The shift has taken the MLB by storm in recent years. Broadcasters love to criticize the shift, despite its numerous advantages. One potential problem that the shift may cause is an increase in fielding errors. This may be a direct result of fielders playing out of their normal position. Using the shift data provided to FanGraphs courtesy of Baseball Info Solutions, as well as batted ball data courtesy of Baseball Savant, I ran a logistic regression to find the likelihood of a batted ball resulting in a fielding error.
The approach I used to find the probability of a batted ball being a fielding error was to run a logistic regression. The variables included in the regression were release speed, hitter-pitcher matchup (dummy variable with a value of 1 if the pitcher and hitter were both righties or lefties), runners on base dummy, launch speed (exit velocity), effective speed, launch angle, and dummy variables for both traditional and non-traditional shifts. The model only included batted balls that were hit in the infield, as the majority of shifts occur in the infield.
Above are the results of the logistic regression used to determine the probability of a batted ball being an error. The dependent variable is whether or not the error occurred. Two results that logically make sense are Exit Velocity (Launch Speed) having a positive coefficient and Launch Angle having a negative coefficient. Both of these variables are significant on the 1% level. Exit Velocity having a positive coefficient shows that the harder the ball is hit, the harder the ball is to field. Launch Angle has a negative coefficient, meaning that the lower the angle (meaning a ground ball over a fly ball) the more likely the fielder is to commit an error. Both of these results are logical, and are consistent with research that has been conducted in the past. The most interesting results from the model are both traditional and non-traditional shifts leading to an increased likelihood of an error occurring. Both variables were statistically significant on the 5% level, and prove that players struggle more in the field when playing out of their normal position.
While teams are unlikely to change their shifting patterns (more good comes out of the shift than bad), they must take into account which fielders are worse when playing out of position.
Despite the increased probability of an error occurring, I still believe that the positives out weigh the negatives when it comes to shifting. In future research, it would be interesting to look at this data on a minor league level, as well as seeing if fielders who shifted more in the minors are more prepared to field out of position in the majors.
Evan is a Junior studying Sport Analytics at Syracuse University. Follow him on twitter @evanweiss10