Football arrived fashionably late to the data and analytics party.
While baseball, basketball, and American football were fighting for room at the hors d’oeuvres table, the beautiful game was at home debating about a night in.
These days, she’s dressed to the nines and stepping into the ball. It just took a while.
Football is indeed a sport steeped in tradition. Experience and gut instincts have long served as the primary inputs when making decisions.
To better understand how advanced stats fit in with football, let’s examine how they came to be, why they are useful, and how the sport is integrating new measures of performance data.
The Beautiful Game: Unquantified
Football has suffered from a lack of tracked stats throughout most of its history. While goals were always counted, little else was.
A few forward thinkers began manually counting events like passes and shots throughout the years, however, these measures were not always uniform across sides or even match to match. Football stats remained largely unchanged from the beautiful game’s early days up to the new millennium.
Why Advanced Stats are Important
Football leagues have an obvious interest in advanced stats. The insights gleaned from computer-assisted analysis is invaluable for setting lineups, sizing opponents, and scouting players. While this is a sea change from the intuitive ways of conducting strategy, many clubs are now relying on big data exclusively or to supplement gut decisions.
An interesting aspect of advanced stats is seeing how they are used outside of team and league decision-makers.
For example, NFL stats are one of the most fascinating parts of the game in pro-American football. A common use among fans occurs with NFL betting. Expert punters may consult a database of advanced stats before placing a wager on point spreads, total points, and halftime scores.
Meanwhile, the fewer advanced stats captured in top flights like La Liga and the Premier League puts a limit on usability.
Football Joins the Advanced Stats Revolution
The story of expected goals, or xG, is a convenient shorthand for telling the story of advanced stats in football.
The theory of xG extends back to a 1993 scholarly paper by Vic Hilditch. However, widespread adoption of the metric is a 21st-century phenomenon. xG measures the likelihood a shot will result in a goal considering how the shot was made and its position on the pitch.
For example, a shot taken directly in front of the goal from a few yards away carries a greater scoring probability than a header from the side. A particular shot may record a .85 xG if it scores 85 percent of the time.
Sports data firms such as Opta are largely responsible for pushing advanced stats forward in football. By collecting xG and its derivatives for years across top leagues, club officials, the football press, and fans have reliable sources for numbers-backed analysis.
The Future of Football’s Advanced Stats
Progress is happening as more advanced stats become mainstream in football. We should expect a future where the Beautiful Game packs just as many sophisticated metrics as other sports.
Two stats, in particular, packing and key pass, are leading the trend.
Packing was developed in 2015 as a way to assign value to positive ball movement. It’s the brainchild of former Bundesliga defensive midfielders Stefan Reinartz and Jens Hegeler.
Packing awards a point for any manoeuvre that bypasses an opposing player. For instance, a player who dribbles beyond an opponent and then passes to a teammate past two other opponents is awarded three points. The point system makes it easy to identify players who effectively move the ball towards the goal.
Key pass is another stat that credits set-up specialists. A key pass occurs when the final pass of advancement sets up the pass receiver with a reasonable attempt at scoring.
A key pass is not awarded when the pass receiver scores. Instead, the stat is the “opposite of an assist” and recognizes a productive pass that fails to result in a goal.