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  1. WARNING! WARNING! This is a long post. Read at your own expense. Grab popcorn, coffee,soda, whatever you need to stay awake. Take your time, several days if needed, to read the whole thing before responding. Also, couple of things that will help you as you read this post. First, APPL stands for Average Pieces Per Lot. If a set or MOC has 1000 pieces and 100 lots then there are on average, then... 10 pieces per lot. The data set I used for the following is something I compiled on my own. If there are any interested parties I have no problem sharing it. I apologize up front if I have misconstrued the data at all. I took the values of piece count and lot count from Bricklink. Also, from memory on some sets regarding piece count. Funny how some things just seem to stick with you. I recognize that there are many more sophisticated methods for examining the below: I chos I chose the below because of its simplicity, and thus maximizing the availability of others to join in the fun. This post has been percolating around in my mind for a while, ever since I read The Mathematics of Lego by Dr. Samuel Arbesman. Some of the ideas in this post were discussed in Dr. Arbesman in his article, but some are not. Some of the ideas are also borrowed from an article that Dr. Arbesman himself referenced; http://www.ncbi.nlm....pubmed/12381294 ; which is absolutely fascinating. Sometimes I wonder if we fully realize how far-reaching the influence of Lego really is. I think that it is fascinating that Lego sometimes acts as a microcosm for phenomena that occur in real life. Dr. Arbesman discusses the non-linear relationship between the total number of elements and total number of lots in a set. Obviously, there is a positive relationship; as the number of total elements increase so do the total number of lots (most of the time). However, this relationship is non-linear because the steepness of slope in this relationship is different for smaller sets compared to larger sets; which emulates real, true life-phenomena. Dr. Arbesman makes his statements based on a data set of all genres, or most all genres, of Lego. I re-examined not his data set, but my own, randomly pulling sets from a variety of genres, but mostly Technic, Star Wars, Town/City, and other general system sets. Although I saw the same relationship as Dr. Arbesman, any purported relationship really fell apart when dealing with very large sets, or sets with specific themes. Here are at least two reasons why (there are likely many more): (1) System sets, or sets like Creator sets, don't necessarily need to have a function. They are either purely artistic or meant to emulate something in real life, but minus the function. This can create problems when looking at the variables of interest in Dr. Arbesman's article. Best way to describe it is to provide examples. Case in point: Lego 3450. Statue of Liberty. 2882 pieces but only 49 lots. That is, on average, nearly 60 pieces per lot! I am sure there are many, many more examples (think of any mosaic project) but this is one that comes to mind. Sets like these will really mess with the data. Comparing sets that are too different from one another will lead to an apple and oranges comparison but not being aware of it. (2) Sets that offer lots of playability may not really be one set at all. Case in point, set 10188 is actually many different builds, right? To say the set has 3803 pieces, and 438 lots, divide the two and come up with an average piece per lot value would be incorrect, right? One would really have to deal with each little room as its own set in terms of lot and element count. Too complex though. It was not done in Dr. Arbesman's data, and therefore really skews the data. Sets also that have lots and lots of accessories (I will use 10188 again as an example) also will skew the data, because they artificially inflate the individual lots value. The accessories are not needed for the build, but included in the lot count. So, to rectify the two above issues, I thought it would be fun to examine total element count and lot count in ONLY Technic sets/MOCs. I think that doing so fixes the above problems, perhaps not totally but in my mind satisfactorily because: (1) Technic models add functionality, which by nature, maintains some sort uniformity in the sets of interest. We are not comparing sets that are so different as say, Lego 3450 and 10188. (2) Technic models don't artificially inflate individual lot values by adding lots of accessories, which a lot of sets in other genres do. As mentioned, for small sets this really is not an issue, because there are not many accessories to be had if the set itself is small. But for large sets, it is a real issue, and that is why the relationship between the two variables of interest pretty much dissolves when dealing with large sets. So, that is a long introduction to what this post is really about; but, there you have it. I wanted to examine the relationship between total number of Technic elements in Technic sets/MOCs and their relationship with individual lot counts. Below is a picture of a simple scatterplot of these two variables. The picture below that gives best of fit lines, with a logarithmic function barely beating out a standard function as the best line of fit for the model (R - squared values of .83 versus 90 respectively). (sorry for the bad pic quality - I upload from mocpages and they have always been behind the curve in terms of technology. If anyone needs clarification,I can try to answer any questions) So, we can therefore say that, although lot counts increase with set size, it is to a much lesser extent with larger sets than with smaller sets. Thismakes sense, especially if you read the article mentioned previously: http://www.ncbi.nlm....pubmed/12381294As systems increase, so does their tendency to repeat themselves, or what can be referred to as "redundancy" in a model. Now, redundancy is usually abad word, but in this case, not really. Redundancy can add to efficiency; which is the very thing that I want to discuss. Cities with many, many gasstations (redundancy) lead to efficiency in traffic. In biology, systems (animals, plants, etc.) that have many, many cells (again, redundancy) areusually more efficient than smaller systems (think of the metabolic rate of an elephant versus a mouse; that is why their life spans are so ridiculouslydifferent). But what does all this mean for Lego sets? If you look at the graphs below, those models that fall above the curve have lower averagepiece per lot (APPL) values than their relative (the term relative is important here, because when using the curve we are comparing small sets tosmall sets, medium to medium, etc.) counterparts. Those that fall under the curve have higher APPL values. The further a dot is from the curve the moredistinguishing they are; either for having low or high APPL values. The question I would like to put forth to the forum is "What do these valuesmean in terms of the sets/MOCs we love so much?" Does a low AAPL value set mean that it uses more unique parts than one with a high AAPL? Is this somecrude measure of UNIQUENESS for our sets? Is a set that has an AAPL of 10 "uniquier" than one with say, a AAPL of 15? (assuming both were more or lessthe same size?). Or... do large APPL values simply mean that a set is more EFFICIENT in its use of parts than a set with a small APPL? I am NOTtrying to use these questions as a way to judge MOCs or sets, there are so many more variables that better depict elements of a quality in a MOC or setthan what I am discussing. I just think that it is an interesting idea, and would like to know more thoughts from others. Many may be thinking. What the heck is this guy talking about??? Perhaps a couple of examples will help. Below are circled two sets that are amongthe most beloved Technic sets; old or young (notice that I say "among" - I get that there is much subjectivity here). Notice how they are both wellbelow the curve, meaning they have a lot of redundancy, or repeating pieces in their build. These sets have very high APPL values relative to theirsize group. Can anyone guess which ones they are? If you guessed 8288 and 42009, both cranes, you guessed right (if you can guess what set is to the left of the 8288 set, GOLD STAR for you!). And they both scream redundancy in part usage right? In fact, that was one of the criticisms of 42009; lots and lots of pins. 8288 has lots and lots of liftarms for a set with only 800 parts. So, are these high APPL values reflective of redundancy or efficiency? Dr. Arbesman would argue both. As a system (or Lego set) gets bigger, it gets more redundant,and therefore more efficient (I get there are many definitions of "efficient" here; in this application we mean efficient from a productionstand-point, not a functionality standpoint. TLG loves sets like these if they sell. Not a lot of molds have to be used and/or created for a set withhigh APPL; and we all know that is really where TLG loses money. In the making of new molds). What do you think though? As I mentioned, I think itis no coincidence that two of the highest APPL values are also two Technic fan favorites; but this could also be to the fact that we all just lovecranes. That is why I am directly stating that I don't think that APPL values have really anything to do with popularity or skill in which they arebuilt, but I do think they are curious phenomena to discuss. So what about low APPL values? Remember we are talking about values relative to set size. An APPL of 8 for a set or MOC that is 3,000 pieces isentirely different from the same value for a set of say, only 300 pieces. Do low APPL values mean that a set is unique? Does it mean that elementsare used in a unique and creative way? Or something that I have not mentioned? Some examples of sets with low APPL values would be set the twosets circled below: Can anyone guess which two these are? Guess the first one (not gonna tell you) and the second (furthest to right) is the 8110 Unimog. Relatively a lot of lot counts for a limitednumber of pieces. What does this mean? Does this confirm or change anyone's previous perception of the Unimog (or the other set that I will notmention)? Lastly, keen viewers will notice that I have included sets that do not reflect any known official Lego sets. These are a variety of MOCs fromwell-known builders. I selected the sets in a somewhat random fashion, and with the help of and some very kind and helpful, talentedbuilders (Thanks Sheepo and Crowkillers!)These MOCs from left to right are: 1 - Porsche 997 GT3 by Crowkillers 2 - Vampire GT (Black) by Crowkillers 3 - Black Muscle Car by Crowkillers 4 - Lambo Aventador LP 720-4 by Stefan Birkefeld 5 - Cadillac Eldorado by Martijn Nab 6 - Land Rover Defender by Sheepo 7 - Volkswagon Bus Type 1, Manual by Sheepo 8 - Mustang GT, Manual by Sheepo 9 - Volkswagon Bus Type 1, RC version by Sheepo 10 - Mustang GT, RC, by Sheepo 11 - Wing Body Truck by Madoca 12 - Terex RH400 Mining Excavator by Sheo 13 - Tractor Truck by Lucio Switch
  2. legostarwars1425

    Special Pieces - Your Opinion

    Ok if you look up "Custom LEGO pieces" you come to find a couple of things. Do any of you out there use the pieces in MOCS? Have you ever tried making your own? I have used them, like Brickarms, Brickforge, and Brickmania for my war models. What do you think about these or any other type?