It’s been Statcast’s average fly ball distance (Avg FB Dist) week, as I introduced the metric on Monday and then listed and discussed names whose Avg FB Dist marks suggest there is either extra stuff going on or some missing ingredients in their batted balls. Today, let’s dive into fantasy relevant players whose Avg FB Dist marks surged the most versus 2016.
Yesterday, I introduced you to Statcast’s batter average fly ball distance (Avg FB Dist), which is one of the new components of my recently updated and improved xHR/FB rate. Today, I’ll continue to swim in the Avg FB Dist pool, this time listing and discussing specific players.
Last week, I unveiled the newest version of my xHR/FB rate, which incorporates two Statcast metrics. The first of those components was Barrels per True Fly Ball Rate (Brls/True FB), which I introduced last Wednesday, and then discussed further on Thursday. Today, I’m going to dive into the other Statcast metric, Average Fly Ball Distance (Avg FB Dist), which is found on the Statcast Search page.
Earlier this month, I asked our readers for any aspects of the fantasy game which are missing. Okra stepped up and said:
“I feel like we still do a poor job of predicting stole bases. I think we could better utilize the new Sprint Speed data and speed scores to predict SBs. Taking it one step further would be to try and quantify each managers propensity for SB attempts.”
This statement is 100% true. We really don’t know which measurable factors fantasy owners should focus on when looking for stolen base breakouts. I’ve gone ahead and dived into the topic of just the manager influence with positive results.
On Tuesday, I unveiled the latest version of my xHR/FB rate, and introduced a new metric that replaced the Brls/BBE equation component. Technically it’s new, but it’s not unfamiliar, as I simply swapped out the BBE denominator for true fly balls, which is just the fly ball total found here, minus pop-ups (IFFB). Yesterday, I dove deeper into the new Brls/TFB metric, sharing some data on my player population and then listing and discussing some names affected by the switch. Today I will continue on that Brls/TFB course with more fun stuff.
Since it was first introduced about a year and a half ago and published on the Statcast Leaderboard, Barrels quickly became one of my favorite metrics. Combining exit velocity with launch angle made it the perfect statistic to reference when investigating a hitter’s power potential. Since we like ratios better than counting stats for projection purposes, Barrels per Batted Ball Event (Brls/BBE) was my metric of choice. But as informative as it remains, I discovered that it actually wasn’t the best ratio when it came to forecasting HR/FB rates.
Nearly three years ago, I unveiled my original xHR/FB rate. Back then, Statcast was in its infancy, and wasn’t installed in all 30 stadiums until the upcoming season. As such, my original equation used metrics that Jeff Zimmerman provided me from scraped Gameday data, I believe. The equation was solid enough, producing a 0.649 adjusted R-squared. Clearly, there was more work to be done, but sadly, the data required to make improvements simply wasn’t available.
With fantasy owner always searching for an edge, I may have found one buried deep in spring training stats. While looking for Yonder Alonso’s MLB.com player page, I noticed he had some batted ball data in the form of GO/AO (groundouts/air outs). In Alonso’s case, his GO/AO value had always been greater than 1.1 until last season when it dropped to 0.87. In the regular season, Yonder’s groundball rate plummeted from 44% to 34%. Yonder admitted to making a swing adjustment and that change should be detectable in spring training. By comparing spring batted ball data, fantasy owners can get an idea of those hitters who may be ready for the flyball revolution.
Note: While flyball and line drive rates are available for comparison, I will only use groundball rate (GB%) because it stabilizes quicker, has less stringer bias (tough call between some line drives and flyballs), and is only one set of benchmarks to memorize.
I’m going to have two foci for this article. Part 1 is all math and disclaimers. It’s the process I used to go from GO/AO values to ground ball rates to launch angles. Part 2 contains the results from Part 1 as a simple procedure for finding launch angle breakouts.
Part 1: Math and Disclaimers Read the rest of this entry »
Nearly a year ago, I introduced the newest version of my xHR/FB rate, this time using the new Statcast metric, Barrels per Batted Ball Event (Brls/BBE). Along with Brls/BBE, I included two additional components — pulled and opposite field fly ball percentage, which I just lumped together for the second factor in the equation (oh, and also a park factor adjustment).
With three seasons in the Statcast Leaderboard books, it’s time to dive in to its short digital history. While the various metrics published on the site are sure to provide a better understanding of what drives a handful of statistics, I’m most interested in what fuels home runs. The leaderboard shares a number of interesting metrics, several of which would intuitively correlate with HR/FB rates. Given the leaguewide home run surge — for the second straight year, HR/FB rate hit a new high since 2002, the first season FanGraphs has batted ball data — we might find some answers on the virtual pages of Statcast’s records.
