Does the Increase in Sacrifice Bunting in College Contribute to More Runs? Continued…

Part 2, Part 3, & Conclusion

Part 2: Examining the relationship between all of the collegiate teams’ run production and their frequency of sacrifice bunting. The teams have been organized by conference.

Here is a link to the data for the College Data By Conference.

By Conference

R = .80

Sacrifice Bunts Runs
Mean 400.9032258 2686.645161

Part 3: Taking a look at the relationship between quantity of sacrifice hits and the total runs scored for the top 120 offenses in college baseball. The chart, is much less descriptive and is below.

Data for part 3

120 offenses

  Sacrifice bunts      runs
mean 43.53333333 342.1916667



First, we have to acknowledge that there are several other factors that contribute to runs. However, with the near 30% increase in the amount of sacrifice bunts in the college game since the change in the bats, there is a contingency that believes sacrificing produces more offense. To restate the hypothesis we are testing, college baseball teams believe that if they advance runners on the bases by sacrificing an out with a bunt then they will score more runs.

By Conference:                                                             By Top 120 Scoring Teams

By Conference 120 offenses


In our study, we have looked at the conglomerate of each team divided by conference. With a coefficient of determination value of .6401 (R-Squared), we conclude that 64% of the variation from the mean is explained by the relationship between sacrifice bunts and runs scored. Although that is less than our threshold to determine statistical significance (usually 95% is required), observing the chart does show a positive correlation between sac bunts and runs. The correlation coefficient is .8, which is fairly strong. With this information, it is reasonable to conclude that more sacrificing could lead to more runs. However, a correlation does not mean causation, and in this instance the rather weak positive coefficient of determination (.64), is not enough to explain all of the variability from the mean. Therefore, college programs are misguided to believe that more bunts with solely contribute to more runs. There are several other factors that do, which is why the lack of a strong correlation here.

To hammer this one out of the park (pun intended), there is nearly zero correlation between the top120 scoring offenses in college baseball and their frequency of sacrifice bunts. The coefficient of determination, R-Squared, is 5.6E-6, and the correlation coefficient is .00236. Of the more than 300 college baseball programs, each school desires to be in the top 120. However, this shows here that these top offenses score in many different ways. Yes some, like Louisiana Lafayette, top five in both runs and sacrifice hits, ploy this model successfully, others score with power, speed, and their ability to get on base. By using all of the teams in college baseball in Part 2, it suggests that struggling offenses bunt more to attempt to score like the top teams. However, once again that team is misguided.

The Cleat Sheet concludes that although there is a positive correlation between sacrifice bunts and runs scored, it is not strong enough to deem significance. Therefore, college teams would be better off not sacrificing so many outs per game (29% more than 2010, not including failed sacrifice attempts), and should rather focus on developing players who can contribute to offensive production in different ways. In other pieces, The Cleat Sheet will examine efforts by the NCAA to counter the diminishing offenses, and we will comment on how this study can translate to different levels of the game.



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