The 2024 Major League Baseball season is in full swing, past the All-Star Game break where National League and American League stars showcased their talents. Baseball, a sport deeply intertwined with statistics, has always captivated fans and analysts alike with its numbers. From childhood fascination with books like the “Bill James Baseball Abstract” to poring over the weighty pages of the “Baseball Encyclopedia,” the allure of baseball stats is undeniable.
As someone who frequently blogs about baseball statistics, especially concerning the application of “Process Behavior Charts,” I often find myself advocating for more sophisticated statistical methods than simple two-point comparisons. It’s common to hear pronouncements like, “Doubles are way down this year,” or “Runs and hits are declining,” based on comparing the current season to the last. While such observations might be factually correct for those specific two points, they often lack deeper meaning and context when you truly Compare Mlb Stats.
Recent headlines echo this sentiment, such as:
The Culprits Behind Baseball’s Run-Scoring Decline
“As Major League Baseball fans know, run scoring is down significantly this season.”
Let’s examine the raw numbers to compare MLB stats for runs per game, using data from Baseball-Reference.com.
Runs per game (per team) in 2023 (full season): 4.62
Runs per game (per team) in 2024 (partial season): 4.39
Indeed, there’s a decrease of 0.23 runs per game, approximately a 5% drop. However, before jumping to conclusions about the reasons behind this, we need to broaden our perspective and compare MLB stats across a longer timeframe.
Runs per game (per team) in 2022 (full season): 4.28
Interestingly, runs per game actually increased by 0.34 in 2023 compared to 2022. Suddenly, the narrative shifts. Why aren’t we seeing headlines proclaiming that 2024 scoring is up compared to 2022? This highlights the pitfall of relying solely on two-year comparisons when we compare MLB stats. Two data points, or even three, hardly constitute a trend.
A far more insightful approach to compare MLB stats is to employ a Process Behavior Chart (PBC). This method, detailed in resources like this guide and my book Measures of Success, provides a richer understanding of statistical trends.
In essence, a PBC visualizes data as a run chart, augmented with calculated lines representing the average, lower limit, and upper limit. These limits are determined from a baseline dataset, and they widen with increased data variation. Let’s compare MLB stats for runs per game using a PBC dating back to 1990:
This chart provides a much more comprehensive view when we compare MLB stats. It reveals that 2024 run scoring isn’t historically low within this period. In fact, scoring was lower in the early 1990s. The chart shows an upward shift, likely related to the steroid era, although no single data point technically breaches the upper control limit as a “special cause.” However, the prolonged period above the average is suggestive.
Following the steroid era, scoring generally declined until around 2015, when the average appears to have shifted upwards again. Notably, starting around 2008, we observe a period of eight or more data points below the baseline average. Recalculating the average and limits from 2008 onwards reveals a new baseline. The reason for this 2008 shift is an interesting question for further investigation when we compare MLB stats across different eras.
When we compare MLB stats for the last fifteen seasons or so, including 2024, the run scoring numbers are statistically consistent. There’s no “special cause” to explain recent fluctuations within this timeframe. ChatGPT’s attempt to create alt text for this chart offered a decent summary:
“The chart shows variations in the average number of runs per game over the years, with noticeable fluctuations. The average number of runs per game has remained relatively stable with some peaks and troughs, particularly around the mid-1990s and the late 2010s.”
While not perfectly worded, it captures the essence better than a simple two-year comparison. My interpretation would be: “The chart illustrates the variation in average runs per game over time, showing fluctuations and shifts. The scoring rates of the past four years are statistically similar, lacking any ‘special cause’ for significant change over the last 15 seasons.”
Comparing Other Key MLB Stats
Are hits and runs truly down this season? Yes, in a two-year comparison, but what does a broader analysis reveal when we compare MLB stats using PBCs?
These charts share similarities with the runs per game chart, showing the upward shift in the 1990s and a subsequent lower baseline around 2008. However, a key difference emerges: recent data points fall below the lower control limit. This signifies a “signal” for hits per game over the past five seasons. While runs are not statistically significantly down, hits are, when we compare MLB stats over time.
What about doubles? This time, I intentionally maintained the original average and limits to emphasize the chart’s inherent insights when we compare MLB stats.
These PBCs provide a much richer statistical context when we compare MLB stats. We can now delve deeper into why doubles might be declining. A potential explanation is the strategic shift towards deeper outfield positioning, aiming to prevent doubles at the cost of allowing more singles. Given that doubles are statistically more likely to lead to runs than singles, this defensive strategy, combined with potentially reduced overall offensive output, could be a “special cause.”
Regarding home runs, an update to the home run per game chart since 2020 reveals interesting patterns. Previous analysis discussed a 2019 home run “surge” in a prior post:
Unraveling the Mystery Behind MLB’s Home Run Surge: A Data-Driven Exploration
The updated chart suggests we’ve moved into a “post-surge” phase, fluctuating around a new average. Further analysis is required to definitively compare MLB stats for home runs and understand long-term trends.
One MLB Stat That Is Truly Increasing
Stolen bases are indeed on the rise, significantly so. This surge began last year. Let’s examine the PBC to compare MLB stats for stolen bases:
The numerous news stories about increased stolen bases last year are validated by this chart. Several “special causes” contribute to this rise: larger base sizes, limitations on pickoff attempts, and the disallowing of infield shifts.
This appears to be a clear example of “cause and effect” when we compare MLB stats before and after these rule changes.
The prior decline in stolen bases, evident in the chart, likely stemmed from teams’ analytical assessments that the risk of an out from being caught stealing often outweighed the benefit of gaining a base (read more).
Bottom of the 9th (Conclusion)
In conclusion, while the temptation to compare MLB stats using simple two-year snapshots is strong, a more rigorous approach, such as Process Behavior Charts, offers a far more nuanced and accurate understanding of long-term trends. Fluctuations in runs, hits, and doubles per game reveal distinct stories when viewed within a broader historical context.
The perceived decrease in runs per game this season, when isolated, might seem concerning. However, when we compare MLB stats against previous years using PBCs, it becomes part of a larger, more variable narrative. Conversely, the surge in stolen bases, clearly influenced by recent rule modifications, underscores how system-wide changes can significantly impact statistical trends.
Employing Process Behavior Charts allows us to better grasp the inherent variability and stability within baseball statistics, and indeed, any business metric. This methodology prevents overreactions to short-term variations and guides us towards identifying genuine trends and underlying causes. Whether it’s defensive shifts affecting doubles or strategic decisions impacting stolen bases, long-term data analysis provides a richer appreciation for the complexities of baseball.
As baseball evolves, so must our analytical tools. Embracing comprehensive statistical methods empowers fans, analysts, and players to make more informed observations and decisions when they compare MLB stats. So, the next time you hear a commentator make a definitive statement based on a single season’s data, remember the value of context and the importance of examining the bigger picture.
Thank you for exploring baseball statistics with me. Here’s to an exciting second half of the season and the enduring appeal of America’s pastime. Play ball!
