Much SQL! So excite!

So after much data scraping and cleaning I’m finally ready to start analyzing.. by which I mean discover how much more cleaning I need to do 😉

Lets have a good, clean race

Lets have a good, clean race

I wanted to add a word on some of my data gathering techniques. I’ve been primarily scraping the data from ProCyclingStats but sometimes I come across data that isnt as scraper friendly. Case in point, 2013 TdF results:

Mmmm span

Mmmm span

Thats a lot of span! I had been using next_element to parse the start list data (you can find the code for that here) but this particular piece seemed outside the 80/20 spectrum. I manually scraped and formatted the data which, while tedious, seemed reasonable to me since it is a limited, immutable set. That being said the html, while span laden, has a defined structure so if I feel inspired to write yet another scraping script I could probably do so.

Some other things I learned are loading sql scripts in psql, which is useful if you want to forgo the whole connect to db thing in your scraping code:

slashi

\i is your friend

I used this method to insert the team roster data.  I also used it to keep track of queries to the data set!!

statssql

Which looks like this for the 2013 Tour results!

Is KOM an indicator of GC ?

Is KOM an indicator of GC ?

Its been about 14 years since I did any SQL joins (not a lot of SQL in hardware design) and I found this oldie but goodie an excellent refresher. Also, who doesn’t love a good Venn diagram?

Hooray for Venn Diagrams!

Hooray for Venn Diagrams!

Rosterizing

Lions and Cyclists and Bears

Lions and Cyclists and Bears

Back from a hiatus (vacation) I wanted to post a quick update on my project

Rosters for Le Tour have been announced and I have been hard at work collecting datas. I updated my GitHub with a scraping script for team roster data from ProCyclingStats. I finally have all the stats of interest in my database from 2013 and 2014.

If you want to play along you can create your own Fantasy Tour de France team. Besides trying to predict the winner I may use the model to help pick my team, assuming its ready before registration closes.

Now that all the team and individual data is populated the next step is denormalizing for analysis. Stay tuned!

Le Tour: King of the Mountain

So now that I have a mountain of data, what shall I do with it?

KOM of Data

KOM of Data

Recalling that I have data sets for individual riders and teams, each individual data set contains the top 100 riders in that category. Riders that are good at mountains are typically not good at sprints, so I have different riders in each data set. How do I create a list of riders to track for the TdF?

Historically, the winner of the TdF has been ranked highly in the General Classification (GC), so that would be a good place to start. As I mentioned, the supporting actors (domestiques in cycling language) play a key part as well. Teams announce their Tour de France rosters shortly before the event (as of this writing, teams have yet to announce their final rosters, though we know Sir Bradley Wiggins will most likely not be participating this year, much to his chagrin.) so we dont know yet who will be riding.

Pulling in the top riders (individual GC) for the top ranked teams (team PCS ranking) should give a reasonable approximation for an initial data set, and when team rosters are announced I can cull those from the herd that wont be making the cut.

Maybe next year Wiggo

Maybe next year Wiggo

I imported the .tsv files created by the scraping scripts into a PostgreSQL database. I was curious about the varchar vs text tradeoffs and found this article useful. I decided to leave the data normalized as it came to me from PCS, so each metric has its own table.

An important note – presently I’m looking at data from this year. If I’m going to build a predictive model I will need to have a training data set where I know the outcome for the dependent variable – the winner. Fortunately I don’t have to fabricate one; PCS has the metrics from last year and we already know who won Le Tour. It would be preferable to have several years of examples, or to create a few more training examples based on the data we have, but for now I’m just planning on using the data from 2013. Since the database will have information spanning multiple years I added a “year” field to all the metrics.

What metric keeps track of contaminated meat consumption?

What metric keeps track of contaminated meat consumption?

Adventures in Data Science: Le Tour

Le Tour de France. A 3 week torture fest featuring svelte men in spandex rolling along the French countryside. Armed with (French) pastries, I enjoy tuning in ridiculously early in the morning to watch this soap opera on wheels.

Recently I stumbled across Pro Cycling Stats. It seemed to be a perfect intersection for my interests in cycling and data science, so I hatched a little project to see if I could predict the winner of Le Tour.

Ridiculous? Of course, this is Le Tour after all

podium girls yellowjersey
tour devil

Day 1. Data Gathering

Winning Grand Tours requires a great team, a strong GC candidate and a lot of unquantifiable luck (i.e. not crashing into a labrador, not being run off the road into a barbed wire fence). What data, if any, would help in predicting the next TdF winner?

Pro Cycling Stats keeps track of a ton of information – General Classification (GC), special points (sprints, mountains, prologues), Tours in various parts of the world, Spring Classics performance, etc. In an effort to keep this project manageable I limited myself to about 10 individual and team stats.

ProCyclingStats GC stats

Definitely going to include GC stats…

Using Beautiful Soup I was able to scrape the stats of interest from the Pro Cycling Stats webpage. I created 2 generic py scripts – one for scraping individual data, another for scraping team data. The scripts take a URL argument so I was able to create a shell script to scrape the stats of interest. I chose to do this so I could easily add new stats pages to the analysis.

As I was looking through the data, I noticed that some stats used “.” to separate thousands and others used “.” to indicate decimals. EEInteresting. As you probably guessed, besides formatting differences, the scales of information are different. Team Distance is in tens of thousands of miles, where as a metric called “Most Efficient” was measured as “Ranking of fraction of points scored on maximum points possible.” What is the maximum number of points possible? Oh good, an Explanation link!

no info on most efficient

An excellent explanation.

It would appear that I have a bonafide Real World ™ data set on my hands!

I was delighted that I had these scripts up and running within an hour, with no prior experience using Python. I wasn’t mired down trying to find the right HTTP API for my target framework to just connect to the damn page. Compared to getting something up and running in .NET this was a breeze

The code for the scraping scripts and the shell script is on my GitHub CyclingStats

Howdy

Well I finally started getting things together around here, so welcome to the distant future, the year 2014. Ill be updating content / adding blog posts about what Im currently working on real soon now.