Arin Tykodi ◆ November 6, 2017
Any fan of baseball can see that the game is changing in a drastic way. Home-runs are being hit like never before, rookie sensations seem to spring onto the scene every few months, and the amount of defensive shifts employed in the game is greater than ever before. What started this trend of adjusted defensive strategies? Moving infielders and outfielders to optimally position them to record an out is an ingenious idea that seems new to many people around the sport. However, it didn’t start as recently as many think. There have been reports of defensiveshifts dating back all the way to 1877, some notably applied to the greats such as Ted Williams and Cy Williams. The hot point of debated comes from the recent spike in shift usage, from 2,357 shifts used in 2011, to 26,700 shifts used in 2017. So, are these defensive mechanisms that have been so popular in the modern game worth all of the extra scouting and analysis? How much do they really affect the hitters yearly performances, and ultimately, how much do shifts affect a team’s chances to win?
Let’s lay out the basic information: a shift occurs when one side of the field is defensively favored by a team. In the modern game, this takes place most often by placing 3 infielders on one side of the infield, as seen on the left. This is known as a “traditional shift,” and has been plaguing pull-hitters for years. Let’s take a look at one of the most shifted against players since the major implementation of the shift, David Ortiz. In order to do this, we will compare his Offensive WAR (Wins-Above-Replacement) in the years of his career before and after 2011 (min. 500 PA). There is an obvious decline in Offensive WAR post-shift, suggesting that shifts may have had an impact on his success. However, looking at David Ortiz’s batting average against the shift and comparing it the league batting average overall, he was hitting well above league average. So what is the correlation? There are many extraneous factors that can be taken into account such as age, injuries, or opponents pitching performances, however, it does not appear that the shift had a perverse effect on the most shifted against player in baseball. In fact, the entire league hit for higher batting averages against the shift, for example, .299 against the shift vs. hitting .255 overall in 2016. Maybe the shift isn’t as effective as people give it credit for after all.
Next, let’s look at how applying shifts affects the winning percentages of the teams who use them the most. This year, the Chicago Cubs and The Milwaukee Brewers used shifts the least and most, respectively. Both had above average seasons, the Cubs having 92 wins (.568 Win%) and the Brewers having 86 wins (.531 Win%). The win percentages of these teams alone suggests that the frequency of use of shifts does not have a major effect on the outcome of team’s seasons. However, if we compare the average runs created by a player on the Brewers and the Cubs, we see that the average Brewers player was less effective. This means that shifts contributed to the Brewers having a comparable season to the Chicago Cubs, despite being an overall worse team.
All of these numbers call into question whether or not teams shifting actually affect player and team’s performances. However, using David Ortiz, the Brewers and Cubs as examples, it can be seen that shifts have the potential to benefit a team significantly, but do not define performance.
Exit Velocity and a Player’s Offensive Value
Jake Singleton ◆ November 2, 2017
The rise of technology in sports, particularly baseball, is having dramatic effects on how professional organizations approach the game. One of the newest and most upcoming statistics that point to how to hit more home runs and extra-base hits is exit velocity. Exit velocity is the speed with which the ball leaves the bat. Baseball’s most prolific home run and extra-base hitters typically average an exit velocity of 90+ mph, while the MLB average in this category comes in at around 87 mph. Players like Nationals’ second baseman Daniel Murphy are constantly tweaking their swings to find the motion that gives them the best chance of driving the ball with an ideal exit velocity. “You want to hit the ball optimally about 25 degrees at 98 mph,” Murphy said, “those are home runs.” Over the past three seasons, Murphy has been adjusting his swing constantly to hit the ball the way he wants. Mets hitting coach Kevin Long has helped Murphy to move up on the plate and reduce the disconnect between his back elbow and back hip. It’s been working, too. His exit velocity increased from 90.8 in 2015 to 91.3 in 2016 to 92.3 through the first month of 2017, resulting in a higher percentage of line drives. He has made the All Star team in 3 of the past 4 seasons, something he had never done before.
The scatterplot below shows wRC+ vs. exit velocity. wRC+ (weighted runs created plus) is a measure of a player’s offensive value using how many runs he contributes to his team relative to other players while taking park effects into account. The 20 players in the chart correspond to the 10 players with the highest exit velocities in 2017 and the 10 players with the lowest exit velocities in 2017 among all qualified hitters. There is a stark difference between the two groups. Generally speaking, those with higher exit velocities create more runs for their team than the league average with two exceptions, Kendrys Morales and Miguel Cabrera, who have been relatively unlucky this year, coming in with with BABIPs lower than their career averages.
The moral of the story is that as exit velocity increases, so does the player’s likelihood to produce more runs for his team. Thus, it is worth it for hitting coaches to consider taking a similar approach to Kevin Long and considering how to help his players hit the ball harder. In addition to Long and Murphy’s efforts, the Tampa Bay Rays have been at the forefront of using Statcast. They observed that almost all home runs are hit with a 95+ mph exit velocity (see the figure to the right). After working on hitting the ball harder and in the air throughout spring training, they ranked 3rd in hits, 2nd in home runs, 5th in batting average, 6th in slugging, and 5th in exit velocity among the fly balls they hit. At the time, outfielder Corey Dickerson was slumping but was still managing a 143 wRC+ against right-handed pitchers largely due to his average of a 94.9 mph exit velocity on fly balls.
Baseball has been and always will be a numbers game. With advances made in technology in recent years, MLB released Statcast in all 30 ballparks in 2015. With Statcast, teams can track a plethora of new stats in pitching, hitting, fielding, baserunning, and more. Among the hitting category is exit velocity. Every single team has access to this relatively new statistic that has the power to help them adapt into a stronger offensive unit since it can predict the offensive value of every single player. The teams that take advantage of these statistics and take the time to help their players adapt their swings will undoubtedly be the ones who reap the rewards.
Spending in MLB to Measure Efficiency
Arjun Srinivasan ◆ March 3, 2017
Calculating how much was spent to win a single game did not yield any significant conclusions about which teams spent most efficiently. For example, the Houston Astros spent less money to win each game than the Pittsburgh Pirates, but the Pirates won eight more games than the Astros. About 65% of the teams spent less, in 2015, than the average of 1,546,594.09 𝑑𝑜𝑙𝑙𝑎𝑟𝑠 to 𝑤𝑖𝑛 win a single game, and 70% of teams that made the 2015 playoffs spent less than this average value. 61% of teams that did not make the playoffs spent less than the average value to win a single game. Upon deeper examination of these metrics, we can consider another interesting metric. If, on average, it costs $1,546,594.09 for a single MLB win, then each dollar spent in the MLB generates an average of 6.47 × 10−7𝑤𝑖𝑛𝑠. Mathematically, ($1,546,594.09)−1 = 6.47 × 10−7𝑤𝑖𝑛𝑠. This number is critical, as it allows us 𝑤𝑖𝑛 $ to calculate the expected number of wins for each team. By multiplying the average wins per dollar by the payroll of each team, we can see how closely each team matched its expectation of wins, based on the amount of the money they spent. The results are shown in the table below.
This table reveals some interesting results about the 2015 season. For instance, even though the Dodgers and Yankees made the playoffs, they were the two teams with the largest difference of wins when compared to their expected win totals. This is likely correlated with the fact that these are the highest spending teams in the league. Additionally, although the Rays, Marlins and Indians, won less than half of their games, they were all in the top 5 of the difference between their wins and expected wins. Furthermore, 7 of the 10 playoff teams had positive differences between their wins and expected wins, indicating that teams that spend money in a wise manner are usually the ones that end up in the playoffs. Finally, this table can help answer the question of which of the Pirates or the Astros, both teams that spent less than 100 million dollars and made the playoffs, were more efficient in spending their money. Based on our table, the Pirates finished with a 1-win advantage over the Astros, indicating that they were more efficient with their spending.
Edited by Derek Topper, Sports Analytics Group at Berkeley