Introduction and Review

In my last post, I introduced you to a project I am putting together that will help me win my fantasy league. I made some simple graphics that showed which players are doing well and who is playing efficiently. Just to catch you up, I scraped every NBA player’s box score data from every game played so far this season, added a new variable that calculates how many fantasy points that player would have scored that night, and made a few plots that show some insights about the data, such as points leaders and fantasy points/minute played. While there was some useful information in those charts, today I will attempt to create one more that contains as much useful info as possible and answers my main question: Which players should I add to my fantasy team? Remember, if you want to check out what I’ve done so far in this project or see how I scraped this data, you can view my last post or check out my github repository here.

My Data Story

One of the best ways to win in fantasy is to add players that are performing better than expected. To that end, I created a scatterplot using plotly that compares a player’s rank this year with their average draft position before the season started. This way, I can clearly see who is overperforming and who is underperforming. Each point represents a specific player, so it wouldn’t be very helpful if we didn’t know where each player was. This is why I chose a plotly graph. Let’s take a look.

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With plotly’s really nice features, we can hover over a specific point to see which player it represents. This allows us to see how specific players are doing. Specifically, the players on the right side have a ranking better than their draft position, and the players on the left side are not performing well relative to their expectations. I also added hover data so that, along with the player’s name, you can see what team they are on, what their average draft position was, and their position, in case you need to look for players of a certain position to add to your team.

Conclusion

One of the reasons I like this project so much is because there are so many possibilities for analysis from this data. For example, you can track stats in certain time periods and look at who has been the best over the last two weeks, or see who had a strong start but fell off. There are as many possibilities as you can create. I will continue to use this data as the season goes on, and hopefully it will help me to win my league! What would you do with this data? What would you add to the chart? Let me know below!