For decades, the only sources of information used to make decisions in the draft were scouts and their reports. Scouting staffs blanketed the country, at times aided by the scouting bureau, bird-dog scouts, and any other local trusted sources they might use to tip them off to where there might be draft-worthy talent. Scouts were resourceful and accurate judges of both ability and player makeup (a catch-all term in scouting used to describe fortitude, perseverance, baseball IQ, etc). These two data sources- scouting reports and the scout’s opinion on player makeup- were the only reliable streams of information for scouting directors to reference to put together draft boards.
While scouts were always aware of player statistics and performance, there was no statistical database to reference in the draft room (let alone analysts to perform deep-dives on players using advanced metrics). With the advent of the internet and centralized record-keeping (at sites like the Baseball Cube and College Splits), teams finally had consistent access to robust college performance data. As analytical teams made use of more advanced statistical data to make decisions on major-league players (hello, Moneyball!), progressive organizations expanded their analysis to use college statistics to aid them in making draft decisions. Like any competitive edge in baseball, other teams quickly followed suit. Today, advanced analysis and draft modeling is no longer a competitive advantage- this stream of data is a necessity to keep from falling behind.
While there will always be competitive edges with insightful scouts and sharp analysts, scouting departments and decision-makers are now on a constant hunt for new sources of consistent, reliable information that they can use in their decision-making that might give them a leg up over their scouting competition. Still, most teams are operating on the same 3 data sources- talented scouts evaluate the same players, they get similar reads on these players’ makeup, and smart, dedicated R&D departments.
However, new data streams are where teams can differentiate themselves, and often, emerging technology is leveraged to try to gain a competitive advantage. Early adopters of technology in the draft process used TrackMan and Rapsodo information (for pitch and batted ball data) to give themselves a leg up in making draft picks, and many teams now use high-speed cameras to get even more granular information about pitch characteristics and biomechanics.
Often, it’s a leap of faith for a club to use a novel technology in their decision-making process: by definition, a new tool hasn’t been vetted to prove how predictive it actually is, and in applying it to draft decisions, the team adopting it has to use it arbitrarily until there’s enough proof to plug it into a draft model where it can be applied more consistently.
This is where Vizual Edge stands out: we’re an information stream that organizations can take advantage of outside of their scouting and internal analysis, but also one that’s been available over 15 seasons. In that time, we have collected the data to objectively measure the importance of, say, an above-average convergence score in a young hitter, or what it might mean if a player’s depth perception is below the mean.
We’ve already released some of our findings on how strong Vizual Edge metrics lead to success in the major leagues (and we’ll have more on the 2020 season soon); in the weeks ahead, we’ll be releasing more information from our internal studies on how it relates to the draft and college baseball.
And it should empower young hitters to know that, like a flaw in their swing, visual characteristics can improve with time, training, and discipline with our software and training program.