Nice post on SI.com mentioning StatDNA from yesterday.   http://sportsillustrated.cnn.com/soccer/news/20120305/monday-musings/

A nice case study written up about us in SportsPro's March edition, attached in PDF format.   StatDNA SportsPro.pdf (666.40 kb)

  I read a tweet a few weeks ago from a Liverpool fan about how opposing goalkeepers always seem to play their best match against Liverpool, and citing the fact that five opposing goalkeepers had been selected Man of the Match this season.  StatDNA has a statistic that measures how many goals a goalkeeper was expected to let in, given the quality of the finishes he’s faced, so I decided to use this statistic to see how valid the claim was.   In mathematical terms we’re measuring the expected value (EV) of the shots on target that the goalkeeper faced (excluding pena... [More]

StatDNA is very excited to announce that onfooty.com's Sarah Rudd has joined us as Vice President, Analytics and Software Development.  We couldn't think of a better addition to our team, Sarah's obviously well known for her soccer analytics acumen to all of her blog followers (not to mention that she won StatDNA's analytics competition), and she also brings tremendous skills in software development from working in search engine customization at Microsoft.  You can look for blog posts from Sarah on StatDNA.com in the months to come, as well as continued posts on her own blog. ... [More]

We would like to congratulate Sarah Rudd, the winner of the inaugural StatDNA soccer analytics research competition. Many of you will already know Sarah from her blog On Football and also as the co-creator of the meta-blog Soccer Analysts.  Her paper "Modeling Possessions in Soccer Using Markov Chains" was chosen as the winner by our judging panel of Dr. Ben Alamar of Menlo College and JQAS and Dr. Andrew Thomas of Carnegie Mellon University. Sarah's paper proposes a framework for understanding offensive contributions of player using Markov models and even goes on to rank top performer... [More]

The statistic of pass completion % is one that I have discussed in the past as having limited relevance - see the post here for example. The key issue is that pass completion % does not tell you anything about who won the game or who scored the most goals, because its very situation specific (at certain times of the game a team may be ceding possession and allowing a team to complete a large number of non-threatening passes, for example).  One key factor that pass completion % does not take into account at all is pass difficulty.  Whether a team is banging the ball around in the defe... [More]

  The next step in the goal scoring framework is finishing quality. Finishing quality is how well a shooter does in taking the opportunity he is given: others have measured finishing quality by shots on targets/shots, which is a good starting point. Our measure of Finishing quality uses a multi-variate logistic regression to test a variety of variables they may impact the probability of a shot struck on target going into the net. The measure is independent of goalie performance: shooters receive credit for striking a shot well – with high velocity, a long distance from the goalkeep... [More]

One key metric in our goal scoring framework is shot quality.  In this post we’re going to outline the key components of our measure of shot quality, look at the distribution of shot quality and see how much teams vary on shot quality.Intuitively, we all know what makes a good shot.  One modicum of proof is the rising voice of the announcer as a team works its way into a good scoring position. During working hours, we always have matches on television in the background at StatDNA, and I often find myself turning my head to watch just in time to see the last pass before a goal o... [More]