If you’re a sports fan and assuming you don’t live under a rock in a cave at the bottom of the ocean, you’ve at least heard about data analytics. The concept of collecting and parsing substantial amounts of performance information has been a growing trend in professional sports for the better part of two decades. Many people were first exposed to the practice in the Brad Pitt movie “Moneyball,” a dramatic, Hollywood portrayal of the sabermetrics used by the MLB’s Oakland Athletics to field a competitive team in 2002.
But unlike North American baseball, global football is a bit of a latecomer to the sports analytics party. For much of football’s history, goals counted, but not much else. And let’s face it: There aren’t typically many goals to count, which makes it hard to derive meaningful insights from the data.
That’s all changing as cutting-edge analytics become commonplace in the football universe.
Of course, being passingly familiar with the intersection of data analysis and sports is different than understanding how to this crazy math informs decision-making. That’s why we present you with this handy guide to how data analytics apply to football.
Data Ana, what?
According to Experfy.com, sports analytics is “the processes that identify and acquire the knowledge and insight about potential players’ performances based on the use of a variety of data sources such as game data and individual player performance data.”
Phew! That hurt to type, much less understand.
In layman’s terms, we can think of sports analytics as smartly using past performance data to shape future decisions.
In relation to football, these future decisions derive from coaches, players, club executives and fans alike, albeit for different reasons.
For instance, the coach wants the best information for staging his lineup. Players seek to optimize their training and conditioning regimens. The executive is always on the lookout for undervalued talent. Fans want to make educated decisions when they manage their fantasy sides or wagering on a match.
Collecting and analyzing all this football data is not the dominion of a few pitch-side nerds with clipboards and calculators. It’s a rapidly progressing field led by companies like Prozone, Opta and WhoScored. Still, smaller leagues and clubs find themselves falling behind the analytics curve due to financial constraints. Making sense of big data costs big money. Naturally, it’s the organizations like Man City and Juventus of the world who are in the best position to leverage analytics at the present time.
Graduating to Advanced Statistics
All the behind-the-scenes computer blips and number crunching involved in sports analytics would be useless if there was no human-readable, actionable output. That’s where advanced stats come into play. Traditional football metrics like Goals For (GF), Goals Defensed (GD) and Shots on Target (SOG) are fine for grasping what happened.
Advanced stats are like a crystal ball. They seek to tell what will happen.
Expected goals or xG is the go-to metric for predicting future performance. xG modeling is a rather complex concept, but the gist is to calculate the probability a shot results in a goal, considering where and how the player took the shot. For instance, a straight-on shot from inside the six-yard box is more likely to score than an angled header.
If a specific shot, given the position of the ball, distance to the goal, contact method and presence of assist on the play, among other factors, goes in 90 percent of the time, it’s worth .90 xG. A more outlandish shot that lands in the back of the net only once every 70 times has a .014 xG.
A side’s expected goals taken and conceded stats may weigh against those of an opponent to determine which side enters the match with an upper hand in scoring.
Is there any quantified intel to glean from possessions that don’t result in a shot? Non-shot xG works to provide this information by dealing in the meta. Non-shot xG models identify sides that may have xG stats inconsistent with other supporting measures.
Specifically, the volumes and types of passes sides take on the attack predict how often passes should lead to shots versus how often passes result in shots.
Such insight helps sides tweak their offenses to maximize the scoring possibilities.
We’ve seen how the outcome of possession uses xG and non-shot xG, but what about those setup plays that lead to the outcome? Enter xGChain, a regressive method of measuring the value of plays involved in creating a shot.
For instance, one xGChain use case is to gauge the contribution of adept midfielders who advance the ball and establish quality shot opportunities. It credits the offense’s supporting cast by working backward, along the chain of possession, which is especially valuable for uncovering dominating ball controllers and passers.
Given its strength at identifying playmaking individuals, xGChain analysis is beneficial for setting lineups and player recruiting.
Why Analytics Matter to Fans
You can sum up the importance of analytics for sports watchers up in three words: enhanced fan experience.
We already mentioned how fans find the practice handy for fantasy football or betting, but sourcing undervalued playmakers using xGChain analysis is an excellent way to fill out your fantasy squad. Maybe you should shuffle your lineup to maximize your xG. Check out the site fplanlytics.com for some incredible data-driven insights about the Fantasy Premier League.
Similarly, football match odds and predictions derive from sophisticated data analysis. So, if you’re a punter, then understanding the odds gives you a thorough picture of how your favorite side will likely perform against an opponent. There are many reliable online sources that are a one-stop for the latest odds, deposit bonuses and bookmaker comparisons like Oddschecker, for example.
By now, you know what sports analytics is, and its increasing role in shaping elite football competition. You’re also familiar with advanced stats, how analysis aids football decision-makers, and how you can put it to work as a fan. Toss on your analyst cap and jump on board. There’s plenty of room on the football data bandwagon.