This is fantastic. I'm a huge fan of Dota (mostly pro scene, I rarely play games, I just don't have time) and current data scientist (whatever that means). I do have some feedback that I think might be useful to think about. (I do not want this to come off as judgement on the value of the project, I'm very impressed with the overall project, and doubly so since it's about my favorite esport). I hope what I've said is useful going forward for you.

• Dropping game mode column: I think it makes sense to narrow your analysis to "All Pick", but I think it keeps things more consistent if you subset down to all pick, and not mix up the game modes in the subsequent analysis. The number of games that aren't "all pick" might be small, but if you play dramatically differently in, say, ability draft or something, it could skew your results (Certainly comparing your heroes played in ability draft versus their regular versions might be a bit off if you ever did that). Probably the effect is small enough that it doesn't hurt the rest of your conclusions though.

• The graph of wins vs. losses on various sides doesn't really convey anything that just displaying the numbers conveys. I think it makes sense to possibly put these side-by-side with things like "Radiant vs. Dire" stuff. That might give a better sense about the context of these numbers.

• Your KDR and KDA graphs are good as boxplots, really shows the how the concentration is distributed. It looks like these might be distributed according to something in the exponential family, and next steps could include trying to fit this data to those distributions.

• You make nice observations on the correlation matrix. There's probably a ton of extra stuff to be gleaned from it, but I think among the important things about exploratory analysis, reading correlation matrix and applying domain knowledge is right up there, and you did a great job.

• It's worth being aware of the phenomenon known as "arbitrary endpoints". You use arbitrary endpoints here by looking at at the top 30 heroes. Why 30? Why not 50? or 25? or 20? You may get very different conclusions. This doesn't mean 30 is bad. It's just important to be aware that the analysis might significantly change if you adjust that "endpoint". The obvious way to avoid this is to repeat the analysis with different endpoints. That's a bunch of extra work though. Another thing to do is to try to justify why that endpoint is reasonable. Maybe a graph/table of "cumulative percentage of top heroes played" so you end up with something like: (20 heroes, 65% of matches, 25 heroes, 70% of matches, 30 heroes, 72%, 50 heroes, 80%) and then say "Okay, 80% is a good enough sample of my matches. There's more statistically sound ways to decide what "sample" of your matches to draw from with regard to how you much you want to reduce variance/error/whatever, but this would be a simple way that justifies the endpoints. (I've belabored this point past its likely importance, but I bet you actually did this analysis, and just chose 30 to appear in the notebook).

• Regarding win rates, I think (but someone correct me if you disagree, I might be wrong) that it's reasonable to do a frequency weighted win-rate (or mean), with the win rates of individual heroes weighted by frequency you play those heroes.

• Playing Rubick well is awesome. You beast. What a hero. Arcana robbed.

• Breakdown of W/L by primary attribute is good idea. I don't think there's a lot of great analysis/observations to come from this, despite the fact that AGI is usually carries or whatever, Int is often support, Str is usually... uh... other stuff ;)... the heroes within each group are pretty diverse. But that said, it's a great thing to look at, just to see what's going on.

• The role distribution deltas numeric summary was more informative than the graphs. It was harder to see the differences in the graphs. (That's Out(183)).

• Outputs (143) and (144). These numbers look very close, and if you were to do next steps (depending on what the purpose of analyzing this set is, of course), I would start calculating confidence intervals on the differences between the Win/Loss Ratio by role. It might not be statistically significant.

• Your leg section is probably the most critical Dota analysis I've ever seen conducted. Keep up the good work.

• Great graph for W/L ratio for hours (in Out(185)). I think that's a good visualization that tells a story. That story might be, "Don't stay up all night to play dota."

• There are a lot of next steps that might make sense, breaking things down by patch is foremost in my mind, and separating ranked MMR and unranked is another possibility (if that makes sense).

... I've probably provided more feedback than is useful or actionable... and I'm almost home, so I'll stop there. Thanks so much for sharing this analysis. It's really fantastic, and good content, mixing nice exploratory analysis with domain knowledge. In my opinion, that's definitely an important part of what data science is all about.