How Analytics Is Helping Sports Teams Reach New Levels
Analytics has been making its mark in a number of sports for many years now, and it’s only a matter of time before every team will have to embrace analytics in order to stay relevant.
What’s the MVP in all of this? Data.
Sports teams are finally realising how valuable the data points they have on every single player and game play are to the decisions they make. Here are a few examples on how teams are using analytics to stand out.
Baltimore Ravens – American Football
The Ravens finished the NFL regular season as the No.1 seed in the AFC North league with a 14 wins to 2 losses record, the best record in the 2019/20 season, and are serious contenders for making it to Superbowl 2020.
They had a decent record the past couple of seasons too, especially with the introduction of Lamar Jackson as their Quarterback in the 2018/19 season, but nowhere near as good as this year’s record.
Their secret? Going for it on 4th down more than any other team, all based on analytics.
If you’re unfamiliar with how American Football is played, the offense has 4 downs i.e. ‘attempts’ to move the football 10 yards down the field towards the end zone. If they don’t make the 10 yards on the 4th down, the opposition offense gets the ball at that point in the field and moves the ball the other way towards the other end zone.
For a more detailed explanation of American Football, check out this link.
With the above scenario in mind, most teams will punt the ball on their 4th attempt, i.e. kick the ball down the field towards the end zone so that the opposition offense starts with the ball at the other side of the field.
As time starts to run out and if the scores are tight, that’s traditionally when you’ll see teams going for it on their 4th attempt – it’s seen as a high-risk play as your offense could lose the ball in a dangerous area.
However, by using analytics to assess the probabilities of game success based on a number of factors, teams are seeing that it actually makes more sense, and is far more valuable, to just go for it on 4th down. As an example, if a team is on 4th and 3 (and therefore need 3 more yards to complete the 10 yards they need), and the team is up by 6 points with 4 minutes to go, it makes far more sense to go for it on 4th down.
If they go on to score after making it on 4th down, they’ve won the game. If they don’t go for it and give it back to the opposition instead, even if it’s further down the field, the opposition still have a huge chance to win the game with a touchdown (equalling 7 points) with the time remaining.
The Ravens have therefore mastered these stats and are huge players in NFL analytics.
Liverpool FC – Football
For those that are football fans, it’s no secret that Liverpool are currently playing some of the best football seen in years (they’re currently top of the Premier League, with a lead of 13 points and not lost a Premier League game this season). They incorporate a lot of data analysis into both the corporate and tactical decisions they make, but it’s hard to measure how much of that has contributed to their success.
An example of how Liverpool are using data in the corporate sense is through Ian Graham, Head of Research at Liverpool FC, who has developed a database of more than 100,000 players and has been tracking their progress. This has been used at Liverpool to evaluate which players should be acquired and how they should be used at the club.
In terms of tactical decisions, the event that stands out the most for a clever use of data is their 2nd Champions League Semi-Final game against Barcelona in 2019. This game was huge, where Liverpool came back from 0–3 down in leg 1 to win 4–0 in leg 2, therefore winning 4–3 on aggregate.
The winning goal was an actual dream for Liverpool fans. Liverpool were 3–0 up and needed one more goal for the aggregate win, and they had a corner. The fact it was a corner is the biggest part of this.
The analytics team had told the coaches how they had seen many occasions in which Barcelona were slower than other teams to set up for set pieces, in particular corner kicks. With this information, the coaches told the ball boys to get a ball to the Liverpool player as quickly as possible on a corner.
This played a huge advantage in the corner mentioned. The ballboy threw a spare ball to Trent Alexander-Arnold (taking the corner) as the previous ball was being kicked off the side. Trent, with incredible intuition, noticed that Liverpool were ready but Barcelona were still setting up. He quickly crossed the ball into Divock Origi (who seemed to have no idea what was going on), and he edged it into the goal to win the game for Liverpool.
Golden State Warriors – Basketball
The Golden State Warriors’ (GSW) have dominated the last few NBA seasons, except for this 2019/20 season where GSW are bottom of the Western conference… (but this is mainly due to almost all of their top players out of play due to injury).
However, the way GSW stayed in the top few teams and made it to the NBA Finals for so many years can be at least slightly attributed to their use of data and analytics.
The NBA works with a company called STATS which has installed 6 cameras per arena to track player movements at 25 frames per second to pull out analytical data for teams to analyse.
The data on each player can be used by the coaching staff to adjust their offensive and defensive strategies, as well as their opponent and individual player health stats are changing throughout the game.
Firstly, thanks to the health data stats, GSW have been more willing to rest their key players such as Steph Curry in order to reduce the likelihood of injury and ensure they can play for longer.
Secondly, the camera technology has also helped to track where a player shoots their best shots from. Regarding Steph Curry, it’s clear he prefers and performs better taking his shots from the 3 point line and in the paint.
Thanks to these types of statistics, GSW are known to have consistently scored 3 pointers, but that doesn’t mean any team can suddenly make the Finals by shooting more 3 pointers. These stats are only useful if you have the players that have shown they can consistently make these shots, just as Steph Curry has shown.
The future of sports and technology
Analytics in sports is by far not at its peak yet, and I’m sure there’s much more to come with technological advances.
VC funding into the sports tech sector reached a total of $2.5 billion in 2018, and is estimated to reach a staggering $30 billion by 2024. With the 2020 Tokyo Olympics looming, we’re seeing massive amounts of investment pouring into the sports technology industry.
As a general example in a sport, The NFL has developed a huge partnership with AWS to use their Machine Learning and AI services to develop ‘Next Gen Stats’. This is a great new tool which is available to the teams themselves as well as fans, with a huge amount of analysis on catch probabilities, passing and rushing yards, and much more.
This is just one case of how a sports league has embraced advances in technology and data. Here’s hoping we see many more alike partnerships in the future!
