Measuring the Moment: Explaining Our MLB Leverage Index and Clutch-Adjusted Statistics ...Middle East

We’ve gone a step further with our advanced baseball data to assess performance under pressure with clutch-adjusted situational hitting and pitching stats. Read on for our metric explainer, or go right to the numbers on the 2026 MLB leaderboards.

“Which player is the most clutch?” It is one of baseball’s most debated questions. Some fans may name their favorite player and point to a single defining moment, perhaps a big home run.

But what if we could quantify clutch performance?

That’s exactly what several existing statistics attempt to do. Most publicly available clutch metrics are calculated by taking win probability added and subtracting a context-neutral baseline, which is the average value of that outcome across all situations. The potential problem with this is similar outcomes can be reached in very different ways.

For example, imagine it’s the bottom of the ninth inning, with runners on second and third, two outs, and the batting team down by one. The batter hits a routine ground ball to the left side, and the third baseman makes a wild throw to first that ends up in the dugout. All runners advance two bases, and the batting team wins. In that same spot, if the batter instead launches a 500-foot walk-off home run, creating the same outcome of the game, most publicly available clutch metrics would still assign the exact same clutch score to both results. While these metrics are useful, it’s clear that evaluating these outcomes the same way is suboptimal.

To address these inefficiencies, Stats Perform has developed a new category of statistics that credits players for their clutch accomplishments. Let’s walk through it.

Win Probability and Leverage Index

First, we need to define how we are going to determine which situations are more important than others. For this, we have a win probability model that looks at the game situation and determines the likelihood that each team will win the game. This uses basic game-state information such as inning, base/out situation and score. From there, we need to know the average change in win probability for each situation.

Let’s walk through a couple scenarios to show how we would calculate the expected change in win probability for a situation.

We start with a situation that happens in every game – the beginning of the game:

Top of the first, no outs, no runners on, score is 0-0. In this case, the away batting team’s win probability is around 0.466 (away teams win about 46.6% of games).

Below is a table that looks at the outcomes, how they change the game situation and their effect on the away team’s win probability:

If we take the outcome percentage, multiply it by the WPA column, and sum the outputs, we get 0.034. This means the average change in win probability from the first play of the game is about 0.034.

Now let’s look at a more intense situation:

Bottom of the ninth, no outs, no runners on, the home batting team is down by one, and its probability of winning in this case is 0.156 (according to our win probability model).

Applying the same weighted-sum calculation gives us an average change in win probability of 0.102. If we want to know how much more important this play is than the start-of-game scenario, we simply divide the average change in win probability of this play (0.102) by the average change in win probability from the first play of the game (0.034).

0.102/0.034 = 3

This means the second scenario has an average change in win probability three times larger than the start of the game. This value of three becomes our new metric, called scaled leverage index. Essentially, this situation is three times more important than a typical play.

How do we use that to create a clutch metric from this? Well, that’s the next step.

Clutch Runs Added and Other Clutch Metrics

Using a similar principle to other clutch metrics, we need to establish a neutral runs-added metric. Luckily, we have a metric called raw value (RV). This metric assigns a runs-above-average value for every pitch of the game. If a batter does something good for his team, the raw value is positive, and if he does something bad, the raw value is negative. The best hitters in the game will add about 50+ runs relative to the average player over a 162-game season, according to raw value.

Using raw value, the best outcome a batter can achieve is a batted ball that we determine has a 100% chance of being a home run. The raw value of that at-bat is somewhere around +1.6. If we want to adjust this metric to get the context-dependent output, we simply multiply it by the scaled leverage index of that game situation. If he hits a 100% home run in the second situation we laid out before:

Bottom of the ninth, no outs, no runners on, the home batting team is down by one Scaled Leverage Index is three.

The calculation:

Raw Value * Scaled Leverage Index or 1.6 * 3 = 4.8

The result of this at-bat is +4.8 for a metric we’ll call leverage-adjusted raw value.

If a batter does the same thing at the beginning of the game, his leverage-adjusted raw value will be the same as his raw value, since the scaled leverage index of that situation is one. So the calculation is:

1.6 * 1 = 1.6

To determine a player’s clutch level, we’ll add up his leverage-adjusted raw value, and compare it to his standard raw value. This value will tell us which hitters reach a new level when the lights get brightest. We’ll call this metric clutch runs added. It tells us how many runs this player adds by making the most of his performance. The calculation for this:

(Leverage-adjusted raw value / average scaled leverage index) – raw value

The reason we divide by the player’s average scaled leverage index is to make sure we’re not overvaluing or undervaluing a player who happens to have appearances only in high-leverage situations.

Using clutch runs added, we can create a rate version of a stat that is called clutch RV+ (or clutch RV- for pitchers). If you’re familiar with our “+” metrics, you know 100 is average and over 100 means above average and under 100 means below average for a hitter (it’s reversed for RV- and a pitcher: lower than the league-average 100 is better). For clutch RV+, 100 means a player performs about the same in high-leverage situations, as in medium and low-leverage situations.

To give a sense of scale, below is a table that provides a guideline for measuring a clutch RV+ value:

For batters:

For pitchers:

To be clear, this metric estimates how much better a batter is in important situations relative to himself. If he’s a poor hitter overall but performs like an average hitter when the moments are tough, he will have a clutch RV+ above 100. Conversely, if he is an elite hitter overall but in high-leverage situations is merely a very good hitter, he will likely have a clutch RV+ of below 100.

In addition to clutch RV+, we can apply this same concept to our other metrics to give us clutch discipline+, and clutch BIP+ for batters as well as clutch whiff+ and clutch strike+ for pitchers. These are clutch-adjusted versions of our standard discipline+, BIP+, whiff+ and strike+, which are explained more in the links for each.

Again, if a player is above 100 for the stat, such as clutch discipline+, this doesn’t necessarily mean he has good discipline; it means he makes better swing decisions when the game is on the line than in lower-leverage situations.

Scales for those stats in tables below:

We’ll use leverage and clutch in our analysis going forward, and we invite you to do the same. For a look at which players are getting it done when it matters most, check out our leaderboard page for batters and pitchers.

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Measuring the Moment: Explaining Our MLB Leverage Index and Clutch-Adjusted Statistics Opta Analyst.

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