CCI + MA Trend-Follow Method for Binary Options Trading

/r/reddevils 2020 Census Results

Thank you for taking part of the 2020 edition of /reddevils' census! We had 3,459 responses over the course of several days, and increase of .
Here are the results!
Age
With a year passing, it's understandable that our user base has also aged. What is interesting is that while last year 59.5% of the userbase indicated that they were 25 and younger, only 46.1% did so this year. Given that there was also a large increase in respondents for the "26-30" age group, it seems that we had a lot of 25 year old folks responding last year. Here is a chart showing the break out by age group and also an age distribution graph. I've included also a year-over-year comparison this year. These do not represent percent change but rather simple subtraction. For example, the 4.1% increase seen in the "26-30" age group comes from this year's "26-30" being 29.17% of this years census responses vs. only being 25.07% last year.
Conclusion? We're getting old folks.
Gender
As with every census we've run, /reddevils is overwhelmingly male. 96.2% of respondents indicated that they were male which translates to 3,328 out of the 3,459 responses. The number of ladies here increased greatly compared to last year with 72, up from 28 in 2019. 18 respondents declined to specify their gender while 41 responded with another gender.
Our resident Wookiees have increased in number to 3, up from 1 last year and in the 2012 census. 2 respondents responded as being Non-binary as well as 2 indicated that they were Olesexual. Each of the following received one response apiece: Coca Cola Can, Lockheed Martin F-35 Lightning II, Cube, Moderator, Divine Enlightened Energy Being, Two-Horned Rainbow Unicorn, Earthworm, Bisexual Leprechaun (who, surprisingly was not from Ireland but rather the Land Down Under), Absolute Chad, Anti-Virus, Attack Titan, Neymar, Ole-Wan Keaneobi, Parrot Lord, Frank Lampard, Optimus Prime, Potato, Slightly Under Ripe Kumquat, Gek (Geek?), Twin Engine Rafale Fighter Jet, Gender Is A Construct, Vulcan, Washing Machine, Wolfbrother, Juggernaut, Woolly Mammoth, Luke Shaw's Masculine Bottom, and Mail. There was also one respondent who deigned to use the "Other" option here to leave me a very rude message. Guess you can't please everyone.
Employment
Most of the reds are employed (75.3% across the Employed, Student Employed, and Self Employed categories), up from last years' 71.5%. Given the current state of the world, it is nice to see that most are still employed. Our student population has gone down, understadably, from 37.4% across the two student categories to 30.0%. A full breakdown of the year-over-year changes can be seen here. Our retirees increased in number from 1 last year to 11 this. Enjoy retirement sirs/madams.
Residence
As expected, the majority of /reddevils is UK or US based (25.85% and 25.93%, respectively). We have seen major changes this year, particularly in relation to Scandinavia, which saw the largest increase in percentage points year-over-year. I wonder what happened there.
If we're breaking it down by the regions I arbitrarily put into the census form, UK (England) is the clear winner for a second year running with 569 members reporting living in England and another 184 specifically saying they are in Manchester.
I received some feedback about covering large areas with a single region. This was largely driven by how few responses had come from these these regions historically. I'll include a few more next year but please do not expect me to list every one of the the 195 countries in the world. I've also received some feedback about not allowing any options for folks with family ties or had grown up in England/Manchester and had moved away. This will also be included in next years census.
Season TicketholdeMatches Attended
Overwhelmingly, most of us here are not season ticketholders (97.95%). We did see an increase in those who are, though it is fairly minor.
Most folks are unable to attend games as well. The number of fans who do go to many games (16+ per season) more than tripled from last year. You all are the real MVPs.
How long have you been following football/Manchester United?
Understandably, we don't have a whole lot of new fans. Interestingly enough though, we've had a large increase in folks who have started following football regularly in the last 1-3 years despite having followed United for longer than that. Putting on my tin foil hat, that at least makes me think we're more fun to watch these days.
How long have you been a subscriber to /reddevils and how do you usually access Reddit?
There are a lot of new-ish users with 63.6% reporting they have subscribed here for less than 3 years. We have a decent number of /reddevils veterans however, 154 users indicated that they had been subscribed for more than 8 years. It's good to see the old guard still around.
Unsurprisingly, Reddit apps are the most popular method to access Reddit by far. This is followed by Old Reddit users on Desktop, users of the Mobile Reddit website, and then New Reddit users coming in dead last. Long live old Reddit.
Favorite Current Player
The mood around this question was incredibly different than last year. Last year, many were vocal indicating that they had a hard time choosing due to our squad being shit. Victor Lindelof ended up being the by and large favorite with around a quarter of the votes, followed by Paul Pogba and Marcus Rashford.
This year, it appeared that there were no such issues. Only 1 response in the survey indicated that they couldn't choose because our squad was shit while the vast majority either selected a player or indicated that they loved them all. Prime Minister Doctor Sir Marcus Rashford overwhelmingly came in first place with an almost 300 vote lead over second placed Anthony Martial. Bruno Fernandes and Mason Greenwood were neck and neck for a while, eventually settling into third and fourth respectively.
Former crowd favorites Victor Lindelof and Paul Pogba fell down the rankings with Lindelof ending in 8th place and Pogba in 5th.
Favorite All Time Player
Wayne Rooney continued to the be the king of /reddevils amassing nearly double the votes of second placed Paul Scholes. Cristiano Ronaldo came in third after a very tight race with Scholes. Beckham came in fourth followed by fifth placed Cantona and sixth placed Giggsy.
Here is a year-over-year comparison purely on recorded responses. Most players received just about the same share of the votes as they did last year. The biggest changes came from Wayne Rooney (up) and David Beckham (down). The way the numbers land, it almost looks like Wazza was stealing votes from Becks! Ole Gunnar Solskjaer had more of the proverbial pie, again I wonder whats happened there.
My man Park Ji Sung came in 11th place, good to see that there are at least 58 Park lovers out there!
Now for a bit of fun. Someone asked in the Census thread how many of George Best's votes came from Northern Ireland. One user suggested it was all of them, the data on the other hand says otherwise. Only 10 of Best's 29 votes came from Northern Ireland. George Best tied for favorite player there with Wayne Rooney with Paul Scholes and Cristiano Ronaldo tying for 3rd place with 8 votes apiece.
I did this same exercise with a few other players. Here are the results:
  • While Scandinavians votes were joint-most for Ole Gunnar Solskjaer (tied with the UK), he was not the most popular player among respondents living in Scandinavia. He came in second behind Wayne Rooney.
  • Roy Keane both received the most votes from the Republic of Ireland and was also the most popular player among Irish respondents.
  • Eric Cantona was not voted heavily by the French. The British, on the other hand, love him with 82 of his 218 votes coming from the United Kingdom. The majority of Cantona voters are older, with 134/218 being over 30 years of age.
  • Park Ji Sung received the most votes from the US (21) followed by the UK (19) and Southeast Asia (4).
  • Among respondents from the United Kingdom, Wayne Rooney was the most popular followed by Scholes, Ronaldo, and Cantona.
  • Among respondents from the United States, South Asia, and Southeast Asia, Wayne Rooney was the most popular. Scholes and Ronaldo alternated in popularity in second and third place. Beckham placed fourth in all three regions.
Thank you all again for your participation. We'll run one next year and see how things have changed!
submitted by zSolaris to reddevils [link] [comments]

The door didn't open, but the doorway did: Fixed points, and mathematical transformations

The door didn't open, but the doorway did: Fixed points, and mathematical transformations
EDIT: read below original text for an expansion on this theory involving overlapping dimensions EDIT2: corrected Giratina logo to Garalina logo
So I've seen a thread about the sort-test option in the Room Impulse menu from Petscop 19, and it seemed like people concluded it has to do with testing axis alignments for 3d polygons. I'm not trying to argue that this isn't a reasonable answer, but I don't think we should write sort-test off as an avenue of exploration.
image description: guardian in the sort-test recording
I've noticed so many references to reflections and rotations throughout the game, and I thought it was interesting how we see so many different orientations of these two intersecting planes, represented by the pink square with an arrow and the blue square with an x. In some of the squares that the guardian passes through, the angle between the pink and blue planes is changing. In others, it seems like the blue plane might be reflected across the x or y axis as well. I think it's likely that there's several geometric transformations for each one. I think I counted 34 of them, BUT if you look at the left side of the above screenshot, there are two more sets of pink and blue squares (which makes me think of parallel dimensions, like what we see in the quitter's room), and this makes 36 pairs in total. Keep this number in mind :)

The door, in fact, did not open at all.

Let me bring up the door puzzle from Petscop 14:
>This windmill vanished off the face of the earth. Here's a similar puzzle. For you, Marvin: There are two pictures of a door. In the first picture, the door is closed. In the second picture, taken later, the door is open. Nobody opened the door. The door did not open itself. The door, in fact, did not open at all. What happened?
I'm not sure if this has already been discussed, but I always felt that the answer would be that the door is a stationary object and the rest of the world rotates around it. Like, maybe imagine that you have a door attached to a frame. You nail the door into the ground so that it's fixed. Let's pretend like there's no latch you need to turn (because video game logic), so you can freely swing the door frame about the hinges. Now imagine that there's a wall attached to the frame, and now a room, and now the entirety of the world swings along that axis. The door hasn't moved at all, but now, your path is no longer blocked by the door. I take the line that the door "did not open at all" to mean that the door is never acted upon—technically, the door did not open itself, and nobody opened the door. Instead, we moved where the doorframe was, and our goal was really to walk through the doorway.

Generation Dial Selector

image description: bottom left is the red dial
I think this is supported by the "Dorito-Dial" (as dubbed by u/Vinny_H1) that we see in Petscop 17 during the birthday party scene with the different iterations of pyramid-heads. Vinny_H1 observes that the red-white gradient is broken into several "slices" and counts about 10. I think it's worth pointing out that this is another use of an arrow symbol and how in this case, the arrow on the selector remains stationary while it's the gradient that rotates. It's nothing concrete and maybe it's just something that looks cleaner and is easier to program, but I would have expected the arrow to move around the circle instead, like how a spinner works for a board game.

Clocks and Gauges

this \"clock\" from the cellar is actually a gauge
There's really just a shitload of circles with rotating arrows/needles throughout this series. u/DeFewquently made a post about how the texture for this supposed "clock thing" REALLY resembles a gauge. Somebody mentioned in the comments of the thread (linked below) that analog displays sometimes use "needles" as indicators—another repeated motif.
And of course, how could I forget about the motif of actual clocks, especially because time seems to have some real significance! People have noted that clocks hands and the windmill will change directions if watched for long enough, switching between clockwise and counterclockwise. My guess is that the generations are gradually changing, like a combination of a clock and the dial selector. The gradient with several "slices" of colors could refer to that gradual change, but also note how the colors don't transition continuously. There are definite divides where the color changes (just a little hard to see because the resolution), which might correspond to visible changes on the player's screen.
Imagine we're watching the windmill and it's spinning clockwise. Say the hour hand is pointing at 12 PM. Gradually, as time passes, it gets closer and closer to 1 PM, but once it hits 1 exactly, the windmill switches directions and starts spinning counterclockwise. Maybe at 2 PM, the windmill is reflected on it's y-axis. At 3 PM, the windmill rotates 180 degrees about its base, so it's underground and all we can see is the placeholder platform.
Actually, to keep with my fixed-point theory, I think the windmill would actually be staying in place, but our angle of perception and the world around it are what's moving.

Garalina Logo

garalina logo in petscop 1, possibly gen 8
garalina logo in petscop 20, gen 6
garalina logo in petscop 14, possibly gen 10
OKAY AND HOW COULD I NOT MENTION THE LOGO? On the Petscop Fandom wiki, they say "The current generation may be indicated by the orientation of the Garalina logo on startup in sets of 22.5 degrees." There are fifteen generations, but 22.5*15 is only 337.5 degrees total. Referring to the daisy that lets you lower the platform with Care NLM, maybe 0 counts as a generation, so gen 0 to 15 means 16 total. 22.5*16 equals a nice even 360 degrees :)
Do you remember the number of plane orientation pairs that we counted in the Sort-test recording? Do you remember how many slices were on the Dorito Dial? We can do the math out together because it's been a long day for me too:
(36 combinations of plane orientations) x (10 slices) = 360 degrees!!!!
I'm not sure what the slices would correspond to because my initial thought was generations, but the numbers don't fit as nicely. I know I'm operating with some confirmation bias here, but I think there's been multiple references to angles and degrees (I don't have any other specific examples though). Also, I'm notoriously bad at counting, so uh just ignore this if there aren't actually 36 pink/blue square pairs.
It's interested how the estimated generations for each logo rotation are all even. I think there should be some correlation between all the even numbers and all the odd numbers (e.g. Even Care vs Odd care, binary, duality, she loves me she loves me not, on/off). My intuition just feels like there's something to do with all that, but I can't place my finger on it.
There might be more examples (especially of arrow motifs), but I've been procrastinating on finals to write this post, so that's all for now! If you know any other examples, reply please :) Also please let me know if anything similar has been discussed before, or if you have any constructive criticism about my theory. I'm having a lot of fun with this theory, and I think the idea of geometric transformations makes a lot of sense within the framework of how Playstation polygon graphics operate, so I hope somebody else might agree with me :)
EDIT:

overlapping dimensions

>it seems more likely that the solution is that there is some kind of visual/locational/chronological anomaly. In the game's "Reality," Paul is running into a closed door, and then back and forth along the wall, but in the world of the "demo" recording, he's actually entering the bedroom and interacting with it.
What if the anomaly is "dimensional" aka generational? Two dimensions overlaid could result in freaky visual/locational/chronological stuff going on.
Let me explain what I mean by overlaying dimensions. You can think about it like old school transparencies that teachers used with overhead projectors. There are two plastic sheets that already have things printed on them. One has math problems like 2 + 2 = [ ] with an empty box. The other has the solutions on it, but by looking at it without seeing the original, it looks like it's random numbers. When laid on top of each other and aligned correctly, we can now see how the solutions fit into the boxes and correlate to their respective problems.
I really like what you said about everyone is playing in their respective time period but their actions are recorded. Each transparency sheet is a separate generation or a separate recording. They were created totally separate from each other at different points in time. However, when they're overlaid, they interact with each other and create a new meaning.
For this to make sense, there probably has to be some temporal fuck shit going on, otherwise, how could a player interact with a recording "from the future"? Maybe there could be a prediction algorithm or something like that, but I also wouldn't be shocked if you told me that the Petscop game is a dimensional constant and is unaffected by the concept of chronology and time.
Oh but anyways, sorry, back to the open door. Do you know how you can create an overlay mask in Photoshop and stuff? Um, it would be like have two different colored sheets of paper with one on top of the other. You could use a mask and "cut out" shapes in the top sheet, so that certain parts of the bottom sheet are visible.
When it comes to the generations though, I feel like the visual change would be discrete like how I described with the dial. However, in terms of physical space, there are instances where the door is physically "cut out" of Paul's dimension, but visually, he's confined to the boundaries of his own dimension. I'm not sure if I'm getting my idea across well, but I'm thinking about him using this method to get into the bedroom with the two beds. Paul is just running into the wall on his screen, but the video is edited to show a clip where we can see how Paul's movements take him around the bedroom space.
Oh I can relate this to fixed points too. Imagine you have a rod pointing up and there are two wooden boards layered on top of each other. The boards each have little mazes carved out of them that are similar, but slightly different. Paul is the fixed rod. When he "moves" it's not the rod that's moving. His button inputs are actually moving the world around him, or the wooden boards, in this example.
For the most part, the two boards can and should move in sync as long as their mazes are the same. However, say that there's a path which extends south on the bottom board, but not the top. When Paul "moves" down, the top board can't go anywhere. However, the bottom board can still follow Paul's instructions, so relative to the bottom maze, Paul is changing location and moving. Even if he's at the dead end for the top board, he can keep moving with respect to the bottom board. Visually, it doesn't seem like Paul is changing location because he's stuck in this dead end. Functionally, he is still traversing the bottom maze and can access different areas on the bottom board that are otherwise unreachable on the top.
(I'm sorry I'm saying top and bottom so much. I'm bad at brevity but I'll try to switch things up.) (hehe)
I actually like this door theory more than my original one because I think its more generally applicable (the player is fixed versus some arbitrary asset like the door).
And I think this analysis satisfies your description, stormy? From the view point of Paul, what displays on his screen is a lie—visually, we are perceiving the world incorrectly.

Image sources:
submitted by bittermelona to Petscop [link] [comments]

[OC] Predicting the 2019-20 Coach of the Year

For those interested, this is part of a very long blog post here where I explain my entire thought process and methodology.
This post also contains a series of charts linked to here.

Introduction

Machine Learning models have been used to predict everything in basketball from the All Star Starters to James Harden’s next play. One model that has never been made is a successful Coach of the Year Predictor. The goal of this project is to create such a model.
Of course, creating such a model is challenging because, ultimately, the COY is awarded via voting and inherently adds a human element. As we will discover in this post, accounting for these human elements (e.g. recency bias, weighing storylines, climate around the team) makes it quite challenging. Having said this, I demonstrate how we can gain insight into what voters have valued in the past, allowing us to propose the most likely candidates quite accurately.

Methods

Data Aggregation

First, I created a database of all the coaches referred to in Basketball Reference's coachs index
Coach statistics were acquired from the following template url:
f'https://www.basketball- reference.com/leagues/NBA_{season_end_year}_coaches.html'
Team statistics were acquired from the following template url:
f'https://www.basketball- reference.com/teams/{team_abbreviation}/{season_end_year}.html'
I leveraged the new basketball-reference-scraper Python module to simplify the process.
After some data engineering that I describe completely in the post, I settled on the following features.
Non numerical data Coach Statistics Team Data
COACH SEASONS WITH FRANCHISE SEASON
TEAM SEASONS OVERALL FG
CURRENT SEASON GAMES FGA
CURRENT SEASON WINS FG%
FRANCHISE SEASON GAMES 3P
FRANCHISE SEASON WINS 3PA
CAREER SEASON GAMES 3P%
CAREER SEASON WINS 2P
FRANCHISE PLAYOFF GAMES 2PA
FRANCHISE PLAYOFF WINS 2P%
CAREER PLAYOFF GAMES FT
CAREER PLAYOFF WINS FTA
COY FT%
ORB
DRB
TRB
AST
STL
BLK
TOV
PF
PTS
OPP_G
OPP_FG
OPP_FGA
OPP_FG%
OPP_3P
OPP_3PA
OPP_3P%
OPP_2P
OPP_2PA
OPP_2P%
OPP_FT
OPP_FTA
OPP_FT%
OPP_ORB
OPP_DRB
OPP_TRB
OPP_AST
OPP_STL
OPP_BLK
OPP_TOV
OPP_PF
OPP_PTS
AGE
PW
PL
MOV
SOS
SRS
ORtg
DRtg
NRtg
PACE
FTr
TS%
eFG%
TOV%
ORB%
FT/FGA
OPP_eFG%
OPP_TOV%
DRB%
OPP_FT/FGA
For obtaining a full description of each statistic, please refer to Basketball Reference's glossary.

Data Exploration

First, I computed the correlation between the COY label and all the other features and sorted them. Here are some of the top statistics that correlate with the award along with their Pearson correlation coefficient.
Statistic Pearson coefficient
CURRENT SEASON WINS 0.21764609944203592
SRS 0.20748396385759718
MOV 0.20740447792956693
NRtg 0.20613382194841318
PW 0.20282119218684597
PL -0.19850434198291064
DRtg -0.12967106743277185
ORtg 0.11896730313375109
As expected, the one of the most important features appears to be CURRENT SEASON WINS.
It is interesting the PW and PL correlate so much. This correlation indicates that not only does performance matter, but disparity between expected performance and reality matters significantly as well.
The weight put towards SRS, MOV, and NRtg also provide insight into how the COY is selected. Apparently, not only does it matter whether a team wins or not, but it also matters how they win. For example, the Bucks are typically winning games at an average of ~13 ppg this year, which would heavily favor them.
The high weight toward SRS(defined as a rating that takes into account average point differential and strength of schedule) indicates that it is even more important how a team performs against other challenging opponents. For example, no one should and does care about the Bucks crushing the Warriors, but they should care if they beat the Lakers.
Let's explore the CURRENT SEASON WINS statistic a little more using a box plot.
Box Plot
It appears coaches need to win ~50+ games for an 82 game season in order to be eligible. The exception being Mike Dunleavy’s minimum win season, there were only 50 games since it was a lockout season. Hence, that explains the outlier case.
Another interesting data point is the unfortunate coach who won the most games, but did not win the award. This turned out to be Phil Jackson, one year after his 72 win season in 1995-96 appeared to underperform by winning only 69 games. This, once again, indicates that the COY award takes into account historical performance. Who won instead? Pat Riley, with 61 wins.
Here are some histograms of the MOV and SRS where the blue plots indicate COY's and orange plots indicate NON-COY's.
As expected, COY’s are expected to dominate their teams and not just defeat them.

Oversampling

Before we begin, there is one key flaw in our dataset to look into. Namely, the two classes are not balanced at all.
Looking into the counts we have 1686 non-COY's and 43 COY's (as expected). This disparity can lead to a bad model, so how did I fix this?

SMOTE Oversampling

SMOTE (Synthetic Minority Over-sampling Technique) is a method of oversampling to even the distribution of the two classes. SMOTE takes a random sample from the minority class (COY=1 in our case) and computes it k-nearest neighbors. It chooses one of the neighbors and computes the vector between the sample and the neighbor. Next, it multiplies this vector by a random number between 0 and 1 and adds the vector to the original random sample to obtain a new data point.
See more details here.

Model Selection and Metrics

For this binary classification problem, we'll use 5 different models. Each model had its hyperparameters fine tuned using Grid Search Cross Validation to provide the best metrics. Here are all the models with a short description of each one: * Decision Tree Classifier - with Shannon's entropy as the criterion and a maximum depth of 37. * Random Forest Classifier - using the gini index as the criterion, maximum depth of 35 and maximum number of features of 5. * Logistic Classifier - using the simple ordinary least squares method * Support Vector Machine - with a linear kernel and C=1000 * Neural Network - a simple 6 layer network consisting of 80, 40, 20, 10, 5, 1 nodes, respectively (chosen to correspond with the number of features). I also used early stopping and 20% dropouts on each layer to prevent overfitting.
The metrics that will be used to evaluate our models are: NOTE that: TP=True Positives (Predicted COY and was a COY), TN=True Negatives (Predicted Not COY and was Not COY), FP=False Positives (Predicted COY and was Not COY), FN=False Negatives (Predicted Not COY and was COY)
  • Accuracy - % of correctly categorized instances ; Accuracy = (TP+TN)/(TP+TN+FP+FN)
  • Recall - Ability to categorize (+) class (COY) ; Recall = TP/(TP+FN)
  • Precision - How many of TP were correct ; Precision = TP/(TP+FP)
  • F1 - Balances Precision and Recall ; F1 = 2(Precision * Recall) / (Precision + Recall)

Results

Model Accuracy Recall Precision F1
Decision Tree 0.963 0.977 0.952 0.964
Random Forest 0.985 0.997 0.974 0.986
Logistic 0.920 0.980 0.870 0.922
SVC 0.959 0.991 0.932 0.960
Neural Network 0.898 1.0 0.833 0.909
In terms of all metrics the Random Forest outperforms all. Moreover, the Random Forest boasts an extremely high recall which is our most important metric. When predicting the Coach of the Year, we want to be able to predict the positive class best, which is indicated by a high recall.

Confusion Matrices

Confusion Matrices are another way of visualization our models' performances. Confusion Matrices are nxn matrices where the columns represent the actual class and the rows represent the class predicted by the model.
In the case of a binary classification problem, we obtain a 2x2 matrix with the true positives (bottom right), true negatives (top left), false positive (top right), and false negatives (bottom left).
Here are the confusion matrices for the Decision Tree, Random Forest, Logistic Classifier, SVC, and Neural Network.
Looking at the confusion matrices we can clearly see the disparity between the Random Forest Classifier and other classifiers. Evidently, the Random Forest Classifier is the best option.

Random Forest Evaluation

So what made the Random Forest so good? What features did it use that enabled it to make such accurate predictions?
I charted the feature importances of the Random Forest and plotted them in order here.
Here are some explicit numbers:
Feature % Contribution
CURRENT SEASON WINS 6.569329857214043
SRS 6.368785568654217
PW 6.059094690243399
NRtg 5.5519116066060175
MOV 4.473122672559081
PL 3.643349558354282
... ...
See more in my blog post.
I found it, once again, interesting that SRS such an important feature. It appears that the Random Forest took the correlation predicted earlier into account.
However, we see that other statistics matter significantly too, like CURRENT SEASON WINS, NRtg, and MOV as we predicted.
Something one wouldn’t anticipate is the contribution of factors outside of this season like FRANCHISE and CAREER features. Along these lines, one wouldn’t expect PW or PL to matter too much, but this model indicates that it is one of the most important features.
Let’s also take a look at where the random forest failed. If you recall from the confusion matrix, there was one instance where a COY was classified as NOT COY.
The point is the 1976 COY who was categorized as not COY. This individual was coach Bill Fitch of the 1975-76 Cleveland Cavaliers. He had a modest win record of 49-33 during an overall down year where the top record was the 54-28 Lakers. Looking at the modern era where 60 win records and obscene statistics are put up on a regular basis, I would say that this is not a terrible error on our model's part.
The reason the model may have classified this as a NOT COY instance is due to the fact that the team's statistics aren't all that impressive, but impressive with respect to THAT year. This lack of incorporating other team performances during the year may be the biggest flaw in our model.

Predicting the next Coach of the Year

Unfortunately, we do not have all the statistics for the current year, but we will obtain what we can and modify the data as we did earlier.
Note that all our data is PER GAME, so for all of these statistics, we will just use the PER GAME statistics up to this point (1/21/20)
The only unrealistic statistic is, then, CURRENT SEASON statistics. We will assume CURRENT SEASON GAMES will be 82 for all coaches and obtain CURRENT SEASON WINS from 538's ELO projections on 1/21/20.
Once again, all other stats were acquired via the basketball_reference_scraper Python package.
Team Probability to win COY
MIL 0.49
TOR 0.46
LAC 0.36
BOS 0.31
HOU 0.23
LAL 0.22
DAL 0.22
MIA 0.17
DEN 0.16
IND 0.13
UTA 0.12
PHI 0.12
DET 0.09
NOP 0.07
WAS 0.05
SAS 0.04
ORL 0.04
CHI 0.04
BRK 0.04
POR 0.03
PHO 0.03
OKC 0.03
CHO 0.03
NYK 0.02
SAC 0.01
MIN 0.01
GSW 0.01
ATL 0.01
MEM 0.0
CLE 0.0
This shows the probability of each coach to win COY in the current season. Let's take a look at each of the candidates in order:
1) Milwaukee Bucks & Mike Budenholzer (49%)
Mike Budenholzer was the COY in the 2018-19 season and, objectively, the top candidate for COY this year as well. The Bucks are on a nearly 70-win pace which would automatically elevate him to the top spot.
However, the model is purely objective and fails to incorporate human elements such as the fact that individuals look at the Bucks skeptically as a 'regular season team'. Voters will likely avoid Budenholzer until there is more playoff success.
Moreover, Budenholzer won last year and voters almost never vote for the same candidate twice in a row. In fact, a repeat performance has never occurred in the COY award.
We see here the flaw in the model to not weight the human elements of recency bias against previous COY's and playoff success sufficiently.
2) Toronto Raptors & Nick Nurse (46%)
The Raptors are truly an incredible story this year. No one expected them to be this good. Even the ELO ratings put them at an expected 56 wins this season and be tied for the 3rd best record in the league behind the Lakers and Bucks.
The disparity between what people expected of the Raptors and what has actually transpired (despite injuries to significant players such as Lowry and Siakam) indicates that Nurse would be a viable candidate for COY.
3) Los Angeles Clippers & Doc Rivers (36%)
Despite the model favoring Doc Rivers, I believe it is unlikely that he wins COY due to the current stories circulating around the Clippers.
Everyone came into the season expecting the Clippers to blow everyone out of the water in the playoffs. No one expects the Clippers to exceed expectations during the regular season, especially with their superstars Kawhi Leonard and Paul George being the role models of load management.
4) Boston Celtics & Brad Stevens (31%)
Brad Stevens is another likely candidate for the COY. Not ony are the Celtics objectively impressive, but they also have the narrative on their side. After last year's disappointing performance, people questioned Stevens, but their newfound success without Kyrie Irving has pushed the blame onto Irving over Stevens. Moreover, significant strides have been made by their young players Jaylen Brown and Jayson Tatum vaulting them into Eastern Conference champion contention.
5) Los Angeles Lakers & Frank Vogel (22%)
Being in tune with the current basketball landscape through podcasts and articles, I can tell that Frank Vogel's campaign for the COY is quite strong. Over and over again we hear praises from players like Anthony Davis and Danny Green on the recent Lowe Post on how happy the Lakers are.
With the gaudy record, spotlight and percolating positive energy around the Lakers, Vogel is a very viable pick for the COY.
6) Dallas Mavericks & Rick Carlisle (22%)
Tied with Vogel is Rick Carlisle and the Dallas Mavericks. The Dallas Mavericks, along with the Raptors, are perhaps the most unexpected successful team this season. Looking at their roster, no one stands out except for Porzingis and Doncic, but they still tout a predicted record of 50-32.
Once again, the disparity between expectations and reality puts Carlisle in high contention of the COY.

Conclusion

Overall, I'm quite pleased with the Random Forest model's metrics. The predictions made by the model for the current 2019-20 appear on point as well. The model appears to favor the disparity between what people expected of teams and their performance on the court quite well. However, the flaw in the model is the lack of weighing recent events properly as we saw with coach Budenholzer.
Once again, predicting the COY is a challenging task and we cannot expect the model to be perfect. Yet, we can gain insight into what voters have valued in the past, allowing us to propose the most likely candidates quite accurately.
submitted by vagartha to nba [link] [comments]

[OC] Predicting the 2019-20 Coach of the Year

For those interested, this is part of a very long blog post here where I explain my entire thought process and methodology.
This post also contains a series of charts linked to here.

Introduction

Machine Learning models have been used to predict everything in basketball from the All Star Starters to James Harden’s next play. One model that has never been made is a successful Coach of the Year Predictor. The goal of this project is to create such a model.
Of course, creating such a model is challenging because, ultimately, the COY is awarded via voting and inherently adds a human element. As we will discover in this post, accounting for these human elements (e.g. recency bias, weighing storylines, climate around the team) makes it quite challenging. Having said this, I demonstrate how we can gain insight into what voters have valued in the past, allowing us to propose the most likely candidates quite accurately.

Methods

Data Aggregation

First, I created a database of all the coaches referred to in Basketball Reference's coachs index
Coach statistics were acquired from the following template url:
f'https://www.basketball- reference.com/leagues/NBA_{season_end_year}_coaches.html'
Team statistics were acquired from the following template url:
f'https://www.basketball- reference.com/teams/{team_abbreviation}/{season_end_year}.html'
I leveraged the new basketball-reference-scraper Python module to simplify the process.
After some data engineering that I describe completely in the post, I settled on the following features.
Non numerical data Coach Statistics Team Data
COACH SEASONS WITH FRANCHISE SEASON
TEAM SEASONS OVERALL FG
CURRENT SEASON GAMES FGA
CURRENT SEASON WINS FG%
FRANCHISE SEASON GAMES 3P
FRANCHISE SEASON WINS 3PA
CAREER SEASON GAMES 3P%
CAREER SEASON WINS 2P
FRANCHISE PLAYOFF GAMES 2PA
FRANCHISE PLAYOFF WINS 2P%
CAREER PLAYOFF GAMES FT
CAREER PLAYOFF WINS FTA
COY FT%
ORB
DRB
TRB
AST
STL
BLK
TOV
PF
PTS
OPP_G
OPP_FG
OPP_FGA
OPP_FG%
OPP_3P
OPP_3PA
OPP_3P%
OPP_2P
OPP_2PA
OPP_2P%
OPP_FT
OPP_FTA
OPP_FT%
OPP_ORB
OPP_DRB
OPP_TRB
OPP_AST
OPP_STL
OPP_BLK
OPP_TOV
OPP_PF
OPP_PTS
AGE
PW
PL
MOV
SOS
SRS
ORtg
DRtg
NRtg
PACE
FTr
TS%
eFG%
TOV%
ORB%
FT/FGA
OPP_eFG%
OPP_TOV%
DRB%
OPP_FT/FGA
For obtaining a full description of each statistic, please refer to Basketball Reference's glossary.

Data Exploration

First, I computed the correlation between the COY label and all the other features and sorted them. Here are some of the top statistics that correlate with the award along with their Pearson correlation coefficient. |Statistic|Pearson coefficient| |--|--| |CURRENT SEASON WINS|0.21764609944203592| |SRS|0.20748396385759718| |MOV|0.20740447792956693| |NRtg|0.20613382194841318| |PW|0.20282119218684597| |PL|-0.19850434198291064| |DRtg|-0.12967106743277185| |ORtg|0.11896730313375109|
As expected, the one of the most important features appears to be CURRENT SEASON WINS.
It is interesting the PW and PL correlate so much. This correlation indicates that not only does performance matter, but disparity between expected performance and reality matters significantly as well.
The weight put towards SRS, MOV, and NRtg also provide insight into how the COY is selected. Apparently, not only does it matter whether a team wins or not, but it also matters how they win. For example, the Bucks are typically winning games at an average of ~13 ppg this year, which would heavily favor them.
The high weight toward SRS(defined as a rating that takes into account average point differential and strength of schedule) indicates that it is even more important how a team performs against other challenging opponents. For example, no one should and does care about the Bucks crushing the Warriors, but they should care if they beat the Lakers.
Let's explore the CURRENT SEASON WINS statistic a little more using a box plot.
Box Plot
It appears coaches need to win ~50+ games for an 82 game season in order to be eligible. The exception being Mike Dunleavy’s minimum win season, there were only 50 games since it was a lockout season. Hence, that explains the outlier case.
Another interesting data point is the unfortunate coach who won the most games, but did not win the award. This turned out to be Phil Jackson, one year after his 72 win season in 1995-96 appeared to underperform by winning only 69 games. This, once again, indicates that the COY award takes into account historical performance. Who won instead? Pat Riley, with 61 wins.
Here are some histograms of the MOV and SRS where the blue plots indicate COY's and orange plots indicate NON-COY's.
As expected, COY’s are expected to dominate their teams and not just defeat them.

Oversampling

Before we begin, there is one key flaw in our dataset to look into. Namely, the two classes are not balanced at all.
Looking into the counts we have 1686 non-COY's and 43 COY's (as expected). This disparity can lead to a bad model, so how did I fix this?

SMOTE Oversampling

SMOTE (Synthetic Minority Over-sampling Technique) is a method of oversampling to even the distribution of the two classes. SMOTE takes a random sample from the minority class (COY=1 in our case) and computes it k-nearest neighbors. It chooses one of the neighbors and computes the vector between the sample and the neighbor. Next, it multiplies this vector by a random number between 0 and 1 and adds the vector to the original random sample to obtain a new data point.
See more details here.

Model Selection and Metrics

For this binary classification problem, we'll use 5 different models. Each model had its hyperparameters fine tuned using Grid Search Cross Validation to provide the best metrics. Here are all the models with a short description of each one: * Decision Tree Classifier - with Shannon's entropy as the criterion and a maximum depth of 37. * Random Forest Classifier - using the gini index as the criterion, maximum depth of 35 and maximum number of features of 5. * Logistic Classifier - using the simple ordinary least squares method * Support Vector Machine - with a linear kernel and C=1000 * Neural Network - a simple 6 layer network consisting of 80, 40, 20, 10, 5, 1 nodes, respectively (chosen to correspond with the number of features). I also used early stopping and 20% dropouts on each layer to prevent overfitting.
The metrics that will be used to evaluate our models are: NOTE that: TP=True Positives (Predicted COY and was a COY), TN=True Negatives (Predicted Not COY and was Not COY), FP=False Positives (Predicted COY and was Not COY), FN=False Negatives (Predicted Not COY and was COY)
  • Accuracy - % of correctly categorized instances ; Accuracy = (TP+TN)/(TP+TN+FP+FN)
  • Recall - Ability to categorize (+) class (COY) ; Recall = TP/(TP+FN)
  • Precision - How many of TP were correct ; Precision = TP/(TP+FP)
  • F1 - Balances Precision and Recall ; F1 = 2(Precision * Recall) / (Precision + Recall)

Results

Model Accuracy Recall Precision F1
Decision Tree 0.963 0.977 0.952 0.964
Random Forest 0.985 0.997 0.974 0.986
Logistic 0.920 0.980 0.870 0.922
SVC 0.959 0.991 0.932 0.960
Neural Network 0.898 1.0 0.833 0.909
In terms of all metrics the Random Forest outperforms all. Moreover, the Random Forest boasts an extremely high recall which is our most important metric. When predicting the Coach of the Year, we want to be able to predict the positive class best, which is indicated by a high recall.

Confusion Matrices

Confusion Matrices are another way of visualization our models' performances. Confusion Matrices are nxn matrices where the columns represent the actual class and the rows represent the class predicted by the model.
In the case of a binary classification problem, we obtain a 2x2 matrix with the true positives (bottom right), true negatives (top left), false positive (top right), and false negatives (bottom left).
Here are the confusion matrices for the Decision Tree, Random Forest, Logistic Classifier, SVC, and Neural Network.
Looking at the confusion matrices we can clearly see the disparity between the Random Forest Classifier and other classifiers. Evidently, the Random Forest Classifier is the best option.

Random Forest Evaluation

So what made the Random Forest so good? What features did it use that enabled it to make such accurate predictions?
I charted the feature importances of the Random Forest and plotted them in order here.
Here are some explicit numbers:
Feature % Contribution
CURRENT SEASON WINS 6.569329857214043
SRS 6.368785568654217
PW 6.059094690243399
NRtg 5.5519116066060175
MOV 4.473122672559081
PL 3.643349558354282
... ...
See more in my blog post.
I found it, once again, interesting that SRS such an important feature. It appears that the Random Forest took the correlation predicted earlier into account.
However, we see that other statistics matter significantly too, like CURRENT SEASON WINS, NRtg, and MOV as we predicted.
Something one wouldn’t anticipate is the contribution of factors outside of this season like FRANCHISE and CAREER features. Along these lines, one wouldn’t expect PW or PL to matter too much, but this model indicates that it is one of the most important features.
Let’s also take a look at where the random forest failed. If you recall from the confusion matrix, there was one instance where a COY was classified as NOT COY.
The point is the 1976 COY who was categorized as not COY. This individual was coach Bill Fitch of the 1975-76 Cleveland Cavaliers. He had a modest win record of 49-33 during an overall down year where the top record was the 54-28 Lakers. Looking at the modern era where 60 win records and obscene statistics are put up on a regular basis, I would say that this is not a terrible error on our model's part.
The reason the model may have classified this as a NOT COY instance is due to the fact that the team's statistics aren't all that impressive, but impressive with respect to THAT year. This lack of incorporating other team performances during the year may be the biggest flaw in our model.

Predicting the next Coach of the Year

Unfortunately, we do not have all the statistics for the current year, but we will obtain what we can and modify the data as we did earlier.
Note that all our data is PER GAME, so for all of these statistics, we will just use the PER GAME statistics up to this point (1/21/20)
The only unrealistic statistic is, then, CURRENT SEASON statistics. We will assume CURRENT SEASON GAMES will be 82 for all coaches and obtain CURRENT SEASON WINS from 538's ELO projections on 1/21/20.
Once again, all other stats were acquired via the basketball_reference_scraper Python package.
Team Probability to win COY
MIL 0.49
TOR 0.46
LAC 0.36
BOS 0.31
HOU 0.23
LAL 0.22
DAL 0.22
MIA 0.17
DEN 0.16
IND 0.13
UTA 0.12
PHI 0.12
DET 0.09
NOP 0.07
WAS 0.05
SAS 0.04
ORL 0.04
CHI 0.04
BRK 0.04
POR 0.03
PHO 0.03
OKC 0.03
CHO 0.03
NYK 0.02
SAC 0.01
MIN 0.01
GSW 0.01
ATL 0.01
MEM 0.0
CLE 0.0
This shows the probability of each coach to win COY in the current season. Let's take a look at each of the candidates in order:
1) Milwaukee Bucks & Mike Budenholzer (49%)
Mike Budenholzer was the COY in the 2018-19 season and, objectively, the top candidate for COY this year as well. The Bucks are on a nearly 70-win pace which would automatically elevate him to the top spot.
However, the model is purely objective and fails to incorporate human elements such as the fact that individuals look at the Bucks skeptically as a 'regular season team'. Voters will likely avoid Budenholzer until there is more playoff success.
Moreover, Budenholzer won last year and voters almost never vote for the same candidate twice in a row. In fact, a repeat performance has never occurred in the COY award.
We see here the flaw in the model to not weight the human elements of recency bias against previous COY's and playoff success sufficiently.
2) Toronto Raptors & Nick Nurse (46%)
The Raptors are truly an incredible story this year. No one expected them to be this good. Even the ELO ratings put them at an expected 56 wins this season and be tied for the 3rd best record in the league behind the Lakers and Bucks.
The disparity between what people expected of the Raptors and what has actually transpired (despite injuries to significant players such as Lowry and Siakam) indicates that Nurse would be a viable candidate for COY.
3) Los Angeles Clippers & Doc Rivers (36%)
Despite the model favoring Doc Rivers, I believe it is unlikely that he wins COY due to the current stories circulating around the Clippers.
Everyone came into the season expecting the Clippers to blow everyone out of the water in the playoffs. No one expects the Clippers to exceed expectations during the regular season, especially with their superstars Kawhi Leonard and Paul George being the role models of load management.
4) Boston Celtics & Brad Stevens (31%)
Brad Stevens is another likely candidate for the COY. Not ony are the Celtics objectively impressive, but they also have the narrative on their side. After last year's disappointing performance, people questioned Stevens, but their newfound success without Kyrie Irving has pushed the blame onto Irving over Stevens. Moreover, significant strides have been made by their young players Jaylen Brown and Jayson Tatum vaulting them into Eastern Conference champion contention.
5) Los Angeles Lakers & Frank Vogel (22%)
Being in tune with the current basketball landscape through podcasts and articles, I can tell that Frank Vogel's campaign for the COY is quite strong. Over and over again we hear praises from players like Anthony Davis and Danny Green on the recent Lowe Post on how happy the Lakers are.
With the gaudy record, spotlight and percolating positive energy around the Lakers, Vogel is a very viable pick for the COY.
6) Dallas Mavericks & Rick Carlisle (22%)
Tied with Vogel is Rick Carlisle and the Dallas Mavericks. The Dallas Mavericks, along with the Raptors, are perhaps the most unexpected successful team this season. Looking at their roster, no one stands out except for Porzingis and Doncic, but they still tout a predicted record of 50-32.
Once again, the disparity between expectations and reality puts Carlisle in high contention of the COY.

Conclusion

Overall, I'm quite pleased with the Random Forest model's metrics. The predictions made by the model for the current 2019-20 appear on point as well. The model appears to favor the disparity between what people expected of teams and their performance on the court quite well. However, the flaw in the model is the lack of weighing recent events properly as we saw with coach Budenholzer.
Once again, predicting the COY is a challenging task and we cannot expect the model to be perfect. Yet, we can gain insight into what voters have valued in the past, allowing us to propose the most likely candidates quite accurately.
submitted by vagartha to nbadiscussion [link] [comments]

MAME 0.213

MAME 0.213

It's really about time we released MAME 0.213, with more of everything we know you all love. First of all, we’re proud to present support for the first Hegener + Glaser product: the “brikett” chess computers, Mephisto, Mephisto II and Mephisto III. As you can probably guess, there’s an addition from Nintendo’s Game & Watch line. This month it’s Mario’s Bombs Away. On a related note, we’ve also added Elektronika’s Kosmicheskiy Most, exported as Space Bridge, which is an unlicensed total conversion of the Game & Watch title Fire. If you haven’t played any of the handheld LCD games in MAME, you’re missing something special – they look superb with external scanned and traced artwork.
On the arcade side, we’ve added The Destroyer From Jail (a rare Philko game), and alternate regional versions of Block Out and Super Shanghai Dragon’s Eye. The CD for Simpsons Bowling has been re-dumped, resolving some long-standing issues. With its protection microcontroller dumped and emulated, Birdie Try is now fully playable. Protection microcontrollers for The Deep and Last Mission have also been dumped and emulated. Improvements to Seibu hardware emulation mean Banpresto’s SD Gundam Sangokushi Rainbow Tairiku Senki is now playable, and sprite priorities in Seibu Cup Soccer have been improved.
In computer emulation, two interesting DOS compatible machines based on the Intel 80186 CPU are now working: the Mindset Personal Computer, and the Dulmont Magnum. The Apple II software lists have been updated to include almost all known clean cracks and original flux dumps, and the Apple II gameport ComputerEyes frame grabber is now emulated. We’ve received a series of submissions that greatly improve emulation of the SWTPC S/09 and SS-30 bus cards. On the SGI front, the 4D/20 now has fully-working IRIX 4.0.5 via serial console, and a whole host of improvements have gone into the Indy “Newport” graphics board emulation. Finally, MAME now supports HDI, 2MG and raw hard disk image files.
As always, you can get the source and Windows binary packages from the download page.

MAMETesters Bugs Fixed

New working machines

New working clones

Machines promoted to working

Clones promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to emulation [link] [comments]

MAME 0.213

MAME 0.213

It's really about time we released MAME 0.213, with more of everything we know you all love. First of all, we’re proud to present support for the first Hegener + Glaser product: the “brikett” chess computers, Mephisto, Mephisto II and Mephisto III. As you can probably guess, there’s an addition from Nintendo’s Game & Watch line. This month it’s Mario’s Bombs Away. On a related note, we’ve also added Elektronika’s Kosmicheskiy Most, exported as Space Bridge, which is an unlicensed total conversion of the Game & Watch title Fire. If you haven’t played any of the handheld LCD games in MAME, you’re missing something special – they look superb with external scanned and traced artwork.
On the arcade side, we’ve added The Destroyer From Jail (a rare Philko game), and alternate regional versions of Block Out and Super Shanghai Dragon’s Eye. The CD for Simpsons Bowling has been re-dumped, resolving some long-standing issues. With its protection microcontroller dumped and emulated, Birdie Try is now fully playable. Protection microcontrollers for The Deep and Last Mission have also been dumped and emulated. Improvements to Seibu hardware emulation mean Banpresto’s SD Gundam Sangokushi Rainbow Tairiku Senki is now playable, and sprite priorities in Seibu Cup Soccer have been improved.
In computer emulation, two interesting DOS compatible machines based on the Intel 80186 CPU are now working: the Mindset Personal Computer, and the Dulmont Magnum. The Apple II software lists have been updated to include almost all known clean cracks and original flux dumps, and the Apple II gameport ComputerEyes frame grabber is now emulated. We’ve received a series of submissions that greatly improve emulation of the SWTPC S/09 and SS-30 bus cards. On the SGI front, the 4D/20 now has fully-working IRIX 4.0.5 via serial console, and a whole host of improvements have gone into the Indy “Newport” graphics board emulation. Finally, MAME now supports HDI, 2MG and raw hard disk image files.
As always, you can get the source and Windows binary packages from the download page.

MAMETesters Bugs Fixed

New working machines

New working clones

Machines promoted to working

Clones promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to MAME [link] [comments]

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