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The potential effects of a ban on the infield shift

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Commissioner Manfred has stated that he is open to a ban on the infield shift in order to bring more offense into the game. Would such a ban accomplish this goal and if so, by what magnitude?

Mark L. Baer-USA TODAY Sports

Shifts There is no denying the impact that shifts have on the production of hitters with strong pull tendencies. New commissioner Rob Manfred made some noise this weekend when he stated that he was open to the idea of outlawing the defensive shift to inject some offense back into the game. What impact would this have on the game? How would the run scoring climate change if teams were prevented from shifting? How would it change if teams started shifting more? How would these changes affect individual players?

There are a variety of ways to measure the impact of shifts. A common approach is to examine batting average on balls in play, as it is easily calculated and intuitively should be affected by shifting. However, BABIP is affected by a variety of factors, and shifting is only one of them. Alternatively, one can use data from Inside Edge, which tracks all shifts and determines if a shift resulted a saved hit for the defense, a hit allowed for the defense, or if it had no impact at all.

In an early September article for the Wall Street Journal, Steve Moyer shared some of Inside Edge’s data on team shifting statistics to that point in the 2014 season. This batch of Inside Edge data can be used to arrive at some rough estimates to answer questions about how shifts affect the game.

The table below represents data from Moyer’s article, but prorated to a full season worth of games, hence the decimals:

Team Shifts Net Hits Saved
Astros 1874.4 52.8
Giants 592.8 30
Blue Jays 1135.2 26.4
White Sox 819.6 26.4
Royals 834 25.2
Athletics 775.2 24
Mariners 759.6 24
Orioles 990 20.4
Indians 848.4 20.4
Brewers 760.8 20.4
Red Sox 744 19.2
Yankees 1140 19.2
Twins 639.6 18
Rangers 823.2 16.8
Tigers 352.8 15.6
Angels 608.4 15.6
Cubs 554.4 14.4
Braves 444 13.2
Padres 410.4 13.2
Nationals 309.6 13.2
Dodgers 355.2 12
Phillies 385.2 12
Cardinals 508.8 10.8
Diamondbacks 423.6 6
Mets 352.8 4.8
Rays 1233.6 4.8
Rockies 297.6 -1.2
Pirates 991.2 -2.4
Reds 404.4 -3.6
Marlins 370.8 -3.6

Adjusted to a full season, teams averaged 691.3 shifts, which netted them 15.6 saved hits. The Astros lead the majors tallying 1874.4 shifts, which resulted in 52.8 saved hits. Meanwhile, the Rockies shifted only 297.6 times, allowing a net of 1.2 extra hits. However, it is clear that more shifting doesn’t guarantee more net hits saved: The Pirates and Rays collectively shifted 2,224.7 times, but only saved a net of 2.4 hits! There is observed variation in shifting efficiency, as the Rays and Pirates saw almost no savings on their collective shifts, while the Astros saved over 50 hits (more on the variation in shifting success later). Despite the variation, number of shifts and hits saved are positively correlated, with a correlation coefficient of .63:

Shifts

What if the shift is outlawed? There is a fine line between shading and shifting. Without complete knowledge of what Inside Edge considers a shift and what the Commissioner’s Office considers a shift, it is impossible to assess the exact impact.

Let us assume for simplicity that a new rule would forbid exactly the shifts considered by Inside Edge. Prorated over a full season across all teams, the data suggests 20,740 shifts netting the defense 468 hits saved. Suppose the new rule prohibits exactly these 20,740 shifts and those 468 balls in play poke through the infield resulting in 468 singles. If Tom Tango’s linear weight of .45 is used for a single in a 4.00 RPG environment then this comes out to 210 extra runs league wide. This would result in an extra .04 runs per team per game across the league, nudging the 2014 MLB average from 4.07 to 4.11, still below 2013 numbers. As Manfred suggests, this would inject some offense back into the game, but clearly not to the levels fans are accustomed to from the late 2000’s. Even in 2010, league average was 4.38 runs per game.

It is doubtful a new rule would restrict a defensive alignment that Inside Edge did not count as a shift. Thus, this can be considered to be the upper limit to the increase in offense due to a shift ban. With that said, the rules could be less strict. Here is a table summarizing the possible effect at each level of "strictness" for the proposed ban:

100% of Inside-Edge Shifts Outlawed 75% of Inside-Edge Shifts Outlawed 50% of Inside-Edge Shifts Outlawed 25% of Inside-Edge Shifts Outlawed 0% of Inside-Edge Shifts Outlawed
Extra Hits 468 351 234 117
Extra Runs 210 158 105 53
Extra RPG 0.04 0.03 0.02 0.01
New RPG 4.11 4.10 4.09 4.08 4.07

Taking away shifts would change the climate of the game, but would it disproportionately affect some teams and not others? Moyer suggests not as much as one might think. In the article, he warns that it is not correct to conclude that certain teams are better at shifting than others, because there is simply a healthy amount of random variation. This means the Rays and Pirates should not be considered worse shifters than the Astros, but rather that they were not as lucky as the Astros. Moyer writes that players are beating poorly employed shifts as much as they are avoiding them simply because "the ball just went to a place where it normally wouldn’t -- the result of a check swing, a broken bat, or dumb luck."

Since good and bad shifting teams are tough to identify, it can be concluded that the teams that would be hurt the most from a ban are simply the teams that shift the most. In 2015, the Astros would be hurt more than the Rockies, but it is not as if the Pirates and Rays wouldn’t be hurt in 2015 either. A team cannot be concluded to be bad shifters just because the ball did not bounce their way in 2014.

To assess the impact of shifts, it will be useful to establish an exchange rate whereby there exists some Y number of hits one can expect to save given X number of total shifts. A linear model can be used to predict net hits saved with total shifts at league average efficiency. Based on the scatter plot above a simple linear fit will suffice. The resulting model takes the form:

Predicted Hits Saved = 1.18 + .02(Number of Shifts)

Our model is not perfect, yielding an R-squared of .38. However, Number of shifts is a statistically significant predictor with a p-value of .00018 making our model valid. The model can used to predict how many hits would be saved by a given number of shifts executed with league average success.

Without a ban on shifting, it follows basic economic logic that the rest of the league will observe the Astros' advantage and begin to follow suit shifting, over 1,800 times a season. Given our model, which assumes league-average success, one would expect a team that shifted as much as the Astros to save 40.3 hits in a full season compared to the league average of an expected 15.6 hits saved in a full season. If all teams shifted this much, one would expect a decrease in hits to the tune of 24.7 hits per team and 741 hits league wide. Again using linear weights, this translates to 333 runs league wide. This would result in .07 fewer runs per team per game across the league, bringing the league average down to 4.00. Let us assume that the Astros shift at the maximum amount before there are diminishing returns. This is a generous assumption for the Astros, but assuming more shifts and the same rate of hits saved per shift would be extrapolation. Thus, for our purposes the Astros' 1,874 shifts in the season represent the upper bound for often a team can shift.

Now, let's look at a world where every team shifts like the Astros, the current world where everyone shifts as they do, and a third world where shifts are banned can be compared. Here is a table summarizing the estimated effect at each level.

Shifting at Astros Level Shifting at Current League Average Level No Shifting Allowed Net Change
Number of Shifts 1,874 691 1,874
Extra Hits -741 468 1,209
Extra Runs -333 210 543
Extra RPG -0.07 0.04 0.11
New RPG 4 4.07 4.11 0.11

These are rough estimates, but provide a pretty good idea of the impact a shift ban could have on the run-scoring environment. If shifts were banned tomorrow, one could expect to see run scoring rise by .04 per team per game to 4.11. However, if teams shift closer to the optimal amount, like the Astros have in 2014, this would result in run scoring dropping by .07 per team per game to just 4.00. Thus a new ban could prop up future run scoring by almost just a little more than a tenth of a run per game.

Shifting has become part of the game, one championed by small-market teams looking for an edge. However, to say that eliminating shifts would cause an imbalance in the game is short-sighted. While we do see a wide variety in number of shifts employed right now, it is safe to assume that within five to ten years, the league will reach an equilibrium in which no one team enjoys an edge by shifting. Whether Rob Manfred issues a ban on shifts or not, any advantage will be wiped out.

Manfred’s comments should not be taken as an attack on sabermetric thinking, but as the beginning of a solution to something both he and others have identified as a problem with the current state of the game. Banning shifts would fall far short of returning the game back to a five-run-per-game environment, but it would be a step toward a higher run-scoring environment. If the Commissioner’s office wants to take this opportunity to promote offense in the game, it is completely within their right.

Daniel Meyer is a junior at Colby College and Contributor of Beyond the Box Score and The Hardball Times. You can follow Daniel on twitter @dtrain_meyer.