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 #21 – Classifying All-NBA Players (Part III – Quadratic Discriminant Analysis)

Last post, we reviewed a few different topics Gaussian distribution Multivariate gaussian distribution Linear discriminant analysis We left off feeling a bit unsatisfied with LDA because were were getting 92% / 69% accuracy in prediction for all-NBA players and non all-NBA players respectively. I’m not too fond of that 69%, especially considering that we were […]

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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 #18 – Exploration of Historical NBA Players (Part VIII, WS & VORP)

Let’s get right into it. Last post, I looked at, well, really I learned what PER, BPM, VORP, and WS were. Basketball-reference’s blurbs now make some sense to me haha: PER – Sum up all a player’s positive accomplishments, subtract the negative accomplishments, and return a per-minute rating of a player’s performance BPM – Box […]

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All-NBA Predict #15 – Exploration of Historical NBA Players (Part VI, PCA to Explore Historical Lineups)

Last time, I explored 9 regions of our PCA bi-plot. I’ll review what I thought the 9 regions represented here: Region 1: All-Star Guards Region 2: All-Star Scorers Region 3: All-Star Big Men Region 4: Great Guards Region 5: Great All-Around / Two-Way Players Region 6: Great Big Men Region 7: Spot Up Shooters Region […]

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All-NBA Predict #14 – Exploration of Historical NBA Players (Part V, PCA to Cluster Playing Styles)

In our last post, we harnessed the power of PCA even further to look at different ways to break up the PCA bi-plot. With ggbiplot’s “ellipse” argument, we were able to check out where different players lie on the plot. To review, my 3 main questions were: How closely can I map different “types” of […]

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All-NBA Predict #13 – Exploration of Historical NBA Players (Part IV, PCA on Advanced Metrics & By Era)

Advanced Metrics Okay, so at this point, I’ve seen some of the benefits of data exploration methods. I really liked the PCA bi-plot for reasons I rambled about in the last post, namely the balance between interpretability and the value of information provided. Here, I’m just going to throw it at some of the advanced […]

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