More CrossFit Data Analysis – This time you can play too!

Don’t care about my post and just want the good stuff, follow this link: CrossFit Data Explorer

My last blog post was quite easily my most popular post to date (although my Searchy-type problems post did receive a fair amount of attention). One former student of mine explained that my entry had, “the most practical findings for average people. Most of your posts even I have trouble understanding … you need a PhD”.

Ok, so my blog isn’t always for the faint of heart :-). Ah well. However, this time I’m going mainstream again. Well, not quite mainstream, but as mainstream as CrossFit is :-).

Due in part to the level of interest my last post generated as well as my own curiosity, I spent some more time looking at the CrossFit Games data. I’ve had some requests to analyze the women’s data from the Canada Regional in the same way I analyzed the men’s data. I haven’t done that yet, but I think what I have done is pretty cool. And, more importantly, anyone can now interact and play with the data to discover their own interesting trends and results!

I decided to build a visual tool for interacting with the various data available on the CrossFit Games website. I started out building it as a standalone Java application, but that seemed soooo 1998. So, part way through development I abandoned the Java application in favor of a Web 2.0-style application, equipped with all the latest buzzword technologies (i.e. AJAX, jQuery, JSON, etc.).

I’ve made the application available online here, so feel free to play with it.

There’s three different visualizations available that allow you to compare athletes based on different event modalities as well as athlete rankings across various events. It should work with any URL that resolves to an overall results page (example page: Men’s Canadian Regional). I’ve pre-populated a drop down with all the regional results so you can select items from there or paste in an appropriate URL. The Men’s Canadian Regional data is loaded by default. The application may not work with Internet Explorer, so I suggest Firefox, Safari or Chrome.

In the Compare events tab, you can set the graph axes to show results from different events. For example, using the default data, we can set the x-axis to be the first event, the 6.7 KM run, and set the y-axis to be the second event, the snatch complex. Comparing athlete values across these two events allows you to explore an athlete’s strength versus endurance. If you look at the screenshot below, the tooltip shows that Garth Prouse was 1st in the run, but 30th in the snatch.

One interesting thing we can see in this view is how widely distributed the athletes are for various events. Consider the screenshot below where I plotted the overall placement versus the time for the double-under/burpee workout. Everyone is pretty closely clustered, but I’ve marked two distinct outliers. I’m guessing these two individuals must have struggled with double-under technique. Even those these two scores appear to be outliers, if you read my last post, there was little statistically significant difference between these athletes on this particular workout. In contrast, there is a lot of variance in the distribution for this workout when you inspect the women’s data (second screenshot below).

In the Compare athlete rankings tab, you can inspect an athlete’s ranking in each event as well as their overall ranking. For example, in the screenshot below I’ve selected only the top 6 athletes from the default data. We can see that three of these athletes (Erik, Nate, and Dan), for the most part, were pretty consistent across all events. On the other hand, DJ Wickham has an outlier on the run, while Garth and Michael have an outlier on the snatch complex event.

Finally, in the Event rank comparison tab, you can compare an athlete’s ranking in a specific event versus their overall placement. This allows you to visually correlate how closely tied an event’s ranking for an athlete is in comparison to how they did after completing all events. The screenshot below shows all athletes and their rank in the run versus their overall ranking. We see that Cam’s ranking went from 25th in the run to 34th overall, while Jason Fleming went from 10th to 50th and in the other direction, DJ Wickham went form 38th to 6th overall.

For those with technical expertise or those just curious, the way the application works is I send the URL corresponding to the overall results for a sectional or regional competition to the server-side code. Taking whatever URL is provided, I make a server-side request to the URL, get the HTML contents and parse out the values of the overall results table using the PHP Simple HTML DOM Parser. I load this into a simple datastructure (a hashtable of hashtables), which describes the athletes, the events, and all the various results. This information gets encoded as JSON and sent back to the client (front-end).

On the client, I convert the JSON text into a Javascript object/associative array. Then, based on whatever tab is selected, the data is processed into a data series for each athlete. I use Flot to handle the rendering of the graphs. All the interactive behavior and some of the UI is built using jQuery.

The data is split into results for each event and overall placement. For each of these, the results are split into rankings and actual scores. For example, in the default dataset, athlete “Rogers, Dan”, has both an overall rank of first as well as an overall placement score of 37. I do some simple things like recognize times, which for plotting purposes are converted into seconds.

Please post to any questions, suggestions, or general comments. Please let me know if you discover anything interesting :-).