Blog – Page 2 – Alex Fast

Adding Animation

Posted on September 25, 2017by alexfastLeave a comment

Lab:

This past Friday Chris Hall, Manning Qu, Oriana Neidecker and I got together to clean up our data. Each of us took turns sitting in front of the computer and getting to know the software a bit better. We started by bringing a simple walking animation Chris did with her group the week before. Due to her hair being in a pony-tail, a lot of the data around her neck was missing which was a good thing because it let us figure out how to use the software properly to clean up the data.

After the walking animation was cleaned up, the other three members head out and I was able to take a look at a horseback riding animation that I had recorded the previous week. The good news was that this information was totally clean and required no additional work but this – may have – led to complications down the line.

Workflow:

Overall, the process of using MotionBlender was pretty easy thanks to Todd’s great tutorial. The one thing that was a bit over my head was that the wrists don’t seem to be animation properly. In the attached video you can see that the forearms and wrists aren’t operating in the way that the skeleton is. Rather than fold at a 90 degree angle, the right arm juts out a bit more which makes it look like the character is petting something next to him as opposed to his own horse.

Final video:

While I’m happy with the fact that the animation plays well, there are definitely more con’s than pro’s in this first video; mistakes that can be chalked up to my first time using the software’s. For example, the scale of each of the different objects – the character, horse and bison – are all very off as is the scale of the foliage. The graphics card in my Mac is also preventing the character from appealing smoothly, instead it looks like a character from Goldeneye.

With that said, I’ve really enjoyed the process and look forward to learning more about the various softwares we’re using.

Projection Mapping

Posted on September 19, 2017by alexfastLeave a comment

One of my favorite films of all time is 2001: A Space Odyssey. The notion of a prescient monolith delivered to earth by an evolved species is just of endless fascination to me. I thought it would be cool to explore this a bit further for my first projection map.

A famous line from the text is the final words Dave Bowman utters before he enters the monolith: “My god, it’s full of stars.”

I thought it would cool to utilize that period at the end of the sentence, using it as an entrance into the monolith.

In an ideal world, the sketch featured above would take up an entire museum wall. The period at the end would be about 7 feet tall by 5 feet wide and the projection would be pointed at the period. (Man, as excited as I am about this projection, it sure does take the appeal out of it when you film it in your dinghy apartment.) That way, there’s a sort of shock when the black period “opens up” and the user enters into the world of 2001.

Overall, I’m pretty happy with how things turned out especially considering I’m still learning Unity. The most difficult technical aspects were making sure the video and door animations worked properly (I admittedly had some help with the Lynda tutorials for the latter). While I’m certainly underwhelmed by seeing the projection on a crappy speaker in my apartment, it makes me really excited to see what it would look like in a more ideal setting. Either way, it’s made me very eager to explore the world of projection mapping a bit more.

Butcher’s Crossing and Lab Documentation

Posted on September 18, 2017by alexfastLeave a comment

John Williams’ Butchers Crossing is a novel I have a long history with.

I first read the book in 2010 and became obsessed with it. The chaotic way nature was represented was unlike any other western I had ever read or seen before. Williams’ denial of Emerson’s idealism and descriptions of the protagonist Will as he came face to face with futility had me quite enamored. So much so that I started a long process of trying to acquire the rights to make the book a film. I’ll spare the details but nothing ever became of my attempts and two years ago I had to quit the conquest.

When we were given the opportunity to create an environment in Unreal, my mood board was about a preconceived idea about baseball. That was until Butcher’s Crossing came back into my brain and I couldn’t resist the urge to have another go with the text.

The atmosphere I was looking to represent was two-fold. I wanted one that had some warm colors, that appeared as if it was occurring at dawn to connote a sense of newness. Unfortunately, my limitations with the software presented me from adorning the landscape with other features – different sorts of foliage, more muted colors, a more craggy “cliff”. I wanted this first part to convey a sense of stumbling onto something untouched and private.

My other atmospheric goal – and one in which I feel I didn’t succeed so well – was to convey a slight touch of danger; some sort of harbinger of ill-will. I put a light fog over the scene and sprinkled in some sharp rocks in an attempt to convey this. I also made sure some of the lake was touched by the sun to give it a sort of “on-fire” look.

At the end of the day, while I would like to make a few changes – a slightly more ominous atmosphere, a better looking horse, a few cowboys – I was really happy with what I was able to create with just a few days of UE4 under my belt.

As a result of using source material, I have three characters that have already been created and fleshed out for me. They are:

Will: A 23 year-old Harvard drop out who has ventured out to “find himself in the great West”.

Miller: A surly hunter who is eager for wealth.

Charley Hoge: A one-armed veteran and companion of Miller’s.

Fred Schneider: The stubborn voice of dissent.

Part Two: Lab

This past Saturday Izzy, Ran, Spencer, Manning, Teresa and I got together to gather some movement data.

Izzy and I were a little early so we calibrated the space without any issue and began to suit up.

Manning, Ran and Teresa handled most of the software while sporadically aiding Spencer in getting all of the sensors on Izzy and I.

Once we were all suited up and skeletons had been made of us, Izzy and I began running though various movements.

We had conversations to get natural hand gestures, we climbed on ladders, we rode pretend horses (really excited to see that one), and ‘went for a swim’. We did some flying, some dancing and some crawling around in the sewers (per Teresa’s request).

Each member of the group made known what specific gestures they needed (Manning needed some jumping, I needed the horseback, etc), we saved and stored the data and went about our ways. I think I speak for all of us when I say we’re all eager to see what it looks like and integrate it into our environments!

AR Movie Posters

Posted on September 15, 2017by alexfastLeave a comment

AR is going to change a lot of different industries. As we’ve already seen from the first assignment we did in class, there are countless opportunities afforded us by this nascent technology. Luckily, this first assignment has us narrowing the scope of things down a bit.

One of my favorite movies is 2001: A Space Odyssey. In my room are two different representations of the work. The first is the movie barcode featured below.

The other is a glow in the dark poster featured at the top of the post. The image on the left is the image during the day and the right side is in the absence of light. I decided to augment this image because I think it would add another level of dimensionality to an already multi-dimensional poster.

Now that I knew what I was going to augment I needed to figure out how to augment it. I wanted to treat the project as if I were a marketer for the film. If I were passing by this poster on the street, how could I use AR to enhance the story without showing a simple trailer for the film (although I feel that to be just as viable a usage of AR).

One of the lines that always stuck with me was the line Dave Bowman was heard uttering when he entered the monolith: “My god! It’s full of stars.” The allure and mystery of the statement keeps with the movie’s vague ending and I wanted that to be featured as to draw people in. Once they – hopefully – were, I wanted to continue to draw them in with the Stargate sequence which gives the allusion of entering a space they’ve never seen before. The AR experience ends with the video expanding very quickly into the image of Dave’s mouth frozen in fear; a happy accident that was more a result of the tech’s inabilities than a creative thought.

The Blippar tech is great for what it is, but there certainly are limitations to the free software. For example, I wanted the words to fade in and out but had to settle for making the text small and large. I also tried to upload custom transparent background mp4’s to no avail.

With that said, I’m still happy with the product I came up with which is featured below. (Sorry for the vertical video).

Mood Board and MoCap

Posted on September 7, 2017by alexfastLeave a comment

Here is a link to my mood board.

The mood board ranges from Gregory Crewdson photos to Minor League Mascots.

I’m interested in the dilapidated suburbia vibe. Not one with nefarious, Lynchian undertones but one with an awkward, painful sort of hope; one that doesn’t take itself too seriously and has a DeLilloesque sort of absurdity to it. In this vibe exists the fictional town of Equator, MO, an oppressively hot suburb where a minor league pitcher is trying to work his way up the minor league ladder. In his way is a slew of mechanical issues that only the user can fix.

As of now there are effectively two baseball video games on the market: MLB The Show and RBI Baseball. The latter features minimal pitching mechanics while the former takes it a step further but fails to take specific atmospheric steps. For example, an entire pitchers arsenal is open to you in MLB The Show, but the mechanics that go into that arsenal are in no way covered. This is the void I am interested in filling.

I want to work with actual minor league pitchers, capture their movements and develop a game that focuses on the minutia. I’m not sure if you’re familiar with the mechanics of pitching but so much goes into it: arm slot, foot placement, grip, etc. This game would not only explore those fields but also serve – I think – to explore the depth in which motion capture can go. The more in tune you get your mechanics, the more success you will have.

As of now, I’ve made contact with the New York Mets affiliate, the Brooklyn Cyclones, who appear to be interested in working with me in making this project. If their interest appears to be as sincere as they let on, I may pivot to helping them use motion capture to establish a training tool to make their pitching staff more effective.

BONUS: Calibratin' the space!

Get a Grip

Posted on September 7, 2017by alexfastLeave a comment

As I mentioned in class, I’m a bit baseball obsessed. Considering the assignment was to create a .gif that represented you, I figured why not stick with what I’m passionate about.

Aside from working for Major League Baseball I also write/podcast for PitcherList, a site dedicated to everything about pitching. So my animated .gif is focused on just that: pitching. Specifically two grips commonly used in the sport: the four seam grip (featured first) and the cutter grip. Below that animated .gif are the two pitches being used.

Here is Marco Estrada’s Four Seam Fastball (first grip)

And here is Jon Lester’s cutter (second grip)

Nature of Code Final

Posted on March 7, 2017by alexfastLeave a comment

For my project, I wanted to create a simulation to see if a home run hit in one major league ballpark would leave another ballpark. At the beginning, my expectations were really lofty in terms of what factors I wanted to take into account. And by so lofty I meant: all of them. I wanted to take into account, humidity, air resistance, wind, exit velocity, distance travelled, etc. After attempting to do this, I figured it would be best if I started a bit simpler by taking weather out of it.

I started by extracting only baseball factors such as distance, exit velocity and launch angle. I used ESPN’s hit tracker – which keeps track of every single home run each season – as a reference for all of this information. I then created some code where I could begin applying forces to things. Due to how many factors needed to be taken into account plus the fact that pixels don’t really measure distance, there were certain constants that I needed to take into account. For example, gravity was always set to 0.2 and acceleration was always set to 5. As I begin to advance this project and bring in different factors, these things will change but keeping those two forces constant’s allowed me to advance my project.

I’ll get more into how I created the code below, but for now let’s take a look at the code in action.

This was the longest home run that was hit in 2016 and it was a good place to start. I knew that if I could map this, I would have the highest threshold for homeruns considering none travelled further than this. It was this home run that let me test if my code was working and in the end, it ended up being successful as you can see below.

Now all I needed to do was test to see if a home run hit in one park would work in a different park.

This is Manny Machado’s grand slam that he hit on April 28th. If we look at the code first we get the sense that it barely left the yard. Let’s take a look to see if that’s true.

It is! Now let’s take a look to see if that same hit would’ve left the yard in Comerica Park.

It wouldn’t have. The sketch works!

Let’s dive a bit deeper though. What can we tell about the data before we see the home run.

If we look at Alex Gordon’s Home Run in the sketch, we get the feeling that it too barely left the park in CF.

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Once again, the video shows this to be true.

How about a home run that wasn’t hit to center field? Can we use to sketch to see if it wasn’t hit to right or left field but hit to center instead, it still would’ve been a home run?

Let’s take a look at Chris Davis’ home run against the Blue Jays. It left the yard in right field and just barely made it over.

So if that same hit was directed towards center field, would it have been a home run?

Nope, it looks like it would’ve come up just short.

Bandz To Make Us Map

Posted on March 5, 2017by alexfastLeave a comment

Unarguably the most difficult part of this process was the in class instillation of the Landsat software. That is to say that using this landsat …software I guess you would call it? …is incredibly easy and dare I say fun to use.

For the assignment, I just wanted to make sure I could use Landsat efficiently and effectively while understanding what was actually happening. My first attempt at using it, I decided to pull in my exact location in Brooklyn. I started by going to www.latlong.net and finding my exact location:

I took the screenshot because I was curious to see just how precise the landsat imagery was going to be. I obviously wasn’t expecting the specificity of a street level view but I was anticipating I could make out some geographical features that would lead me to believe: “Oh, this Brooklyn.”

I took the first 6 digit of the lat and long, and input that into my terminal. After the JSON information loaded, I chose the scene ID that seemed to have the most minimal amount of cloud coverage, used that to download and process some bands and got this image:

This does…not look like Brooklyn to me. Doesn’t look like any area that I know in NY. I decided I’d try it again with another familiar image: my home in MD. This time however, rather than taking just 6 digits from lat and long, I took the entire lat long as found by the same aforementioned website.

Here is my before image:

And here is the processed image when the exact lat/long was placed in the landsat software:

This, to me, is a much more recognizable image.

The purpose of this to me was to used locations in which I am specific to see how granular the satellite imagery will get. Now, I have some context as to how most effectively I can use Landsat.

Forces in Baseball

Posted on February 19, 2017by alexfastLeave a comment

Title:

Forces in Baseball

What is it:

A physics engine that looks to simulate the effects of various aspects of air density – humidity, temperature, altitude – and wind on a baseball.

Visual Mock-Up:

Sketch:

Status Update:

As I’ve started to explore this assignment I’ve realized how incredibly vast an undertaking it is. I feel like the best way to go about doing this is to reverse engineer a home run to see what factors I need to take into account, and how to take them into account going forward. So, I’ve decided to start with what was – by StatCast’s measurements – the HR that had the highest projected distance in 2016: Giancarlo Stanton’s solo shot against the Colorado Rockies on August 8th, 2016.

Here are things that I know about the home run:

Distance: 504 feet
Exit Velocity: 115.8
Launch Angle: 18.3
Height:72.3 Feet
Temperature: 75 degrees
Wind: 13.8 MPH ENE
Barometric Pressure: 29.85 Hg
Average Humidity: 55%
Weight of baseball: 145 g or 0.31967 lbs
Hangtime: 5.4 seconds

Statcast data: http://m.mlb.com/statcast/leaderboard#hr-distance

Weather features found: https://www.wunderground.com/history/airport/KDEN/2016/8/6/DailyHistory.html?req_city=&req_state=&req_statename=&reqdb.zip=&reqdb.magic=&reqdb.wmo=&MR=1

So how do I put these things in code? I know how to create different forces and how to apply them to different objects but what is the best way to proceed. I imagine it’s by starting with the ball at home plate with no forces applying to them. Then changing velocity and acceleration to launch angle and bat speed as those are two determining factors as to how far a home run is hit. Then applying all of the other aforementioned forces. I have difficulty weighting the forces though: which has more of an impact. Also, how do I determine the mapping of exit velocity? Find the highest and lowest measurement of 2016?

Everything below is personalized questions and notes so you don’t need to read further if you don’t like.

Questions:

When you’re applying a force, you’re essentially changing acceleration, correct? Because acceleration = that new force. This doesn’t negate the acceleration that’s already in there, correct?

The forces that I need to put into a sketch all fall under categories that effect air density: temperature, humidity, and altitude. The higher the altitude, the lower the air density, the further the ball flies.

How do I make a force effect the entire sketch? Like I need air density to be a blanket that slows down the entire sketch.

Is the best thing than to recreate a home run using the data that we have? Like Stanton hit a HR in Coors that went a projected 504 feet, with an exit velo of 115.8, a launch angle of 18.3. The inning was the top of the 5th so the temperature was about 75 degrees, wind was about 14 mph and was blowing NE.

Check out Alan Nathan, physics professor

5 things that can affect a baseball game from this accuweather article:

Air temperature can change a baseball’s trajectory. A ball coming off the bat at 100 mph with a 30 degree launch angle will travel about 2.5 feet less in 60 degree weather as compared to 70 degree weather. That’s not a big difference in this context but games in April are played in 40 degree weather while a game in the same ballpark in August can be played in 90 degree weather. That’s a 10 feet difference which can easily be the difference between a fly ball and a home run.
Air density. Look at Coors Field in Colorado which is a mile above sea level and therefore has less air density than your average baseball stadium. The balls used in this stadium at stored in a humidor at a constant 50% relative humidity and at 70 degrees to help change the coefficient restitution or bounciness of the ball. The ball becomes a bit heavier and the ball doesn’t come off the bat as fast and doesn’t quite travel as far. What is the average mass of a baseball?(it’s 145 grams).Due to the density of the air, breaking balls like Curveballs and Sliders will not break as much in less dense air. Pitchers perform better at Coors when it’s colder because the ball will spin more.
High and low temperatures If you are sweating a lot, it’s harder to feel the ball and get a good grip on it. Same thing if it’s too cold, the batter will lose feeling in his fingers.
Cloud Coverage A bright blue sky – or high sky – can cause problems with depth where as clouds can cover up the ball.
Wind Some pitchers that rely on movement from their breaking pitches prefer to have the wind in their face, they think it will add movement to their pitches while some feel the opposite.

Notes from Alan Nathan’s “You Can Observe A Lot By Watching”

Quotes from The Physics of Baseball by Robert K. Adair
- “Our goal is not to reform the game but to understand it”
- “The physics of baseball isn’t rocket science. It’s much harder”
The bat will slow down after contact is made with the pitch
The bat is not a rigid object, and the further away from the “sweet spot” the hit is, the more the bat will vibrate or even splinter in half.
Queue up to 18:19 to see the different vibrations of a bat
The sweet spot is the node on a bat where vibration would be smallest. That’s why it hurts a bit when you get a hit o the sweet spot. At the sweet spot vibrations are minimized.
The manner at which a bat breaks depends on where the impact occurs. If the impact occurs on the end of the bat then it bends towards the pitcher and that’s the way it breaks. Compare that to when the bat breaks closer to the barrel and it cracks towards the catcher.
The ball doesn’t know the end of the bat is even there because the impact takes 1/1000 of a second.
The batter could just let go of the bat just prior before making contact with the ball and it wouldn’t make a difference to what happens with the ball. This means: if a bat is swung at a certain speed, it doesn’t matter who is holding that bat.
Look at Todd Frazier’s 5/30/12 home run. Ball hit 375 feet and the bat was barely in his hands. Grip doesn’t matter.

Nasty, Nasty, Nasty

Ball swerves in direction that leading edge is turning
Pitch F/X. Created by Marv White
- two video cameras @ 60 FPS set up in each ballpark above first base line and above home plate
- Tracks every pitch in every ballpark
“Hitting is fifty percent above the shoulders” — Ted Williams & “Hitting is timing; pitching is upsetting timing” — Warren Spahn
Control: the ability to throw strikes. Command: the ability to throw well located strikes
Movement: how much a pitch deviates from a straight line.
For Jon Lester’s 4 seam there is back spin which means the force is actually moving upward, it drops because of gravity but the force due to the spin is upwards. It also breaks slightly to the side, bearing in to a LHH.
A knuckleball has no spin whatsoever, it has to do with how the seams are oriented. If the ball is barely spinning or spinning slowly, the seams have the most effect on the ball. If the ball were perfectly smooth there would be no knuckleball effect. Small changes in how you release that ball can cause big changes in what happens to the ball when it gets to home plate.

Three For the Price of One

Backspin on the ball because the ball slides along the surface of the bat temporarily and the friction between the ball and the bat acts in the opposite direction
If a batter is out in front of a ball it will move towards left field if he is a right handed batter and towards right field if he is a left handed batter. Ball begins to spin counter clockwise.
Live drives in the outfield curve towards the foul pole

Building a worse mouse trap

When a ball hits a wooden bat, the ball’s surface will change but the wooden bats won’t. All of the energy of the collision is taken up by the ball when it squashes, not the wooden bat. That’s super inefficient.
When you get a hit with the aluminum bat something called the trampoline effect occurs. Result of trampoline effect: less energy lost in the bat.
Ball bat coefficient of restitution. When it’s large the performance of the bat is better when it’s small the performance of the bat is worse. BBCOR of a wood bat is about 0.5. You want to regulate the performance of the bat’s you regulate the BBCOR.

Extra Innings: The Carry of a Fly Ball

Three pieces of information about every home run:
- Precise landing point of the home run
- The time it takes to travel from contact to landing
- And initial speed and angle
With that information you can do…
What does it mean when you say the ball “carries well”. If you look at the actual distance the ball traveled compared to how it would travel in a vacuum, if there were no air effects and only gravity it would travel one distance and with air effects it would travel another
If you take the same initial velocity of a home run and say how far would this go if there were no air, gravity but no air, it would travel 571 feet. With air effects it goes about 397. 397/571 is 0.695. So 0.695 is the carry.
Look up Alan Nathan’s Normalized Carry done in 2009
The ball carries very well in Denver but doesn’t carry in Cleveland.
Denver is a mile above sea level and the air density is roughly 80% of what it is at sea level so the bar carries further because there is less air. A ball that would travel 400 feet would travel 425 in Denver.
When the temperature is high, the air density is less and the ball carries further.

Polar Rectangles

Posted on February 14, 2017by alexfastLeave a comment

Between the particle systems and oscillations there was a lot of content that was learned, each of which I was curious to explore. When making pendulums, I wanted to make something that was like a Processing version of Miley Cyrus’s “Wrecking Ball”. The Particles systems reminded me of volcanoes and I wanted to play with color. Then I did the polar coordinates video and ended up experimenting with different variables for the better part of an hour. I put “The Who” on and just sort of went to town and got the following.

It doesn’t really look like it but this was my first foray into multi-dimensionality. I’m still a bit confused on how to incorporate the Z axis but these are the next steps I would like to be taking.