Swing Quality Metric-A Baseball Hitting Revolution

With spring training heating up for baseball, I thought it would be a good time to do some research into the subject, in particular looking at some advanced analytics for batters. This information was presented at the Sloan Sports Conference and can be found at the following link, then clicking on the paper PDF: http://www.sloansportsconference.com/?p=2834. The Sloan Sports Conference is an annual conference held at MIT and is known as a sort of “sports geek” convention where all the brightest statisticians and proponents of sports analytics gather to share innovative ideas on how to use advanced statistics to better understand their sports. The paper I will discuss here details an innovative way of looking at baseball hitter’s abilities beyond both traditional statistics and modern advanced analytics. Baseball is a sport that has historically lent itself better to statistics than any other dating back to its inception, and the statistical movement within the game took a giant leap forward with people such as Bill James in the 1980’s and the success of the Oakland Athletics using advanced statistics as detailed in the book Moneyball.

The paper first details the current use of advanced batter metrics, most of which utilize the system known as Pitch F/X to detail how often batters swing at pitches in or out of the zone as well as types of batted balls by detailing Fly Ball, Ground Ball and Line Drives percentages. Although these stats have helped people better understand hitting than traditional stats like batting average, the authors of the paper believe the advanced stats do not go far enough in their analysis. Specifically, the authors detail the lack of use of pitch types, pitch velocity and pitch location within or outside the strike zone. Their paper presents the first statistic to utilize all three factors, which they term “Quality Swing Metric”

Derrek Lee-source: friarsoncardboard.blogspot.com

The Quality Swing metric is calculated by looking at a player’s OPS (On-base plus slugging percentage) for each of 7 different pitch types at 7 different velocities in 16 different zones within the hitting area. An example would be a fastball, between 85 and 90 mph in zone 7 (upper middle, inside middle). OPS is a recent statistic generally considered to be a good indicator of a batter’s effectiveness as it looks at their abilities to both get on base (OBP) and hit for power (Slugging %). The case study done to illustrate the Quality Swing Metric’s effectiveness looks at the performance of veteran first baseman Derrek Lee, who enjoyed a great season in 2009 but had a rather poor 2010 campaign. According to usual advanced metrics, Lee’s performance in 2010 was essentially the same as in 2009. In fact, he had a higher Line Drive % and lower Infield Fly Ball %, suggesting his numbers should have actually been better in 2010. However, a deeper look into his performance in the years studied indicated he performed significantly worse against all types of pitches at different velocities in different zones. The paper speculates the reason could have been due to injuries or simply declining skills, but the Quality Swing Metric clearly shows he performed much worse than the current advanced stats would suggest.

 

 Pitch Zones-source: Gore and Snapp, "A Major League Baseball (MLB) Swing Quality Metric

 

I believe this statistic has the potential to revolutionize the industry and give a great insight into a batter’s true performance. A poor performance against certain pitch types could indicate a hole in the player’s swing they can fix in the offseason to better their games. It could also tell pitchers exactly which types of pitches to throw batters. However, I do think there are some flaws in the system. I believe they need to take things one step further and look at performance against all pitches in an at-bat, not only the final pitch. A batter who swings and misses at two straight low and away curveballs, then strikes out on an inside fastball would look weak against the inside fastball when in fact it was the lack of success against the curveballs that put him in the 0-2 hole in the first place, which is an extremely difficult count to hit in. Another flaw I see is the lack of distinction between four-seam and two-seam fastballs in the pitch types. The difference can be enormous, as a 95 mph four-seam with no movement is much easier to hit than a 93 mph two-seam with four inches of movement. The smallest movement can cause a swing and miss or more likely move the ball away from the sweet spot of the bat, resulting in weaker contact. Incorporating this into the tracking system would give a better sense of how batters are truly performing against fastballs. In conclusion, the Swing Quality Metric, although it has some flaws, is a potentially revolutionary way of looking at baseball hitting analytics which smart teams will use in player evaluation and scouting reports to perform better during games.