Statistical Report on Run Scoring In Baseball

After this, I ran another regression, counting all of the previously used variables except for Stolen bases and caught stealing.

These two sets of data seem to suggest a very low relationship between stolen base statistics and runs scored, if any.

While both Caught Stealing and Stolen Bases are statistically significant in the first regression model, and suggest a much more logical successful stolen base ratio (about 76%), the b value of the data is extremely high, and the r-squared value is extremely low, suggesting a very low correlation from those numbers and runs scored.

In turn, the second regression is the same data that was used in the original 1967-2008 regression, minus SB and CS. The r-squared value dropped very slightly, from about .951 to about .948, meaning SB and CS only added 0.3% of understanding to the model. In turn, the standard error of runs only increased from 21.23 to 21.81.

Statistically speaking, using the original model, we would be able to find approximately 97.5% of teams within 2 standard errors of their expected runs using the coefficients and variables we have available.

Given that teams averaged 716.2 runs a season in this time period, I believe that these statistics have provided a strong model moving forward in being able to understand what wins baseball games offensively (extra base hits, walking) and what just does not matter as much (strikeouts vs. in play outs, steals, sacrifice hits).

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