Freddie Freeman had the best season of his career in 2016. He set career highs in just about every stat you can name: home runs, extra base hits, slugging, on base percentage, walks, BABIP and strikeouts. All of that sounded pretty great up until those last two, right? You wouldn’t be alone if you felt that way. Freeman has always been a good player, I doubt many have felt otherwise. It seems that every year Freeman marches into the season, puts up solid numbers at first base, shows off his leadership skills on and off the field, and then quietly goes into the offseason. This year was different, though. In 2016 Freeman had a true break out season worthy of MVP consideration. He had a substantial increase in performance across the board, in every major statistical category, and yet, there is still question about his ability to repeat this performance in 2017. All due, in large part, I think, to one little number: BABIP.
Freeman posted a .370 BABIP in 2016. This is significantly higher than his career averages .344 respectively, but it certainly isn’t unprecedented for a slugger to maintain a BABIP this high in the major leagues. Paul Goldschmidt, for example, has posted a .369 BABIP since 2014. J.D. Martinez has hovered around the .366 mark as well of that same time frame. Neither of these guys are horrible comps for Freeman. Neither are speedsters, we know that much. Although both are probably faster than Freeman by a decent margin, and I feel comfortable saying both are better power hitters as well. We know the key to BABIP is hitting the ball around 14-18 degrees vertically, and the harder, the better. Those sorts of hits will turn into singles pretty frequently, and if you can knock the launch angle up north of 24 degrees every once in awhile, you’ll hit home runs as well. It’s easier said than done, but, in general, that is how you’re going to achieve consistently high BABIP over multiple seasons. Otherwise you start introducing the concept of luck.
I have a few tools to examine just how ‘lucky’ Freeman may have been in 2016, though. xStats examines each batted ball measured by statcast, and determines the league average success rate by comparing it to similarly hit balls (those with similar exit velocity along with vertical and horizontal launch angle). So, for example, if the ball is hit 105 mph on a 22 degree vertical angle and 22 degree horizontal angle, xStats will compare that batted ball to all those hit between 104-106 mph and 20-25 degrees vertically and 20-25 degrees horizontally; count how many singles, doubles, triples, and homeruns were hit in that group divided by the size of the group; and then adjust these numbers for the running speed of the batter. So, these numbers should be, theoretically, neutral for both park and fielding effects. Read the rest of this entry »