posts a great OBP in April, be skeptical.
The practical approach is something analysts call "regressing to the mean." You don't just look at what a player is doing right now. You blend the small current sample with their established baseline. So a career .260 hitter who's batting .310 through April is probably a .265 or .270 hitter right now, not a .310 hitter. The current sample gets weight, just not equal weight to the prior history.
This is also why predictions in baseball are genuinely difficult, especially early in a season. The tools and data are better than ever, but the fundamental randomness of the game means uncertainty never goes away. If you want to dig deeper into why sports predictions are hard in general, check out our piece on why sports predictions are harder than they look.
Does Exit Velocity Tell You More Than Batting Average in 2026?
Yes, in almost every meaningful context. Exit velocity and related Statcast metrics stabilize faster and predict future performance better than batting average ever has. A hitter's hard-hit rate, defined as balls hit 95 mph or harder, starts to tell a real story within 50 to 75 batted ball events. That might be three weeks into the season.
This is one of the reasons baseball analytics has evolved so dramatically. When all you had was box scores, batting average was the best available measure of hitting quality. Now that we have spin rate, launch angle, sprint speed, and exit velocity on every single play, batting average looks almost quaint. It's still meaningful over a full season, but it's the last stat worth checking in April.
The best predictive combo in early-season MLB? Exit velocity plus strikeout rate plus walk rate. Those three together give you a real picture of what a hitter is doing at the plate, regardless of what the actual hit count looks like.
If you want to put this knowledge to work on real player predictions this season, Download GAGE and compete on stat predictions scored against the true implied odds.
FAQ
How many at-bats does it take for a batting average to be reliable?
Around 600 at-bats, which is roughly one full MLB season for a regular starter. Below that threshold, luck from balls in play is distorting the number more than actual skill is.
Which MLB stats are trustworthy early in the season?
Strikeout rate and walk rate stabilize fastest, within about 150 to 200 plate appearances. Exit velocity metrics from Statcast are meaningful even earlier, often within 50 to 75 batted ball events. Batting average is the last stat to trust in small samples.
What is BABIP and why does it make batting average unreliable?
BABIP stands for batting average on balls in play. It captures the role of luck and defense in determining whether a batted ball becomes a hit. Because so many hits depend on where fielders are positioned and whether a ball finds a gap, BABIP bounces around dramatically in small samples, dragging batting average with it.