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.
Hard to believe January is already half finished. Maybe even harder to accept that there are a significant number of talented MLB free agents left unsigned. As we wait through the long winter days, I’m looking forward to seeing these players in action in 2018.
Several years ago Alan Nathan, Jeff Kensrud, Lloyd Smith, and Eric Lang brought an air cannon and a few boxes of brand new baseballs to Minute Maid Park. If you’re anything like me, you like where this is going. They set up their cannon to fire balls roughly 96mph on a 28° angle and used Trackman to measure their distance and spin rate. They tested four groups of balls, two groups composed of MLB balls, one MiLB, and one NCAA. One group of MLB balls, group A, were tested using reasonably low spin rates, about 1800. The other, group B, had variable spin rates, ranging from 2100 to 3300. The results of their study were published in an article titled How Far Did That Fly Ball Travel (Redux)? on Baseball Prospectus, although it can also be found here. I encourage you to read the piece, but today I want to focus on the MLB-A and MiLB groups.
| Ball Lot | Distance (S. D.) | Spin (S. D.) |
|---|---|---|
| MLB-A | 390 (8) | 1806 (58) |
| MiLB | 362 (8) | 1583 (49) |
The major league ball traveled 28 feet further than the minor league ball. Albeit with a higher spin rate. Presumably, the higher spin rate should translate to increased distance, but it is difficult to imagine that a difference of 200 rpm could bridge a gap of 28 feet. More on this in a moment. Read the rest of this entry »
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 »
I’ll never be confused for a Byron Buxton fanboy. The tremendous hype of being a top-2 prospect for three straight seasons set outsized expectations that few could realistically meet. Buxton fell waaay short, though. He totaled just 138 games in 2015-16 with a paltry .220/.274/.398 line including 12 HR and 12 SB in the 469 PA. He was even worse for the first three months of 2017, hitting .195/.272/.280 through 78 games (263 PA). From there, he started a six-game hitting streak before going on the DL only to return as one of the league’s best players for the final two months. How’d that happen?