# All-NBA Predict #29 – Classifying All-NBA Players (Part XI – Predicting The Players)

So I’ve just spend the last… Jesus… 10 posts talking about random mumbo jumbo… Sensitivity, specificity, AUC, ROC… But I really haven’t actually said “Hey, Lebron’s probably going to be all-NBA”. Don’t need an algorithm for that one, fortunately, but what about… one Paul George? And I say that basically cheating in hindsight because the […]

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# All-NBA Predict #28 – Classifying All-NBA Players (Part X – Gradient Boosted Trees)

Jesus christ… we are on our 10th classifier… I’m going to have to make this my last one because I feel like I’m not doing any analysis anymore haha. NOT THAT THIS ISN’T FUN… but I’d like to actually be able to compare some of these methods together to get a better sense of how […]

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# All-NBA Predict #27 – Classifying All-NBA Players (Part VIIII – K-Nearest Neighbours)

We go from one of the most complex models to one of the least complex… K-NN here we go. Not really too much to explain here… We pick the closest observations by euclidean distance and take a vote. If the majority of them are all-NBA, then the observation is all-NBA. If the majority of them […]

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# All-NBA Predict #26 – Classifying All-NBA Players (Part VII – Support Vector Machines)

Well, the last 2 posts was probably the most I’ve ever talked about trees in my life. I usually frequent r/nba at least a few times a day, but no joke I accidentally googled ‘r/trees’ a few times just because I had trees on the mind and for some reason found myself much more relaxed […]

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# All-NBA Predict #25 – Classifying All-NBA Players (Part VIII – Neural Networks)

Alright, we’re running out of classifiers for us to try here. We’ve already gotten some pretty decent results thus far. My personal favourite has probably been just a single decision tree or the random forest. The interpretability of those models are pretty cool. LDA and QDA are also pretty neat because of their simplicity, but […]

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# All-NBA Predict #24 – Classifying All-NBA Players (Part VI – Random Forest)

In the last post, I explored a single decision tree. In this post, I’m going to expand on that single tree to look at the random forest model. Before we get into random forests, we have to understand the error breakdown of a decision tree. Intrinsically, a tree boasts higher variance compared to some other […]

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# All-NBA Predict #23 – Classifying All-NBA Players (Part V – Decision Tree)

Alrighty, so we’ve taken a look at the following classifiers: Manually selected linear boundary, LDA, QDA, and logistic regression. First one, not so scientific. Next 3, a bit of math involved, nothing too crazy. We now jump to one of the most interpretable models that exist. ESL calls the decision tree as close to an […]

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# All-NBA Predict #22 – Classifying All-NBA Players (Part IV – Logistic Regression)

Okay. So last time, we tried looking at LDA and QDA in addition to our manually chosen linear decision boundary to classify if players are all-NBA team worthy or not. We got the following results: Manually chosen linear decision boundary – 93% / 92% accuracy for all-NBA / non all-NBA respectively LDA – 69% / […]

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# All-NBA Predict #20 – Classifying All-NBA Players (Part II – Linear Discriminant Analysis)

Okay, so last time, I selected two boundaries – one a rectangle, one a linear line. Anything on one side of the shape was considered all-NBA, anything on the other was considered non all-NBA. Did it work? For the rectangle, no. Well… I guess I didn’t really try to optimize it, but it was clear […]

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# All-NBA Predict #19 – Classifying All-NBA Players (Part I – Manually Selected Boundaries)

Okay, I’ve taken a look at Win Shares and VORP so far, and it looked like there was a pretty clear line between all-nba level players and non all-nba level players. Okay, not a clear line, but there is a… quadrant? Let’s refresh our memories of what the WS vs VORP plot looks like for […]

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