Updated: Jan 13
I made this report in R Markdown, but decided to break it down piece by piece to provide some general notes and commentary about the tables and visuals. This report is meant to aid coaches in evaluating their hitters and generate ideas for development opportunities.
My personal approach is to look for areas the hitter has a knack for and try to help him elevate and build around that particular strength. I think it has been commonplace in hitting development to look for a hitter's weaknesses and solely focus on improving them. Of course there are certain "minimum requirements" hitters must meet in order to allow their talents to play and if they don't meet those pre-requisites I would agree it's best to start there. However, there isn't a rule that says because a hitter is bad at something it will be easier for them to improve in that area opposed to a strength of theirs. In fact there is research that supports that it's easier to improve upon strengths and that small gains in strengths are more beneficial than larger gains in weaknesses.
Another major theme of my analysis is to look at paired or collective metrics as much as possible. Everything in the swing is connected in some capacity so when I look at one metric I am often doing so with the full context of a hitter's swing and batted ball profile in mind.
Bat Sensor- Blast Motion
This first table provides a basic overview of the Blast Motion metrics that I've found to be most informative. Looking at a hitters from a 10,000 foot view is a good idea before advancing into the more granular components of the swing.
The two vertical bars on the chart represent a 4 and 16 degree range for attack angle. Keeping swings in this range give hitters the best chance to match the vertical plane of the pitch. These density plots show where a player's attack angle most likely lies as well as how reliable or unreliable it is. Hitters with a high spike have a more consistent attack angle, whereas players with a flatter distribution have a more varying attack angle. Ideally players will have a high spike distribution between the optimal attack angle range.
This can be one area where a coach's intuition can be supported by the data. For example, I had a coach tell me that 'Hitter 4' in the below chart had an inconsistent swing plane. What he meant was some days the hitter was matching the plane of the pitch well and hitting gaps, and other days he was swinging down hard on the ball.
Connection Scores- Early Connection
With Early Connection we want our hitters to be inside the range of 80 and 110 degrees. Too high of an Early Connection could lead to issues with pitches up in the zone. EC's that are too low could contribute towards issues hitting pitches lower in the zone. From the visual below, we can see that 'Hitter 2' has a tendency to be on the high side with his EC.
Connection Scores- Connection at Impact
A hitter's Connection at Impact typically falls between a range of 70 and 100 degrees. According to Blast, 90 degrees is the most efficient Connection at Impact achievable however, most hitters have a Connection at Impact in the low 80's according to study done by Driveline Baseball. Hitters with too high CAI's will at times stay inside balls that tend to slice to the opposite field (picture a signature Derek Jeter single the other way). Hitter's with extremely low Connection at Impact scores have the tendency to hook balls to the pull side, and struggle pulling the ball in the air.
Connection at Impact yet again quantifies observations that coaches have been making about hitters for a long time. Coaches will often refer to hitters having either a flat barrel (low CAI) or too steep of a barrel (high CAI) and how these tendencies can negatively affect a hitter's path through the hitting zone. Being able to confirm or deny these observations will allow coaches to be more confident about stepping in and helping the hitter make an adjustment.
From the visual below we can see that Hitters 2 & 4 have EC's that are a bit high while Hitter 3's EC is much too low.
At Connection at Impact, we see that both Hitter's 2 & 4 do a good job despite their high EC's, however Hitter 3's Connection at Impact is extremely low.
This table provides a more granular view of bat speed. I think that average and max bat speeds are pretty easy to interpret but I will elaborate a bit further on the others. Players with an extremely low AVG/MAX may have an issue with intent. These players sometimes have the capability to swing the bat at high velocities but on average swing much slower than they're capable of for a variety of reasons. Sometimes you will see this with very fast runners that have been coached to only prioritize making contact to use their speed. Another possibility is a hitter is very inconsistent with their sequencing and on rare occasions are able to swing efficiently resulting in high bat speeds but struggle repeating the move.
The two vertical bars on this plot at 67.5 and 70.2 miles per hour represent the average bat speeds for college players and affiliate level players (via Driveline Baseball).
In the chart below, we can tell Hitter 2 has elite bat speed compared to his peer group.
"Sweet Angles" is a play on words of Sweet Spot Percentage. Sweet Angle Percentage is how often the hitter's Attack Angle is between 4 and 16 degrees. With a metric like Attack Angle, it can be very easy to get fooled by the average if the hitter has a lot of variance, so looking at Sweet Angle % gives a better idea about how consistently the hitter is producing swing at good Attack Angles.
Plus Bat Speed is simply when a swing is faster than affiliate average.
With Pre Barrels, I am looking for how often the hitter has swings with plus bat speed AND an attack angle within the optimal range. Swinging the bat fast and at good angles are important but the hitters ability to do both simultaneously is what will lead to optimized batted ball results. The more points a hitter can fill up the green zone the better off they will be.
I've found these plots are a good way to quickly see what a hitter's areas of improvement might be. Too many points on the left of the green zone and he clearly needs to swing the bat faster. Large clusters above or below the green box indicates a substantial attack angle problem. Both areas are two of the primary focuses of my programming as a coach.
Vertical Bat Angle
Vertical Bat Angle is largely dependent on the pitch height with the average typically around 30 degrees(via Driveline). For me, VBA information is more supplemental and informative about the type of pitches a hitter might crush or struggle against. I have found the averages worth monitoring in case there is an outlier like there is with Hitter 1.
Efficiency is how well a hitter turns peak hand speed into bat speed. Rotational Efficiency is how well a player turns rotational acceleration into bat speed.
Batted Ball Data-Hittrax
One of the major things to look for in the table below is the difference between average launch angle and average launch angle of well hit balls (85+ mph). Hitters must optimize their well hit balls, and must hit them at optimal angles to do so.
A coach can typically tell you which of his guys can "hit" or "really hit" and which guys can't. Standard deviation of launch angle provides a quick and simple way to quantify this observation. Standard deviation of launch angle has been found to be a good proxy of the 'hit tool'. As important as it is to hit balls at good angles, hitting them there consistently is equally important. A smaller standard deviation of launch angle is what we want our hitters to develop and will lead to a higher 'true' BABIP.
Looking at a hitter's average launch angle together with their standard deviation of launch angle can help provide coaches additional insight into the hitter's performance and avenues for development. The goal for each hitter is to have their average launch angle of well hit balls to be in the range that produces good results, and for the hitter' s launch angle standard deviation to be as small as possible.
The two horizontal bars represent the range for optimal launch angles (8-32 degrees). We want the majority of a hitters' batted balls to come in this range. The more narrow the box the more consistent a hitter's launch angle is. The benefit from using a boxplot to analyze launch angles is we can also quickly see the hitters median launch angle, which I believe is much more informative than the average launch angle with how many outliers there can be with a collection of batted balls.
We can see 'Hitter 1' has at least more than ~50% of his well hit balls below 8 degrees. With the inability to elevate his hard hit balls, he is leaving a lot of potential performance on the table.
These stats should not necessarily be compared directly to the in-game metrics of the same name. However, being able to hold the training environment constant (Competitive BP in my case) these metrics are a good benchmark for training periods. For example, if you are working with a player to improve their plate discipline skills, you could set a goal for them to decrease their Out of Zone Swing percentage in training by 3 %. Or if the player excels at pulling the ball in the air, set a goal for them to raise their air pull % over the next 6 weeks.
By looking at exit velocity and launch angle together, you can learn much more about the hitter than looking at each metric by itself. By plotting EV and LA you can learn if the hitter has a tendency for flares, burners, or topped batted balls. Similar to the Pre Barrel plots with bat speed and attack angle, you can also quickly see if there are any glaring exit velocity or launch angle issues.
Similar to the Pre Barrel plots, the more a hitter can fill up the green box, the better his batted ball results are.
Point of Contact
Point of contact data is very important to monitor especially when players are in a bat speed training phase. If a hitter gains bat speed while maintaining his average POC, we can have more confidence the gains are legitimate.
One aspect of point of contact that I currently believe is under analyzed is the standard deviation of POC. I believe that players with a large POC standard deviation have more adjustability in the zone. Looking at this for well hit balls is also important additional context.
I built this report for myself and other coaches to use as we try to learn more about our hitters. This report supplements the things I see in the cage, on video, hear from other coaches, as well as conversations that I have with the players themselves. All of these other sources of information are valuable and are data points themselves. What I hope I have demonstrated is that these reports can support a coaches natural intuition and observations about a player, while also serving as a check and balance in the case that the coach is wrong. In addition, they can help shed new light on development opportunities a coach may be unaware of.