Taking a Second Look at Defensive Analysis by MT January 29, 2016 The game is on the line. It’s the bottom of the 9th inning, runners on first and second with two outs for the Mets. Justin Turner drives a fly ball off the bat at a speed of 88.3 mph. All hope for the Braves looks to be lost. In a blink of an eye or just .02 seconds Jason Heyward reacts and races out of center field traveling 18.5 mph to make an incredible diving catch to save the game. This data set was one of the earlier Statcast recordings released to the public. It shows how important such information could potentially be to clubs in the future. Statcast can record data such as Acceleration, Route Efficiency, Reaction Time, Max Speed, Distance Covered and more. Although not all of their data is available to the public, I wanted to further explore how a baseball club would benefit by using this technology to research defensive analysis on improving a player’s abilities and a club’s defensive positioning. First off, a team could compile this data and separate each player’s metrics by direction. Players move differently when heading in different areas of the field. It’s obviously easier to move forward than running backward, so having this data would allow teams to identify key information and make comparisons down the road. This can be done so by separating a fielder’s range into eight different quadrants (see graphic below). Once that is done, averages are created based for each quadrant. For instance, on average, what is Brett Gardner’s route efficiency when moving right? When moving in quadrant 6, what is Charlie Blackmon’s average reaction time? Quadrants #1: Forward #2: Right Forward #3: Right #4: Back Right #5: Backwards #6: Back Left #7: Left #8: Left Forward All this information, separated into different quadrants, will help in visualizing and breaking down defensive ability. When we have averages of acceleration, max speed and reaction time it can create a visual graphic or “Statcast Range” to witness how much distance a player could potentially cover in a certain amount of time. For example, lets say Jason Heyward’s average reaction time, acceleration and max speed when going left was .02 sec, 15.1 ft/s^2 and 18.5mph respectively. We know using this information Heyward could cover approximately 81 feet in 4 seconds. Time can help us represent a player’s estimated “Statcast range.” Each player’s range will look differently as they may show in which directions they are better at fielding. We can then use this analysis to compare fielders and also adjust defensive positioning. Example of what Jason Heyward’s range may look like This information will help guide a team in improving its players’ abilities. Teams can compare players much easier and understand what flaws coaches must look into fixing. For example, if a fielder has below-average route efficiency or reaction time to a certain part of the field, this information can be relayed to the coaching staff to further improve a player’s ability over time. In order to put this in perspective, Eugene Coleman of the University of Houston found that the average major-league ballplayer ran 24 feet per second. Using this number, having 0.04 more seconds means the average major leaguer can cover 11.5 more inches of ground. That’s almost a foot more and within only .04 seconds. If a ballplayer cuts down his reaction time, improves his route efficiency, and more, he would be able save time in covering several more feet of ground and thus improving his defensive ability. To adjust a player’s defensive positioning, a team would have to combine its knowledge from this analysis with the understanding of a hitter’s batted balls. If they know a certain player is a pull hitter and hits to certain parts of the field, they can track his batted-ball locations, hang time and exit velocities to project areas in the field to which he may hit. Using what we know about a fielder’s Statcast metrics and “Statcast Range “ a player’s positioning could be adjusted. Doing so would lead to more accuracy. Improving the range of a team’s fielders will help save distance and time. The ability to increase production of more outs will provide a club with a better advantage for winning the game. To try and go more in depth on my theory, I took a quick look at Brian McCann’s heat map from the past couple years (courtesy of BaseballSavant.com). It includes all singles, doubles and triples. I choose this because these are all the plays that weren’t recorded for an out and for the sake of my argument I am using this as an example. McCann is a notorious pull hitter and teams usually play the shift against him which fits my point. With pull hitters, like McCann, it’s easier to predict where they will hit, compared to a spray hitter. When teams are confident in certain areas of the field opponents hit to, they can analyze the “Statcast Range” based on each fielder to adjust defensive positioning. We might be able to align our “Statcast Range” with something like a player’s heat map to give us further indications where to field. With more research, I’m confident we will be able to find better spacing to move fielders around and cover more area. Each player is different and the ground that they’ll be able to cover will depend on their abilities. I think we cannot only take advantage of our opponents’ weaknesses but also our defenders’ strengths. When we have more specific data I think it will shed more light on what we can accomplish. Further analysis must be done to gather more information to investigate the strategy between a fielder’s “Statcast Range” and a hitter’s batted balls. Since Statcast’s data is limited for public use, it’s hard to further dive into its potential. But from what we know at this point, every millisecond and foot we can cut down on is a step in the right direction.