As we pass the midpoint of the 2024 Major League Baseball season, with the All-Star Game highlighting talents from both the National and American Leagues, the ever-present fascination with baseball statistics intensifies. The beautiful intersection of baseball and numbers has captivated fans and analysts alike for generations. From poring over the “Bill James Baseball Abstract” to meticulously studying the Baseball Encyclopedia, the quest to understand the game through data is deeply ingrained in baseball culture.
It’s common to hear narratives emerge based on simple comparisons: “Hitting is down this year,” or “Stolen bases are surging.” These observations often stem from comparing the current season’s stats to the previous year. While such comparisons offer a snapshot, they often lack the depth needed to truly understand underlying trends and variations in MLB performance. Analyzing just two data points can be misleading and fail to capture the bigger picture of baseball statistics over time.
News outlets frequently highlight these year-over-year changes. For instance, reports have surfaced about declining run scoring in the current MLB season.
Examining the Decline in Run Scoring Across Major League Baseball
“Major League Baseball observers have noted a significant decrease in the number of runs being scored this season.”
Let’s delve into the raw numbers to Compare Stats Mlb, using data from Baseball-Reference.com.
Average Runs Per Game (Team) – 2023 (Full Season): 4.62
Average Runs Per Game (Team) – 2024 (Partial Season): 4.39
Indeed, there’s a decrease of 0.23 runs per game, approximately a 5% drop. However, to truly compare stats mlb and understand the significance of this change, we need to broaden our scope and consider more context.
Average Runs Per Game (Team) – 2022 (Full Season): 4.28
Interestingly, run scoring actually increased by 0.34 runs per game in 2023 compared to 2022. The 2024 scoring rate is still higher than in 2022. Why isn’t that comparison making headlines? This illustrates the pitfall of relying solely on two-year comparisons when analyzing MLB stats.
To gain a more meaningful understanding when we compare stats mlb, we need a more robust analytical tool. Enter the Process Behavior Chart (PBC), a method designed to analyze data over time and distinguish between common cause variation and special cause variation. Instead of fixating on isolated two-year comparisons, PBCs allow us to see data within a broader historical context. You can learn more about creating PBCs here or in Measures of Success.
In essence, a PBC plots data points on a run chart and adds calculated lines: an average line, a lower control limit (LCL), and an upper control limit (UCL). These limits are statistically derived from a baseline period and account for the inherent variation in the data. Higher variation in the baseline data leads to wider control limits.
Let’s apply a PBC to MLB runs per game, going back to 1990 for a longer-term perspective to effectively compare stats mlb over time.
This chart offers a much richer narrative than a simple two-year comparison. When we compare stats mlb using a PBC, we see that 2024 run scoring is not historically low within this timeframe. Scoring was actually lower in the early 1990s (1990-1992). Then, a significant upward shift occurred, although without a clear “special cause” signal (a data point exceeding the UCL or eight consecutive points above the average).
However, the upward trend in the mid-1990s is strongly associated with the “steroid era” in baseball, a period known for increased offensive output.
Following the steroid era, run scoring generally declined until around 2015, when the average appears to have shifted upward again. Notably, from 2008 onward, there are multiple years with data points below the baseline average. Recalculating the average and control limits from 2008 provides a new baseline for analyzing more recent trends in MLB run scoring. The reasons behind the shift in 2008 are less clear-cut but warrant further investigation when we compare stats mlb and look for potential influencing factors.
ChatGPT’s attempt at alt text for this chart provides a basic description:
“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 adequate, it doesn’t capture the nuances as effectively as analyzing the chart ourselves, especially for those seeking to compare stats mlb for deeper insights.
A more accurate interpretation would be: “The chart reveals fluctuations in average runs per game over time, with noticeable shifts rather than simple peaks and troughs. The scoring rates of the past few years appear statistically consistent, lacking any ‘special cause’ to explain significant deviations in the last decade and a half.”
Comparing Other Key MLB Statistics
Are hits and doubles also experiencing a decline this season, as suggested by some narratives? Again, let’s move beyond simplistic two-year comparisons and examine Process Behavior Charts for these key offensive statistics to effectively compare stats mlb.
The hits per game chart reveals some similarities to the runs per game trend. We observe the upward shift in the 1990s and the establishment of a new, lower baseline average around 2008.
However, a crucial difference emerges: in the hits per game chart, we see data points falling below the lower control limit. This constitutes a “signal” indicating a statistically significant shift, observed for the past five seasons. While runs are not down in a statistically meaningful way based on PBC analysis, hits indeed are. This nuanced insight is easily missed when we only compare stats mlb using two-year averages.
What about doubles? For this chart, we intentionally maintain the original average and control limits to illustrate the long-term perspective. The chart still reveals valuable insights.
These PBCs provide a far richer context for statistical analysis compared to simple two-year comparisons. When we compare stats mlb using these charts, the trends and deviations become much clearer.
We can now ask why doubles might be declining. One prevailing theory is that defensive strategies have shifted, with teams positioning outfielders deeper to prevent extra-base hits like doubles, potentially at the cost of allowing more singles. Given that doubles are statistically more likely to lead to runs than singles, this strategic shift, combined with potentially decreased overall offensive output, could contribute to the observed trend. Is this shift in defensive strategy the “special cause” influencing the decline in doubles, alongside broader offensive trends?
Regarding home runs, an update to the home runs per game chart since 2020 provides further context. A previous analysis explored a home run “surge” up to 2019:
Unraveling the Mystery Behind MLB’s Home Run Surge: A Data-Driven Exploration
The updated chart below reveals whether we’ve entered a “post-surge” phase. It appears home run rates are now fluctuating around a new, potentially stabilized average. Further analysis using PBCs over longer periods would be needed to confirm this stabilization and compare stats mlb effectively in the home run category.
The Exception: Stolen Bases Are Indeed Surging
Stolen bases present a different picture. Unlike runs, hits, and doubles, stolen bases are demonstrably up, and significantly so, starting in the 2023 season. This increase has been widely reported. Let’s examine the PBC for stolen bases to compare stats mlb and understand the magnitude and nature of this change.
The stolen base PBC clearly illustrates a dramatic increase. Several “special causes” appear to explain this surge, primarily rule changes implemented by MLB:
This scenario seems to be a clear example of “cause and effect.” Rule changes directly designed to encourage stolen bases have indeed resulted in a significant increase in stolen base attempts and successes.
Conversely, the chart also reveals a long period of decline in stolen bases prior to 2023. This decline is attributed to strategic shifts in team thinking, driven by data analytics. Teams began to perceive the risk of an out (when a runner is caught stealing) as outweighing the potential benefit of gaining a base in many situations. Read more about this trend.
Bottom of the 9th (Conclusion)
In conclusion, while the temptation to make quick judgments by comparing two years of baseball statistics is strong, using Process Behavior Charts offers a far more insightful and accurate method for understanding trends in MLB data over time. Fluctuations in runs, hits, and doubles per game, when viewed through the lens of PBCs and longer historical data, tell a complex story that simple two-year comparisons miss.
The apparent decrease in runs per game in the current season, when examined in isolation, might seem concerning. However, when we compare stats mlb within a broader historical context using PBCs, it becomes part of a nuanced narrative of variation and shifts in offensive output. Similarly, the dramatic rise in stolen bases, directly influenced by recent rule changes, exemplifies how deliberate system changes can significantly impact statistical trends in baseball.
By employing Process Behavior Charts, we gain a better understanding of the inherent variability and stability within baseball statistics, and indeed, any business metric. This approach helps us avoid overreacting to short-term fluctuations and instead focus on identifying genuine trends and their potential underlying causes. Whether it’s defensive strategy shifts affecting doubles or strategic calculations influencing stolen bases, long-term data analysis provides a deeper appreciation for the complexities of baseball.
As baseball continues to evolve, so too must our analytical methods. Adopting comprehensive statistical tools like Process Behavior Charts empowers fans, analysts, and team personnel to make more informed observations and decisions. So, the next time you hear a commentator make a sweeping statement based on comparing just two seasons, remember the value of context and the importance of looking at the bigger picture when you compare stats mlb.
Thank you for exploring this deeper dive into baseball statistics. Here’s to an exciting second half of the season and the enduring appeal of America’s pastime. Play ball!
