*These graphs were constructed with data from retrosheet.org. If you like baseball data, I suggest you go there. Also any individual player stats must have at least 100 season at bats to appear on the graph/analysis.*

Last year a lot of emphasis was placed on how bad the Pirates were offensively, specifically with runners in scoring position [RISP]. Batting Average with RISP is the most basic measurement of ‘clutch’ — a much debated and analyzed concept. RISP stats are important to the extent that they can be rather descriptive of what happened during a game or a season. However, they don’t serve as a true judge of talent or as a useful method of prediction. My personal belief is that ‘clutch’ is ill-defined and fuzzy. Every time that ‘clutch’ is measured the best players are still the best players in every sport. To be useful, a ‘clutch’ player would have to be a bad player in normal situations, but happen to be better than the best players in high leverage situations time and time again.

With this in mind, some interesting things happened in MLB last season with regards to RISP. The Pirates made the playoffs with a terrible difference between their normal batting average and their batting average with RISP. In a stark contrast, the Cardinals had an outrageously high batting average with RISP, and the two teams played each other in the NLDS.

## Runners in Scoring Position

Breaking it down, the batting averages are roughly normally distributed over the league with an average of about .250. Due to randomness alone overall batting averages can vary +/- .030 from year to year, and that is with 400-600 at bats over the course of the season. At bats with RISP only occur in about 25% of a players total at bats allowing for twice the variation due to randomness.

The graphs above show the distribution of the batting averages across the league along with the distribution of the differences in the RISP batting average from the overall batting average. A positive difference means the player is batting with RISP at a higher percentage over their overall batting average, while a negative difference indicates that a player who bats at a lower percentage with RISP than their overall average. This is normally distributed as well.

The scatter plot represents the two batting averages graphed against each other. The plot shows a reasonably strong correlation between overall batting average and RISP batting average. In fact, the regression coefficient (the slope) of the best fit line is almost exactly 1. This means the league as a whole bats the same with RISP as it does overall. There is a lot of variance, because of the limited number of at bats with RISP. You would expect similar results if you randomly sampled 25% of any batter’s at bats and plotted them against that batter’s overall average.

## The Pirates

Last year, the Pirates won 90+ games and made the playoffs for the first time since a guy named George H.W. Bush was the President. They accomplished this feat mainly on highly efficient pitching, while their batting ability was below league average. (So much so that it would be worth looking at the reverse of this stat and see if the Pirates got lucky with pitching and fielding.) Their batting average with RISP was well below the league average. This can be attributed partially to a few players: McCutchen, Marte, Walker and Martin. McCutchen and Marte are excellent hitters; factoring in randomness, their batting average with RISP could have easily been really high and the Pirates might have won even more games. McCutchen could have easily batted .350 with RISP than his actually .282. Looking forward to 2014, you can assume that the Pirates will bat better with RISP than they did in 2013, just because it’s unlikely that those four batters will bat as poorly with RISP again this season.

Looking at the Pirates’ batting average with RISP is only useful in trying to understand what happened last season, and not predicting what could happen in 2014. Their overall team batting average of .245 in 2013 indicates what their offense will likely produce in 2014. (Although I would look at more advanced stats than just batting average.)

## Saint Louis Cardinals

Looking at the RISP differential chart, the Cardinals really stick out. They were absolutely tearing it up with RISP in 2013, but they had several really good hitters on that team. They had four batters with an average over .300 while the Pirates have just one. Also those St. Louis batters all happen to fall on the positive side of the RISP variation, and most of the players fall in an expected range of variation. Allen Craig is an exception/outlier batting with RISP 130 points over his overall batting average. I wouldn’t expect to see a repeat performance next year.

## Wrap-up

I don’t exactly have an explanation, but it appears that there are more MLBers that bat worse with RISP than overall. My one thought is that pitchers are pitching more cautiously or that fresh relief pitchers are more likely to come into the game when there’s a runner in scoring position.