Blog: Combing the Opta Stats

A look inside FC Cincinnati’s team and individual Opta stats on the USL site, with examples that look at the team’s midfield.

The blog section of Orange & Blue Press is new and will feature less formal writing about whatever’s going on with the club at the moment, or whatever strikes the author as interesting to write about. It will be more words, less multi-media, more about opinions and conversation, and less about facts and news. More personal, less objective. Think of it as scratching ideas on the back of the napkin rather than writing an essay you’d turn into a teacher. You know, a blog (like I had to explain).

USL’s relationship with Opta Stats is new this year and the information and features available on their site are impressive. The most commonly used stats are contained in the Match Center, where you will find data in real time for a match that’s in progress (or has concluded). There is also a stats page for each team, which aggregates match data at the team and individual levels (click on Player Stats) over the course of the season. Some events like red cards (topical?) can skew the stats of a particular game, but meaningful patterns often emerge in the aggregated data when you look over a longer time period.

Here are a couple of examples of the kind of information you can glean from this page.

Corben Bone leads FC Cincinnati midfielders in passing accuracy (PA) at 77.7% for players with over 100 passes. Andrew Wiedeman’s PA is actually higher but he’s played midfield, forward, and on the wing, so we’ll exclude him. What’s interesting is that Bone’s passing accuracy does not decline in the opposition’s half. His PA is 78% in his own half and 77% in the opposition’s half. Most other midfielders are about 20% less accurate in the opponents half than their own. Aodhan Quinn’s PA for example is 90% in his own half but 66% in the opponents half. Quinn’s overall passing accuracy is almost the same as Bone’s at 77.1%, but he achieves it in a different way. Quinn typically plays deeper and attempts more long passes. Bone is statistically a more accurate passer in forward positions. He keeps the ball moving and is more likely to look for interplay with nearby attackers.

Another example. Kenney Walker’s contributions both with and without the ball show up in the individual player stats. Walker leads the team in chances created at 14, which is 4 more than any other player. He also has been involved in considerably more tackles than any player on the team. He’s attempted 31 total tackles, and won 27 of them (87%). The next highest number of tackles attempted is 17, a tie between Bahner and Quinn.

It seems odd that the central defenders on the team don’t have more tackles right? Their defensive efforts often show up as duels and interceptions in addition to tackles. Duels are more complicated and we’ll look at those in a future post.

This brings up a good question, what the hell do all of these terms mean? One of the more complete descriptions of the terms used can be found on the Opta Event Definitions Blog. Below are the definitions of some of the terms we’ve used.

Passing Accuracy – This is simply a formula where successful passes are divided by total attempted passes.

Tackles – A tackle is defined as where a player connects with the ball in a ground challenge where he successfully takes the ball away from the man in possession. A Tackle Won is deemed to be where the tackler or one of his team-mates regains possession as a result of the challenge, or that the ball goes out of play and is “safe.” A Tackle Lost is where a tackle is made but the ball goes to an opposition player.

Chances Created Assists plus key passes, where a key pass is the final pass or pass-come-shot leading to the recipient of the ball having an attempt at goal without scoring.

If you’ve read Orange and Blue Press in the past, you’ll know that I like stats and that I cite them frequently. Why?

There’s a lot about soccer and its analysis that is subject to interpretation. For example, whether someone is playing well, fouls, red cards (topical?), what tactics the coach is trying to employ and whether they’re effective. All of this drives good and relevant dialogue, but two people can argue about this subjective stuff and often never get to any meaningful conclusion. I like statistics because they have the ability give you a more concrete understanding of what happened. Further, they can validate or refute what you “think” you saw while you were watching a match.

Most people take in soccer matches as entertainment. And unless you’re analyzing the match from the press box, they’re likely to be subjected to a series of distractions during the match. You might have had four beers before you went in and spend 15 minutes of the match waiting in line to void your bladder (my mom is a nurse and used to make me say “void my bladder” when I was little…messed up right?) You’re subject to the comments and biases of the people around you, and they can affect your perception of the game. Stats allow you to bounce your perception up against data to see if how you perceived things jibes.

Stats aren’t arguable (assuming reliable data collection, which Opta is). They are what happened. That being said, the interpretation of those statistics can be used to support many ends. Some stats can be misleading or, more often, are just not relevant to the outcome of a match. My opinion however is that on balance, they help me to better understand what happened after I’ve watched with my own two eyes.

There’s a lot of good information out there, comment below or on Twitter with anything you find on Opta that piques your interest.

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