BABIP and Year-to-Year Offensive Fluctuations

As we anticipate the start of the 2015 Major League Baseball season, we begin to speculate about player performance in the upcoming season. While most players are somewhat consistent year-to-year, there are some who have either breakout years or terrible seasons. These extreme years are a confluence of events throughout the season such as player health, skills peaking, and luck — which can be partially captured by BABIP.

To find the seasons with the greatest offensive output changes, I calculated year-to-year changes for players from 2000-2014 in a handful of offensive statistics: WAR, OPS, BABIP, and HR. Since playing time can fluctuate because of injury or being a rookie, I eliminated comparisons of seasons that a player had a high discrepancy in plate appearances.

To visually compare the seasons, I used slope graphs to show the year-to-year changes in the various statistics. Each graph is limited to players in the sample who had the largest changes in both the positive and negative directions. The left end of the line represents the player’s statistic in one year with the right end of the line representing the following year. A steeper the slope indicates the largest change between two years.

For home runs, both the largest increase and the largest decrease came from Adrian Beltre. In 2004, Beltre had a breakout year hitting 48 home runs with the Dodgers, but after signing with the Mariners and switching to the American League the following year, he regressed heavily hitting 29 fewer HR. In the context of Beltre’s career, 2004 was his highest season HR total, and 2005 was one of his worst seasons.

MLB Year to Year HR Comparison

Beltre’s HR performance swing between seasons barely beats the spike in HR performance during Barry Bonds’ record-setting, 73-HR season. Beltre had a 25-HR increase, while Bonds only had a 24-HR increase.

Not surprisingly after dropping 29 HR, Adrian Beltre’s 2005 season also surfaces as the largest single-season decline in WAR. Beltre did not only lack home run power in 2005, but he struck out more (2004: 13.2%, 2005: 16.6%), walked less (2004: 8.1%, 2005: 5.8%), and had a 40-point drop in BABIP (2004: .325, 2005: .281). These statistics all factor into a lower WAR value for Beltre’s 2005 season.

MLB Year to Year WAR Comparison

In his near-MVP 2011 season, Matt Kemp’s WAR spiked at 8.4, and this was a drastic improvement over his near replacement level season in 2010. While 2011 is Kemp’s best season to date, he had previously recorded a 3.1 WAR and 5.0 WAR season before 2010, so the jump to 8.4 was not completely unexpected. This large year-to-year jump can also be attributed to the slump season he had in 2010 being so low. Since peaking at 8.4 WAR, Kemp has had injury problems causing loss of playing time and drops in his offensive output.

BABIP is notoriously inconsistent between seasons; it can be used to analyze a player’s performance indicating possible regression. Between 2012 and 2013, Jarrod Saltalamacchia had the largest fluctuation in BABIP in the sample improving about 100 points. This dramatic increase in BABIP contributed to the best year in his career and a World Series win.

MLB Year to Year BABIP Comparison

On the other end of the spectrum, David Murphy similarly had a career year in 2012 helped by his .333 BABIP, but in the following year, 2013, he had a 100-point drop in his BABIP and a subsequent 80-point drop in his batting average.

The performance swings of Saltalamacchia and Murphy demonstrate the relationship between offensive output and BABIP. I plotted the year-to-year change in OPS against the year-to-year change in BABIP, and there’s a moderate relationship (r = .37) between the two. However, the most interesting aspect of this plot is that the five players with the largest change in offensive output between seasons are all far outliers.

Change in OPS vs Change in BABIP

I have highlighted the players we have already looked at for having a breakout or slump year; their change in offensive output is reflected in the change in OPS from season to season. The players also have a correlating change in BABIP with the exception of Barry Bonds, who had a slight drop in his BABIP while his OPS rose dramatically. This occurred because HRs are excluded from BABIP since the ball does not fall into the field of play.

The dramatic year-to-year changes and BABIP’s relationship with offensive output highlight the highly variable nature of baseball outcomes.

We hoped you liked reading BABIP and Year-to-Year Offensive Fluctuations by Sean Dolinar!

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Rob Parker

This is interesting stuff and the visuals are well made. Is there a way to use some of this data to identify players whose offensive value is most/least dependent on BABIP? For example, Jose Bautista posts below average BABIPs every year, but his offensive production is still high because he can hit the ball hard and take lots of walks. Saltalamacchia is the opposite type of hitter, whose value swings wildly from season to season based on his BABIP. Basically, it would be interesting to see a correlation between year to year BABIP and wRC+ to see in data and visual forms which players fall into which categories.


It occurs to me that power hitters are less reliant on BABIP (because they rely so heavily on HRs, which do not rely on BABIP) while slow guys without a lot of power rely heavily on BABIP (because they have no way to generate offense except to get on base). Bautista strikes me as a good example of the first group while Chris Johnson is an extreme example of the second.