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The SwingD app is another example of how we are challenging the status quo across all spectrum of the game. We are a data driven company and modern analytics companies have fallen short in providing usable content as it relates to ** implicit training**. Every hitting app and analytics tool on the market today is designed for explicit use only. Which means that the players and coaches look at the numbers collected and watch video analysis and make cognitive adjustments to what they are seeing and interpreting. Since hitting is the brains assimilation with time and space at nano speeds its completely illogical to think that explicit drills and approaches can improve performance.

Consistent progress in performance in the only way to validate and establish cause and effect correlations. A hitters personal progress while training on V-Flex can be easily measured and monitored using our app (SwingD) It is the baseline measurement tool we use to track a hitters performance. It is a beautifully designed app that is simple and easy to use. The real time data is a valuable tool for understanding how a hitter is perceiving and processing strike zone spaces. It gives coaches and players a unique view of pitch efficiency during practice and games. Correlations become apparent after a few practice and games.

The features for collecting batting practice data are specifically designed for implicit training protocols. Which means verbal instructions aren't given during implicit batting practice. There are numerous setup and variations of spatial information that the hitters brain has to adjust to but they aren't cognitively engaging in mental approaches or exercises and they aren't working on hitting in certain pitch counts. They are simply seeing and processing different types of strike zone spaces. Space is expressed as ubiquity and counts have no influence upon it. Therefore counts do not determine if a pitch is a ball or strike, its location in a given space does. Basic ball and strike outcomes are tabulated using the SwingD app and adjustments in training are made according to efficiency variations. www.SwingD.com

The developer of our App is Dr. Les Anderson. Listen as he and Jenny Dalton-Hill talk about when and how to use collected data.

*Click here to watch in fullscreen on Youtube*

Impact of Strike Recognition By Dr. Les Anderson

Our game, really, revolves around one axis, one main premise; swing at strikes, take balls. I know this sounds stupid but I have struggled with the EXACT best term to use when describing the ability of a hitter to differentiate between a pitch that is a strike and one that is a ball. Pitch recognition isn’t really correct. Most in the game think of pitch recognition as a hitter recognizing the difference between a drop, rise, curve, screw, change, etc. At the core of the response is does a hitter initiate locomotion, does the hitter recognize a pitch, any pitch, as a strike and swing at it? So, strike recognition seems to be the most logical term to use. But how do we describe it? How do we quantify it? And most importantly, how to we improve it.

Unfortunately, college softball does not have a database like MLB. We don’t have pitchf/X. We don’t have an endless database of all pitch locations, pitch speeds, pitch movements, and outcomes including ball exit velocity, launch angle, distance or a simple swing and miss. Since we don’t have the ability to mine such a rich database, I had to create one. I began collecting data in 2015 by watching video replays of D1 games available either on-line or on television. I have watched and recorded each pitch and each outcome of 469 games. These games include all the games from the 2015, 2016, and 2017 WCWS, all the Super Region games from 2016 and 2017, some of the Region games from 2016 and 2017. The remaining games were either conference tournament games or regular season games from the SEC, PAC 12, Big 10, Big 12, and ACC. Most (80%) were from the SEC and PAC 12. The data include three main points: 1) was the pitch a ball or a strike, 2) what was the outcome (swing and miss, take, contact, 3) was the contact hard (middle barrel, middle ball). Hard contact was not quantified by any quantitative measure (ball exit velocity, etc) but I also did not care if the hard contact was a grounder, line drive, or long fly ball or if it was a hit or an out. So, a hitter was credited for hard contact regardless of whether the contact resulted in a hit or an out. I then imported the game data from the NCAA website so that I could learn if strike recognition was associated with game performance. In total, data was analyzed on 53,811 pitches, 13,935 AB’s, in 469 games.

First, I need to explain a few terms. Strike efficiency is a term we developed to describe the percentage of time a hitter swings at a strike. It is calculated by dividing the number of strikes swung at by the total number of strikes seen. Ball efficiency is a term we developed to describe the percentage of time a hitter takes a ball. It is calculated by dividing the number balls taken by the total number of balls seen. Total strike recognition efficiency we termed VQAB (Vflex Quality at Bat). This the percentage of time a hitter makes the correct recognition of strike or ball. It is calculated by adding the total number of strikes swung at and the total number of balls taken and dividing this number by the total number of pitches seen. %HH is the percentage of time a ball is hit hard and is calculated by dividing the number of hard hits by the number of pitches swung at. The outcomes I am most interested in are how these variables influence the number of runs scored and wOBA (weighted on-base average). This sabermetric is the one used most often by MLB and is regarded as the most accurate indicator offensive production.

The Data

The average strike efficiency was 61.4% while the average ball efficiency was 72.6%. The average total strike recognition efficiency (VQAB) is 67.3%. More strikes were hit hard (16.3%) than balls (3.0%).

Strike efficiency is not correlated with offensive performance. So, taking a strike has no impact on the %HH, number of runs scored, nor wOBA. This seems counter-intuitive; you want to swing at strikes not take them. However, it was obvious watching the film that many teams purposely take strikes especially early in the count. If a team’s goal is to hit the ball hard, they need to swing at strikes. In this data set, 16% of all strikes swung at are hit hard while only 3% of all balls swung at are hit hard.

On the other hand, higher ball efficiency is associated with increased offensive performance. Ball efficiency (NOT swinging at balls) is correlated with a higher percentage of hard hit balls (r = .32), more runs scored (r = .23), a higher percentage of AB that resulted in a free pass (BB or HBP; r = .43), and a higher wOBA (r = .34). So, NOT swinging at balls greatly increases offensive performance. Actually, the data indicate that 12% of the total variation in wOBA is related to ball efficiency.

Hard contact rates in games average about 10% of all pitches swung at and about 16% for all strikes swung at. The BEST teams hit all pitches hard about 18-20% of the time and hit strikes hard about 30% of the time (top 5 recorded). In cage BP, hard contact rates of 80+% are not uncommon and 90% is possible. One reason I don’t like cage hard contact numbers are that they are super inflated. Look for hard contact rates in open space BP to be above 40% depending upon how game-like the BP is.

Each coach will have their own philosophy on aggressiveness. The data are very clear that NOT swinging at balls is the most important of these two variables. Ball efficiency is highly correlated with hard contact (.43) and moderately correlated (.25) with wins. Interestingly, free passes (BB, IBB, HBP) is more highly correlated with wins (.33) than BE. Actually, free passes (FP) is as highly correlated with wins as the number of XBH.

Your target for ball efficiency in game is 80+%. This performance is obtainable, will win a bunch of games, but only if measured, practiced, and emphasized. Most teams are ball efficient when they are up in the count. The real key is to be ball efficient when hitters are down in the count and especially with two strikes. Lots of teams have ball efficiencies less than 50% in two strike counts which means they are swinging at more than half the balls they see.

In my data set, the number of hits was the most highly correlated offensive variable with wins (.44). The trend in baseball sabermetrics has been an emphasis on ball exit velocity, extra base hits, and HR. In this data set, hits are more valuable than extra base hits. This data was taken from the NCAA website and is consistent with MLB data but not the trends in the game.

The most dramatic shift in the numbers for teams using Vflex is that hitters draw more free passes (BB, IBB, HBP) and strike out less often. A great target for each stat (FP and K) is 17% of plate appearances. If your team responds on average with other teams training with the Vflex, the number of K per PA will drop by about 16% while the number of FP will increase about 13% when using the Vflex.