Martinez, in particular, worked with Sosa to revamp his swing to emphasize an increased launch angle and, as they say, the rest is history.With Sosa in his back pocket, and the early returns looking good, Ramos could be poised for a breakout 2020. Should he not fix his ground-ball issues, he’ll still continue as an offense-first backstop, a post that he’s held for the last decade.But if he implements these changes, he could enter into the upper tier of MLB catchers.Hitting a ton of ground-balls is an approach that Ramos has succeeded with in the past, but with an optimal launch angle, he can be even better in 2020.Hello everyone, my name's Matt Mancuso and I'm studying business administration at TCNJ. With that caveat out of the way, let’s look at how various metrics correlated with running splits, starting with batting:It is interesting to note that almost every metric began somewhat clustered and spread apart to a greater degree over larger running splits but reached a relatively stable point fairly quickly, between 20 and 30 feet. This is tracked for all Batted Ball Events -- outs, hits and errors. Either way, stolen base rate certainly stands out on the above graph. All switch hitters were averaged to find one value while all other players’ splits were left unchanged based on handedness. Fast baseball players tend to hit ground-balls. It is important to note that I’ve transformed DRS, UZR Range Runs, and Outs Above Average to represent rate stats per 1000 defensive innings to create a more fair standard of comparison. As with bunt hit rate, it appears that Sprint Speed is much more closely related to how frequently a player attempts speed-related actions than how successful a player typically is at said actions. We’ll first look at how Sprint Speed relates to a handful of batting metrics, including triples, bunt hits, and infield hits. Intuitively, it would seem to make sense that players slow down as they age, but I was curious to investigate what the data said about the rate and pattern at which players lose a step. These take the same start_dt and end_dt arguments as the statcast function, as well as a player_id argument. This may be in part because speed matters less on easier plays and that its small impact mainly depends on short initial bursts of speed.While none of this research is conclusive or definitive, it is certainly interesting to look at how speed ages and relates to various other aspects of the game typically tied to speed and may help give us an idea of lenses through which Sprint Speed may be appropriately viewed. For a player’s seasonal average, the following two types of plays currently qualify for inclusion in Sprint Speed. It should also be said that while this data is meant to represent how speed typically ages, this won’t line up perfectly for each and every player as every player ages slightly differently.
The following table breaks down each category of batting performance, the qualifications players had to meet to qualify for the metric, and the correlation values between a player’s average Sprint Speed from 2015 to 2018 and their respective batting statistics:As you can see, the degree to which Sprint Speed correlates with measures of performance at the dish varies widely by metric. Initially, I thought this might have in part been due to a lower bar for qualification, but even when the minimum number of stolen base attempts was raised higher there was a limited correlation. Now that we’ve examined how Sprint Speed relates to various other speed-related metrics, it’s time to examine how these relationships play out across various running splits.While a player’s speed does generally correlate with other factors of their game on the basepaths, in the batter’s box, and in the outfield, it is worth investigating how a player’s speed over certain distances impacts these abilities. Here’s a look at the correlations for each metric among all players with at least 1500 defensive innings in the outfield over the last four seasons:DRS and UZR range runs each possess a moderately strong positive correlation with Sprint Speed, indicating that speed is at least somewhat predictive for these measures of defense. In addition to providing players' times, in seconds, on home-to-first runs, Statcast breaks down these runs in five-foot increments up to 90 feet. Additionally, since I wanted to isolate a player’s outfield defense in a primarily range-focused context, I removed ARM runs from DRS and used only Range Runs from UZR. Two-run homer off Tyler Bashlor. It was interesting to note that the degree to which Sprint Speed correlated with plays made declined sharply as plays rose above a 60% likelihood of being made, suggesting that speed matters less when it comes to easy plays in the outfield than for more difficult ones. This category peaks with a weak correlation around the 10 foot mark before consistently displaying a weaker correlation as distance increases. I created a database full of player stats and their speed metrics, then set out to answer these questions, starting with how speed ages.As previously mentioned, it seems obvious that a player’s speed would decline as the player ages, but I was curious whether this was the pattern and at what rate players typically lose speed. Finally, I was curious whether specific running splits were more predictive than others for these metrics – for example, is speed over a shorter distance more important for stealing bases than speed over the full 90 feet? Inside Statcast.
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